Invasion percolation & basin modelling for carbon capture storage site screening and characterization

Abstract

A fluid migration method called invasion percolation (IP) is adopted for the carbon capture and storage (CCS) screening phase to predict the extent and location of CO2 plumes in the subsurface during and after injection. Results are compared to traditional reservoir models revealing similar outputs given the overall geological uncertainty. It can be concluded that IP as part of basin modeling is a fit for purpose method, which can assess multiple opportunities rapidly during the early intake screening phase of CCS sites. In addition to the plume evaluation, basin models can provide useful basin-scale or injection site specific pressure and temperature predictions as well as CO2 density estimates for static volume calculations before detailed reservoir and stratigraphic models are available. Hence, IP can be deployed successfully during the screening phase to assess risk and rank opportunities relative to each other and to build a CCS injection site portfolio.

Description

INVASION PERCOLATION & BASIN MODELLING FOR CARBON CAPTURE STORAGE SITE SCREENING AND CHARACTERIZATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States Provisional Patent Application Serial Number 63 / 641,704 titled “INVASION PERCOLATION & BASIN MODELLING FOR CARBON CAPTURE STORAGE SITE SCREENING AND CHARACTERIZATION” and filed on May 2, 2024, the entire contents of which are hereby incorporated herein by reference.TECHNICAL FIELD

[0002] The present application relates to field operations, and in particular, methods and systems for carbon capture storage site screening and characterization.BACKGROUND

[0003] Carbon capture and storage (CCS), as described herein, is the process of capturing carbon dioxide (CO2), either to prevent it from entering the atmosphere or to directly remove it from the atmosphere, and permanently store that CO2 underground. Different technologies can help to reduce greenhouse gas emissions. One such technology is to capture the CO2 at large industrial point sources or to strip the CO2 out of the air and transport and store the CO2 safely and permanently within deep subterranean saline aquifers at carefully selected and suitable injection sites. CCS is becoming more important to mitigate greenhouse gas emissions and to meet the 2015 Paris Agreement goals.

[0004] To date, the industry has treated CCS opportunities more like individual first- mover projects. However, individual CCS opportunities may be trending toward being managed as part of a portfolio, in which prospective storage resources and their geological and economic risks and uncertainties are assessed for a number of individual injection sites. Building such a portfolio is part of the exploration phase, or in CCS terms is part of the intake screening phase. Typically, high graded opportunities from the portfolio are passed to the next phase, in which they are appraised and characterized in much greater detail to validate the containment model and to prepare permit applications for injection, which are often supported by stratigraphic test wells. The data available and the goals during the permit application phaseare very different from the intake screening phase, and hence different technologies and workflows may be applied.SUMMARY

[0005] In general, in one aspect, the disclosure relates to a method for screening and characterizing CCS sites. The method may include combining, by a controller, a plurality of maps, where at least one of the plurality of maps is generated using seismic data, and where the plurality of maps comprises a location of a reservoir and a top seal. The method may also include translating, by the controller, the seismic data to uncalibrated rock properties. The method may further include performing, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model. The method may also include measuring, by the controller, uncalibrated column heights of subaccumulations within the uncalibrated plume analysis. The method may further include calibrating, by the controller, the uncalibrated column heights using a scaling factor to generate calibrated column heights, where the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration. The method may also include generating, by the controller, a calibrated plume prediction based on the calibrated column heights.

[0006] In general, in another aspect, the disclosure relates to a non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor, enables the computer processor to: combine, by a controller, a plurality of maps, where at least one of the plurality of maps is generated using seismic data, and where the plurality of maps comprises a location of a reservoir and a top seal; translate, by the controller, the seismic data to uncalibrated rock properties; perform, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model; measure, by the controller, uncalibrated column heights of subaccumulations within the uncalibrated plume analysis; calibrate, by the controller, the uncalibrated column heights using a scaling factor to generate calibrated column heights, wherein the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration; and generate, by the controller, a calibrated plume prediction based on the calibrated column heights.

[0007] In general, in yet another aspect, the disclosure relates to a system that includes a plurality of sensor devices measuring a plurality of parameters associated with a subterranean formation being screened and characterized as a site for carbon capture and storage (CCS), where one of the plurality of sensor devices generates seismic data. The system may also include a controller communicably coupled to the plurality of sensor devices, where the controller is configured to: combine, by a controller, a plurality of maps, where at least one of the plurality of maps is generated using the seismic data, and where the plurality of maps comprises location of a reservoir and a a top seal; translate, by the controller, the seismic data to uncalibrated rock properties; perform, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model; measure, by the controller, uncalibrated column heights of subaccumulations within the uncalibrated plume analysis; calibrate, by the controller, the uncalibrated plume using a scaling factor to generate calibrated column heights, where the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration; and generate, by the controller, a calibrated plume prediction based on the calibrated column heights.

[0008] These and other aspects, objects, features, and embodiments will be apparent from the following description and the appended claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings illustrate only example embodiments and are therefore not to be considered limiting in scope, as the example embodiments may admit to other equally effective embodiments. The elements and features shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the example embodiments. Additionally, certain dimensions or positions may be exaggerated to help visually convey such principles. In the drawings, reference numerals designate like or corresponding, but not necessarily identical, elements.

[0010] FIG. 1 shows a block diagram of an overall system that includes an example system for CCS site screening and characterization according to certain example embodiments.

[0011] FIG. 2 shows a block diagram of a controller of the example system for CCS site screening and characterization of FIG. 1 according to certain example embodiments.

[0012] FIG. 3 shows a block diagram of a computing device according to certain example embodiments.

[0013] FIG. 4 shows a flowchart of a method for screening and characterizing CCS sites according to certain example embodiments.

[0014] FIG. 5 shows a carbon dioxide phase diagram according to certain example embodiments.

[0015] FIG. 6 shows temperature and pressure maps derived from a main target depth map according to certain example embodiments.

[0016] FIG. 7 shows a carbon dioxide density map derived from the temperature and pressure maps of FIG. 6 and the graph of FIG. 7 according to certain example embodiments.

[0017] FIG. 8 shows a graph of a cross section of carbon dioxide plume in a subterranean formation using Darcy and Invasion Percolation simulations according to certain example embodiments.

[0018] FIG. 9 shows a graph of buoyancy pressure versus depth of various fluids in a subterranean formation according to certain example embodiments.

[0019] FIG. 10 shows a graph of counter force of various fluids in a subterranean formation according to certain example embodiments.

[0020] FIG. 11 shows a graph of column height of various fluids in a subterranean formation according to certain example embodiments.

[0021] FIG. 12 shows a graph using capillary numbers and viscosity ratios to characterize carbon dioxide plume regimes according to certain example embodiments.

[0022] FIG. 13 shows a graph comparing carbon dioxide plumes based on various simulations according to certain example embodiments.

[0023] FIG. 14 shows graphs of carbon height probability and spatial distribution of Invasion Percolation accumulations according to certain example embodiments.

[0024] FIG. 15 shows a graph of probabilities of carbon dioxide plumes according to certain example embodiments.

[0025] FIG. 16 shows a graph of a 7 well development with a 100 metric ton carbon injection according to certain example embodiments.

[0026] FIGS. 17A and 17B show a graph of column height distribution and a graph ofcarbon dioxide plume outlines, respectively, for a scenario with a set of porosity and capillary scalars according to certain example embodime.

[0027] FIGS. 18A and 18B show a graph of column height distribution and a graph of carbon dioxide plume outlines, respectively, for another scenario with a set of porosity and capillary scalars according to certain example embodiments.

[0028] FIGS. 19A and 19B a graph of column height distribution and a graph of carbon dioxide plume outlines, respectively, for yet another scenario with a set of porosity and capillary scalars according to certain example embodiments.

[0029] FIGS. 20A and 20B show a graph of column height distribution and a graph of carbon dioxide plume outlines, respectively, for still another scenario with a set of porosity and capillary scalars according to certain example embodiments.

[0030] FIG. 21 shows a flow diagram of an embodiment of the method of FIG. 4 according to certain example embodiments.

[0031] FIGS. 22A through 24 show graphical examples of calibration of uncalibrated column heights according to certain example embodiments.DETAILED DESCRIPTION

[0032] The example embodiments discussed herein are directed to systems, methods, and devices for invasion percolation and basin modelling for CCS site screening and characterization for the purpose of removing biogenic carbon from the terrestrial carbon cycle, thereby reducing the rate of climate change. Wellbores that are identified using example embodiments and into which carbon may be injected may be land-based (out of water) or subsea. Example embodiments may be used in areas that are required to be rated for marine, corrosive, and / or hazardous environments. A wellbore that is identified using example embodiments and into which carbon is injected may be a newly drilled wellbore or an abandoned wellbore (e.g., formerly used for oil or gas production). The part of a subterranean formation that is identified using example embodiments and into which carbon is injected may be a salt cavern, one or more other geological formations, or any combination thereof.

[0033] As defined herein, carbon that is injected into a subterranean based on CCS site screening and characterization using example embodiments discussed herein may take one or more of any of a number of forms. Carbon may be or include a liquid, a solid, and / or a gas ata point in time. Carbon that is injected may maintain a substantially constant chemical composition and / or state over time. Alternatively, carbon that is injected may have a chemical composition and / or state that substantially changes over time. Carbon can be or include any compound (e.g., carbon dioxide, methane, ethane, nitrous oxide) that includes the element carbon and / or has a negative effect on changing the global climate. Also, as defined herein, the term “real time” may mean instantaneous and / or with some slight delay (e.g., a few seconds, a minute) that may be accounted for factors that may include, but are not limited to, processing time, communication delays, distance, and translations. Also as defined herein, any probability for column heights described herein is expressed as “pX”, where p means the probability and X represents the value of probability. In such cases, the X value is higher for larger column heights. This means, for example, that a p90 value for a column height means that the column height at that point is greater than approximately 90% of the column heights within that distribution.

[0034] If a component of a figure is described but not expressly shown or labeled in that figure, the label used for a corresponding component in another figure can be inferred to that component. Conversely, if a component in a figure is labeled but not described, the description for such component can be substantially the same as the description for the corresponding component in another figure. The numbering scheme for the various components in the figures herein is such that each component is a three-digit number or a four-digit number, and corresponding components in other figures have the identical last two digits. For any figure shown and described herein, one or more of the components may be omitted, added, repeated, and / or substituted. Accordingly, embodiments shown in a particular figure should not be considered limited to the specific arrangements of components shown in such figure.

[0035] Further, a statement that a particular embodiment (e.g., as shown in a figure herein) does not have a particular feature or component does not mean, unless expressly stated, that such embodiment is not capable of having such feature or component. For example, for purposes of present or future claims herein, a feature or component that is described as not being included in an example embodiment shown in one or more particular drawings is capable of being included in one or more claims that correspond to such one or more particular drawings herein.

[0036] Example embodiments of systems and methods used for invasion percolation and basin modelling for CCS site screening and characterization are described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of systems and methods used for invasion percolation and basin modelling for CCS site screening and characterization are shown. Example embodiments of systems and methods used for invasion percolation and basin modelling for CCS site screening and characterization may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein. Rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of systems and methods used for invasion percolation and basin modelling for CCS site screening and characterization to those of ordinary skill in the art. Like, but not necessarily the same, elements (also sometimes called components) in the various figures are denoted by like reference numerals for consistency.

[0037] Terms such as “first”, “second”, “primary,” “secondary,” “above”, “below”, “inner”, “outer”, “distal”, “proximal”, “end”, “top”, “bottom”, “upper”, “lower”, “side”, “left”, “right”, “front”, “rear”, and “within”, when present, are used merely to distinguish one component (or part of a component or state of a component) from another. This list of terms is not exclusive Such terms are not meant to denote a preference or a particular orientation, and they are not meant to limit embodiments of systems and methods used for invasion percolation and basin modelling for CCS site screening and characterization. In the following detailed description of the example embodiments, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0038] FIG. 1 shows a block diagram of an overall system 100 that includes an example system 190 for CCS site screening and characterization according to certain example embodiments. The overall system 100 of FIG. 1 includes, in addition to the example system 190 for CCS site screening and characterization, a field system 199. The field system 199 includes three wellbores 111 (wellbore 111-1, wellbore 111-2, and wellbore 111-3) within asubterranean formation 110 that each have a casing string 125 (casing string 125-1 in wellbore 111-1, casing string 125-2 in wellbore 111-2, and casing string 125-3 in wellbore 111-3) inserted therein and cemented to the subterranean formation 110. The field system 199 in this case may also include one or more optional carbon sources 128, an optional carbon preparation apparatus 193, and optional carbon injection equipment 194 (e.g., pumps, compressors, pipes, valves, controllers (e.g., controller 104), sensor devices (sensor device 160)).

[0039] The example system 190 for CCS site screening and characterization includes one or more controllers 104, one or more power sources 189, one or more sensor devices 160 (e.g., sensor device 160-1, sensor device 160-N), one or more users 159 (including one or more optional user systems 155), and a network manager 180. The components shown in FIG. 1 are not exhaustive, and in some embodiments, one or more of the components shown in FIG. 1 may not be included in the overall system 100, for example, in order to simplify the drawing. Similarly, one or more components (e.g., a motor, a valve) not shown in FIG. 1 may be included in the overall system 100. For example, piping and electrical cables, as well as associated equipment (e g., valves, circuit breakers, conduit), are not expressly shown in FIG. 1 but are part of the overall system 100. Any component of the overall system 100 may be discrete or combined with one or more other components of the overall system 100. For example, a controller 104 may be combined with a sensor device 160.

[0040] The field system 199 of the overall system 100 of FIG. 1 may include one or more wellbores 111. As discussed above, there are three wellbores 111 in this case. Some or all of the wellbores 111 may be from a common pad. Over time, a wellbore 111 may be used for different purposes. For example, a wellbore 111 may be used as a production well at one time (e.g., prior to the time shown in FIG. 1), and at another time (e.g., as captured in FIG. 1), the wellbore 111 may be used as an injection well. In some cases, rather than being an abandoned or depleted production well, a wellbore 111 may be drilled and developed for the sole purpose of injection of carbon 147 into the subterranean formation 110.

[0041] The unprocessed carbon 127 is obtained from each of the carbon sources 128 before being processed by the carbon preparation apparatus 193. The unprocessed carbon 127 may originate from a single carbon source 128 or multiple carbon sources 128 at a point in time. The one or more types of unprocessed carbon 127 and / or the one or more carbon sources 128that supply the unprocessed carbon 127 at a point in time may change over time. A carbon source 128 may be or include an original source (e.g., the atmosphere, an industrial facility, a fossil fueled power plant) of the unprocessed carbon 127 and / or a downstream source (e.g., an aggregator, a distributor) of the unprocessed carbon 127. A carbon source 128 may provide one or more types (e.g., state, chemical composition) of the unprocessed carbon 127.

[0042] A carbon source 128 may be an original source of carbon or some other source of carbon that has been previously processed in some way. When there are multiple carbon sources 128, one carbon source 128 may be owned and / or operated by the same entity or a different entity that owns and / or operates one or more of the other carbon sources 128. Examples of a carbon source 128 may include, but are not limited to, a municipal source, a landfill or waste facility (e.g., specialized, general), an industrial facility, a fossil fueled power plant, the general atmosphere, and a collection site.

[0043] The unprocessed carbon 127 (or one or more portions thereof) may be moved from a carbon source 128 to the optional carbon preparation apparatus 193 using a conveyance network 188. The conveyance network 188 can include any equipment and / or modes of transport so that some or all of the unprocessed carbon 127 is delivered to the optional carbon preparation apparatus 193. The conveyance network 188 can include any equipment that can transport the unprocessed carbon 127, regardless of the state (e.g., solid, liquid, gas, a combination thereof) of the unprocessed carbon 127. Examples of such equipment may include, but are not limited to, pipes, tubes, valves, storage tanks, pumps, motors, controllers (e.g., a controller 104), sensor devices (e.g., a sensor device 160), conveyor belts, trucks, rail systems, vibrating devices, mixers, agitators, flat boats, ultraviolet devices, shipping containers, refer containers, compressors, cranes, heaters, coolers, and dehumidifiers.

[0044] The optional carbon preparation apparatus 193 is configured to process some or all of the unprocessed carbon 127 in some way before the unprocessed carbon 127 is delivered to the carbon injection equipment 194 as processed carbon 147 (or, more simply, carbon 147). In this way, the carbon preparation apparatus 193 is configured to process the unprocessed carbon 127 in such a way that the unprocessed carbon 127 may be transformed into carbon 147. Such processing of the unprocessed carbon 127 may be or include, but is not limited to, drying, chemically treating, introducing additives, introducing another fluid, separating, pressurizing,depressurizing, humidifying, dehumidifying, filtering, removing unwanted components (e.g., hydrogen, metals), mixing, and agitating. In some cases, the unprocessed carbon 127 may be pre-treated before reaching the carbon preparation apparatus 193.

[0045] In order to perform its one or more functions, the carbon preparation apparatus 193 may include any suitable equipment. Such equipment may include, but is not limited to, a heater, a blower, a mixer, an agitator, a compressor, a heat exchanger, a sifter, a cooler, and a filter. When the unprocessed carbon 127 is fully prepared through the use of the optional carbon preparation apparatus 193, the carbon 147 is delivered to the carbon injection equipment 194 via the conveyance network 188. Each component of the conveyance network 188 may have an appropriate size (e.g., inner diameter, outer diameter, height, width) and be made of an appropriate material (e g., steel, PVC) to safely and efficiently handle the pressure, temperature, mass, and other characteristics of the unprocessed carbon 127 and the carbon 147 transported thereby.

[0046] The carbon preparation apparatus 193 may have a single stage or multiple stages (e.g., drying, chemically treating, pressurizing). In addition, or in the alternative, the carbon preparation apparatus 193 may be located at a single facility and / or location, or distributed over multiple facilities and / or locations. The carbon preparation apparatus 193 may be owned and / or operated by an entity that is the same as, or different than, the entity that controls the carbon injection equipment 194 and / or the example system 190 for CCS site screening and characterization. When there are multiple stages and / or locations of the carbon preparation apparatus 193, one stage or location may be owned and / or operated by the same entity or a different entity that owns and / or operates one or more of the other stages or locations.

[0047] Some or all of the operation of the carbon preparation apparatus 193 may be controlled by a controller 104 (discussed below). In such a case, the controller 104 may base some or all of its control on measurements, captured by one or more sensor devices 160 (discussed below), of one or more parameters (e.g., mass, chemical composition, specific gravity, temperature, moisture content, volume) associated with the unprocessed carbon 127. A controller 104 of the carbon preparation apparatus 193 may be in communication with or may be part of a controller 104 of the carbon injection equipment 194, discussed below. Some or all of the carbon preparation apparatus 193 may operate on a continuous basis or in discreteperiods or intervals of time.

[0048] In some cases, a controller 104 (e.g., external to the carbon preparation apparatus 193, internal to the carbon preparation apparatus 193) is designed to control one or more components of the carbon preparation apparatus 193 in order to achieve carbon 147 having certain characteristics (e.g., a certain viscosity or range of viscosities, a certain specific gravity or range of specific gravities, a certain density or range of densities, a certain salt content or range of salt contents, a certain chemical composition). In addition, or in the alternative, one or more of the components of the carbon preparation apparatus 193 may be controlled manually (e.g., by a user 159, including an associated user system 155). In some cases, some or all of the carbon preparation apparatus 193 may be operated based on measurements, made by one or more sensor devices 160 (discussed below).

[0049] Examples of components of the carbon preparation apparatus 193 used to prepare the unprocessed carbon 127 may include, but are not limited to, a motor, a pump, a filter, a centrifuge, a compressor, a condenser, a vibrating device, a funnel, a strainer, a separator, an agitator, a paddle, a circulating system, an aerator, a baffle, a column, a separator, a mixer (e.g., a centrifuge mixer, a desander, a tumbler mixer, a homogenizer, a static mixer, a drum mixer, a fluidization mixer, agitator mixers, paddle mixers, an emulsifier, a pail mixer, a convective mixer, a batch mixer, and a ribbon mixer), piping, a valve, a fan, a blower, a heater, a heat exchanger, and a cooler.

[0050] When the carbon preparation apparatus 193 includes multiple components or pieces of equipment used to process the unprocessed carbon 127, these components or pieces of equipment may operate in series and / or in parallel with each other. A controller 104 may also use measurements made by one or more sensor devices 160 to drive control decisions made by the controller 104 with respect to the operation of the components of the carbon preparation apparatus 193. Such decisions may be based, at least in part, whether in real time or with some time delay, on one or more outputs generated by the example system 190 for CCS site screening and characterization.

[0051] Once the carbon 147 leaves the carbon preparation apparatus 193, the carbon 147 is delivered to the carbon injection equipment 194 via part of the conveyance network 188. In some cases, before the carbon 147 is delivered to the carbon injection equipment 194, the carbon147 is evaluated to ensure that the carbon 147 is suitable for subterranean injection. In such cases, a controller 104 of the system 190 for CCS site screening and characterization may be configured to compare the measurements, as taken by one or more of the sensor devices 160, of one or more parameters associated with the carbon 147 to ensure that each of the values of the measurements fall within a range of acceptable values or threshold values. If a controller 104 determines that the value of a measurement of a parameter associated with the carbon 147 falls outside a range of acceptable values, the controller 104 may take appropriate action (e.g., change the operation of one or more of the components of the carbon preparation apparatus 193, modify (e.g., add, remove, adjust an amount of) an additive) to correct the issue.

[0052] The carbon injection equipment 194 of the field system 199 of FIG. 1 is configured to inject carbon 147 into one or more of the wellbores 111 for storage in the subterranean formation 110 adjacent to one or more parts of a wellbore 111. Examples of carbon injection equipment 194 may include, but are not limited to, a pump, a motor, a compressor, a valve, a sensor device (e.g., a sensor device 160), a controller (e.g., a controller 104), piping, a regulator, and a power source (e.g., power source 189).

[0053] In certain example embodiments, injection of carbon 147 into the subterranean formation 110 using the carbon injection equipment 194 is a carbon sequestration process that may rely on a waste disposal method from the oil and gas industry. For example, an injection technique used by oil and gas well operators to dispose of drilling waste may apply to the injection of carbon 147 into the subterranean formation 110. The carbon 147 can be deposited in underground reservoirs (e.g., salt caverns) and / or stored in newly created fractures in suitable geologic layers of the subterranean formation 110. Such reservoirs and / or fractures may be identified by the example system 190 for CCS site screening and characterization according to certain example embodiments.

[0054] The carbon injection equipment 194 may have a single stage or multiple stages (e.g., heating, pressurizing). In addition, or in the alternative, the carbon injection equipment 194 may be located at a single facility and / or location, or distributed over multiple facilities and / or locations. The carbon injection equipment 194 may be owned and / or operated by an entity that is the same as, or different than, the entity that controls the carbon preparation apparatus 193 and / or the example system 190 for CCS site screening and characterization. When there aremultiple stages and / or locations of the carbon injection equipment 194, one stage or location may be owned and / or operated by the same entity or a different entity that owns and / or operates one or more of the other stages or locations.

[0055] Some or all of the operation of the carbon injection equipment 194 may be controlled by a controller 104 (discussed below). In such a case, the controller 104 may base some or all of its control on measurements, captured by one or more sensor devices 160 (discussed below), of one or more parameters (e.g., mass, chemical composition, specific gravity, temperature, moisture content, volume) associated with the carbon 147. A controller 104 of the carbon injection equipment 194 may be in communication with or may be part of a controller 104 of the carbon preparation apparatus 193. Some or all of the carbon injection equipment 194 may operate on a continuous basis or in discrete periods or intervals of time.

[0056] In some cases, a controller 104 (e.g., external to the carbon injection equipment 194, internal to the carbon injection equipment 194) is designed to control one or more components of the carbon injection equipment 194 in order to inject carbon 147 having certain characteristics (e.g., a certain pressure or range of pressures, a certain flow rate or range of flow rates, a certain temperature or range of temperatures). In addition, or in the alternative, one or more of the components of the carbon injection equipment 194 may be controlled manually (e.g., by a user 159, including an associated user system 155).

[0057] In some cases, some or all of the carbon injection equipment 194 may be operated based on measurements, made by one or more sensor devices 160. Each sensor device 160 of or used by the carbon injection equipment 194 (as well as the rest of the overall system 100) includes one or more sensors that measure one or more parameters (e.g., pressure, flow rate, temperature, humidity, depth, location, chemical composition of the carbon 147, volume, voltage, electrical current, viscosity of the carbon 147, etc.). Examples of a sensor of a sensor device 160 may include, but are not limited to, a temperature sensor, a flow sensor, a pressure sensor, a gas spectrometer, a voltmeter, an ammeter, a gyroscope, a spectrograph, a gas chromatograph, a load cell, a viscometer, and a camera. A sensor device 160 may be a standalone device or integrated with another component of the carbon injection equipment 194 (or other part of the overall system 100).

[0058] Examples of components of the carbon injection equipment 194 used to inject thecarbon 147 may include, but are not limited to, a motor, a pump, a filter, a compressor, a condenser, a circulating system, an aerator, a baffle, piping, a valve, a fan, a blower, a heater, a heat exchanger, and a cooler. When the carbon injection equipment 194 includes multiple components or pieces of equipment used to inject carbon 147, these components or pieces of equipment may operate in series and / or in parallel with each other. A controller 104 may also use measurements made by one or more sensor devices 160 to drive control decisions made by the controller 104 with respect to the operation of the components of the carbon injection equipment 194. Such decisions may be based, at least in part, whether in real time or with some time delay, on one or more outputs generated by the example system 190 for CCS site screening and characterization.

[0059] Each sensor device 160 of the overall system 100 includes one or more sensors that measure one or more parameters (e.g., pressure, flow rate, temperature, humidity, depth, location, content of carbon 147, specific gravity, voltage, electrical current, etc.). Examples of a sensor of a sensor device 160 may include, but are not limited to, a seismograph, a seismometer, a temperature sensor, a wireline tool, a flow sensor, a pressure sensor, a gas spectrometer, a voltmeter, an ammeter, a gyroscope, a spectrograph, a gas chromatograph, a viscometer, and a camera. A sensor device 160 may be a stand-alone device or integrated with another component (e.g., the carbon injection equipment 194) of the overall system 100.

[0060] As discussed above, a parameter measured by a sensor device 160 may be associated with the carbon 147 and / or the unprocessed carbon 127. In addition, or in the alternative, a parameter measured by a sensor device 160 may be associated with CCS site screening and characterization within the subterranean formation 110. For example, a sensor device 160 may be configured to generate seismic data with respect to portions of the subterranean formation 110.

[0061] In some cases, a number of sensor devices 160, each measuring a different parameter, may be used in combination to determine and confirm whether a controller 104 should take a particular action (e.g., operate a valve, adjust the speed of a motor, operate or adjust the operation of the carbon preparation apparatus 193). When a sensor device 160 includes its own controller (or portions thereof), similar to a controller 104, then the sensor device 160 may be considered a type of computer device, as discussed below with respect toFIG. 3.

[0062] A user 159 may be any person that interacts, directly or indirectly, with a controller 104 and / or any other component of the overall system 100, including any component of the example system 190 for CCS site screening and characterization. Examples of a user 159 may include, but are not limited to, a business owner, an engineer, a company representative, a geologist, a consultant, a drilling engineer, a contractor, a regulatory authority, and a manufacturer’s representative. A user 159 may use one or more user systems 155, which may include a display (e.g., a GUI). A user system 155 of a user 159 may interact with (e.g., send data to, obtain data from) a controller 104 via an application interface and using the communication links 105. The user 159 may also interact directly with a controller 104 through a user interface (e g., keyboard, mouse, touchscreen). Examples of a user system 155 may include, but are not limited to, a cell phone, a smart phone, a desktop computer, a laptop computer, a tablet, and a handheld electronic device.

[0063] The network manager 180 is a device or component that controls all or a portion (e.g., a communication network, a controller 104) of the overall system 100 or portions thereof, including one or more components of the system 190 for CCS site screening and characterization. The network manager 180 may be substantially similar to some or all of a controller 104, as described above. For example, the network manager 180 may include a controller that has one or more components and / or similar functionality to some or all of a controller 104. Alternatively, the network manager 180 may include one or more of a number of features in addition to, or altered from, the features of a controller 104. As described herein, control and / or communication with the network manager 180 may include communicating with one or more other components of the overall system 100 (including one or more components of the system 190 for CCS site screening and characterization and / or the field system 199) and / or another system. In such a case, the network manager 180 may facilitate such control and / or communication. The network manager 180 may be called by other names, including but not limited to a master controller, a network controller, and an enterprise manager. The network manager 180 may be considered a type of computer device, as discussed below with respect to FIG. 3.

[0064] Interaction between each controller 104, the sensor devices 160, the users 159(including any associated user systems 155), the network manager 180, and other components (e.g., the carbon sources 128, the carbon preparation apparatus 193, carbon injection equipment 194) of the overall system 100, including other components of the system 190 for CCS site screening and characterization, may be conducted using communication links 105 and / or power transfer links 187.

[0065] Each communication link 105 may include wired (e.g., Class 1 electrical cables, electrical connectors, Power Line Carrier, RS485) and / or wireless (e.g., Wi-Fi, Zigbee, visible light communication, cellular networking, Bluetooth, Bluetooth Low Energy (BLE), ultrawide band (UWB), WirelessHART, ISA100) technology. Each power transfer link 187 may include one or more electrical conductors, which may be individual or part of one or more electrical cables. In some cases, as with inductive power, power may be transferred wirelessly using power transfer links 187. A power transfer link 187 may transmit power from one component (e.g., a power source 189) of the overall system 100 to another (e.g., a controller 104). When in the form of electrical cables, each power transfer link 187 may be sized (e.g., 12 gauge, 18 gauge, 4 gauge) in a manner suitable for the amount (e.g., 480V, 24V, 120V) and type (e.g., alternating current, direct current) of power transferred therethrough.

[0066] A controller 104 of the overall system 100 is configured to communicate with and in some cases control one or more of the other components (e.g., a sensor device 160, the carbon injection equipment 194, a valve, another controller 104) of the overall system 100, including other components of the example system 190 for CCS site screening and characterization. A controller 104 performs any of a number of functions that include, but are not limited to, obtaining and sending data, evaluating data, following protocols, running algorithms, and sending commands.

[0067] A controller 104 may include one or more of a number of components. An example of the components of a controller 104 of the example system 190 for CCS site screening and characterization is provided below with respect to FIG. 2. A controller 104 (or components thereof) may be located at or near the various components of the overall system 100, including the example system 190 for CCS site screening and characterization. In addition, or in the alternative, a controller 104 (or components thereof) may be located remotely from (e.g., in the cloud, at an office building) the various components of the overall system 100.

[0068] When there are multiple controllers 104 (e.g., one controller 104 for one or more of the power sources 189, another controller 104 for a motor of the conveyance network 188, yet another controller 104 for the carbon inj ection equipment 194), each controller 104 may operate independently of each other. Alternatively, two or more of the multiple controllers 104 may work cooperatively with each other. As yet another alternative, one of the controllers 104 may control some or all of one or more other controllers 104 in the overall system 100 or portion thereof (e g., the system 190 for CCS site screening and characterization). Each controller 104 may be considered a type of computer device, as discussed below with respect to FIG. 3.

[0069] Each power source 189 of the overall system 100 may provide power and / or control signals to one or more of the other components (e.g., a controller 104, a sensor device 160, the carbon injection equipment 194, the carbon preparation apparatus 193) via power transfer links 187 and / or communication links 105 (discussed below). In some cases, a power source 189 obtains power from a power supply (e.g., AC mains, a generator) and manipulates (e.g., transforms, rectifies, inverts) that power to provide the manipulated power to one or more other components of the overall system 100 or portions thereof, where the manipulated power is of a type (e.g., alternating current, direct current) and level (e.g., 12V, 24V, 120V) that may be used by one or more of the other components of the overall system 100.

[0070] A power source 189 may include one or more of a number of single or multiple discrete components (e.g., transistor, diode, resistor, transformer) and / or a microprocessor. A power source 189 may include a printed circuit board, upon which the microprocessor and / or one or more discrete components are positioned. In addition, or in the alternative, a power source 189 may be a source of power in itself to provide power and / or control signals to the other components of the overall system 100. For example, a power source 189 may be or include one or more energy storage devices (e.g., batteries). As another example, a power source 189 may be or include a photovoltaic generation system.

[0071] FIG. 2 shows a block diagram of a controller 104 of the example system 190 for CCS site screening and characterization of FIG. 1 according to certain example embodiments. Referring to the description above with respect to FIG. 1, the controller 104 of FIG. 2 includes multiple components or modules. For example, as shown in FIG. 2, the components of such a controller 104 of the example system 190 for CCS site screening and characterization mayinclude, but are not limited to, a control engine 206, a map integration module 241, a translation module 243, a simulation module 244, a calibration module 245, a recommendation module 242, a communication module 207, a timer 235, a power module 230, a storage repository 231, a hardware processor 221, memory 222, a transceiver 224, an application interface 226, and, optionally, a security module 223.

[0072] In such a case, a controller 104 of the example system 190 for CCS site screening and characterization may be configured to perform analysis (e.g., seismic analysis, geometric analysis, plume analysis, temperature analysis, flow rate analysis) on the subterranean formation 110. In this way, a controller 104 may be used, for example, to monitor the performance and / or status of the example system 190 for CCS site screening and characterization (including portions thereof) in real time. In some cases, a controller 104 of the example system 190 for CCS site screening and characterization may also control and / or assess the performance of part (e.g., the carbon preparation apparatus 193, the carbon injection equipment 194) of the field system 199 during a CCS operation. In the latter case, the controller 104 can make improvements to algorithms 233 and / or protocols 232 based on differences between predicted results and actual data.

[0073] The various components of the controller 104 may be centrally located. In addition, or in the alternative, some of the components of the controller 104 may be located remotely from (e g., in the cloud, at an office building) one or more of the other components of the controller 104. The components shown in FIG. 2 are not exhaustive, and in some embodiments, one or more of the components shown in FIG. 2 may not be included in the example controller 104 of the example system 190 for CCS site screening and characterization. Any component of the controller 104 may be discrete or combined with one or more other components of the controller 104. Also, one or more components of the controller 104 may have different configurations. For example, the controller 104, rather than being a stand-alone device, may be part of one or more other components of the example system 190 for CCS site screening and characterization. For instance, part of the controller 104 may be integrated with a sensor device 160, the carbon preparation apparatus 193, part of the conveyance network 188, and / or some other component of the overall system 100.

[0074] The storage repository 231 of the controller 104 may be a persistent storage device(or set of devices) that stores software and data used to assist the controller 104 in communicating with one or more other components of the overall system 100 (including other components of the example system 190 for CCS site screening and characterization), such as the users 159 (including associated user systems 155), the network manager 180, the other controllers 104, the sensor devices 160, the power sources 189, the carbon preparation apparatus 193, the carbon injection equipment 194, the valves, and / or any other components of the overall system 100. In one or more example embodiments, the storage repository 231 stores one or more protocols 232, one or more algorithms 233, and stored data 234.

[0075] The protocols 232 of the storage repository 231 may be any procedures (e.g., a series of method steps) and / or other similar operational processes that the control engine 206 of the controller 104 follows based on certain conditions at a point in time. The protocols 232 may include any of a number of communication protocols that are used to send and / or obtain data between the controller 104 and other components of the overall system 100, including other components of the system 190 for CCS site screening and characterization. Such protocols 232 used for communication may be a time-synchronized protocol. Examples of such time- synchronized protocols may include, but are not limited to, a highway addressable remote transducer (HART) protocol, a wirelessHART protocol, and an International Society of Automation (ISA) 100 protocol. In this way, one or more of the protocols 232 may provide a layer of security to the data transferred within the overall system 100. Other protocols 232 used for communication may be associated with the use of Wi-Fi, Zigbee, visible light communication (VLC), cellular networking, BLE, UWB, and Bluetooth.

[0076] The algorithms 233 may be or include any formulas, mathematical models, forecasts, simulations, and / or other similar tools that a component (e.g., the control engine 206, the map integration module 241, the translation module 243, the simulation module 244, the calibration module 245, the recommendation module 242) of the controller 104 (including portions thererof) uses to reach a computational conclusion.

[0077] Stored data 234 may be any data associated with the various equipment (e.g., the carbon preparation apparatus 193, a power source 189, a sensor device 160), including associated components, of the example system 190 for CCS site screening and characterization, the user systems 155, the network manager 180, the other controllers 104, the sensor devices160 outside the system 190 for CCS site screening and characterization, measurements made by the sensor devices 160, specifications of the sensor devices 160, the composition of the unprocessed carbon 127, the composition of the carbon 147, temperature values, viscosity values, threshold values, scaling factors, parameters for shifting uncalibrated column height curves, ranges of acceptable values, tables, results of previously run or calculated algorithms 233, updates to protocols 232 and / or algorithms 233, user preferences, and / or any other suitable data. Such data may be any type of data, including but not limited to historical data, present data, and future data (e.g., forecasts). The stored data 234 may be associated with some measurement of time derived, for example, from the timer 235.

[0078] Examples of a storage repository 231 may include, but are not limited to, a database (or a number of databases), a file system, cloud-based storage, a hard drive, flash memory, some other form of solid-state data storage, or any suitable combination thereof. The storage repository 231 may be located on multiple physical machines, each storing all or a portion of the protocols 232, the algorithms 233, and / or the stored data 234 according to some example embodiments. Each storage unit or device may be physically located in the same or in a different geographic location.

[0079] The storage repository 231 may be operatively connected to the control engine 206. In one or more example embodiments, the control engine 206 includes functionality to communicate with the users 159 (including associated user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components in the overall system 100 (including other components of the example system 190 for CCS site screening and characterization). More specifically, the control engine 206 sends information to and / or obtains information from the storage repository 231 in order to communicate with the users 159 (including associated user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100 (including other components of the example system 190 for CCS site screening and characterization). As discussed below, the storage repository 231 may also be operatively connected to the communication module 207 in certain example embodiments.

[0080] In certain example embodiments, the control engine 206 of the controller 104 controls the operation of one or more components (e.g., the communication module 207, thetimer 235, the transceiver 224) of the controller 104. For example, the control engine 206 may activate the communication module 207 when the communication module 207 is in “sleep” mode and when the communication module 207 is needed to send data obtained from another component (e.g., a sensor device 160, another controller 104) in the overall system 100, including other components of the example system 190 for CCS site screening and characterization. In addition, the control engine 206 of the controller 104 may control the operation of one or more other components (e g., a sensor device 160, another controller 104), or portions thereof, of the overall system 100 (including components of the example system 190 for CCS site screening and characterization).

[0081] The control engine 206 of the controller 104 may communicate with one or more other components of the overall system 100 (including other components of the example system 190 for CCS site screening and characterization). For example, the control engine 206 may use one or more protocols 232 to facilitate communication with the sensor devices 160 of the overall system 100 (including the example system 190 for CCS site screening and characterization) to obtain data (e g., seismic data, measurements of other various parameters (e g., temperature, viscosity, chemical composition, pressure, proximity, flow rate)), whether in real time or on a periodic basis, and / or to instruct a sensor device 160 to take a measurement. The control engine 206 may use measurements (including the associated values) of parameters taken by the sensor devices 160 to perform one or more steps in screening and characterizing CCS sites within the subterranean formation 110 using one or more protocols 232 and / or one or more algorithms 233.

[0082] For instance, the control engine 206 may use one or more algorithms 233 and / or one or more protocols 232 to obtain values associated with measurements of one or more parameters associated with a potential CCS site. If the sensor device 160 that made the measurement is not capable of generating an associated value for the measurement, then the control engine 206, using one or more algorithms 233, one or more protocols 232 and / or stored data 234, may generate values based on the measurements. In some cases, the control engine 206, using one or more algorithms 233, one or more protocols 232 and / or stored data 234, may validate and / or format the measurements made by a sensor device 160 and received by the control engine 206 before the measurements are used by the controller 104 and / or communicated to anothercomponent (e.g., a user system 155, the network manager 180) in the overall system 100.

[0083] As still another example, the control engine 206 (or some other component of the controller 104 working in conjunction with the control engine 206, including but not limited to the map integration module 241 and the translation module 243) may use one or more algorithms 233 and / or one or more protocols 232 to use the values associated with the measurements to generate a result (e.g., a numeric value, a range of probabilities). As yet another example, the control engine 206 (or some other component of the controller 104 working in conjunction with the control engine 206, including but not limited to the recommendation module 242) may use one or more algorithms 233 and / or one or more protocols 232 to compare the result of an algorithm 233 with a range of acceptable values (e.g., stored data 234), where the range of acceptable values is established using prior results (e.g., stored data 234) of the algorithm 233.

[0084] As still another example, the control engine 206, using one or more algorithms 233, one or more protocols 232, and stored data 234, may modify or establish a new algorithm 233 and / or protocols 232 based on differences between expected values and actual values. As yet another example, the control engine 206, using one or more algorithms 233, one or more protocols 232, and stored data 234, may use the values associated with the measurements made by the sensor devices 160 to control the operation of one or more portions (e.g., the carbon preparation apparatus 193, the fluid injection equipment 194, part of the conveyance network 188) of the field system 199.

[0085] The control engine 206 may generate and process data associated with control, communication, and / or other signals sent to and obtained from the users 159 (including associated user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization. In certain embodiments, the control engine 206 of the controller 104 may communicate with one or more components of a system external to the overall system 100. For example, the control engine 206 may interact with an inventory management system by ordering, in real time, replacements for components or pieces of equipment (e.g., a sensor device 160, a valve, a motor) within the overall system 100 that has failed or is failing. As another example, the control engine 206 mayinteract with a contractor or workforce scheduling system, in real time, by arranging for the labor needed to replace a component or piece of equipment in the system. In this way and in other ways as discussed herein, the controller 104 is capable of performing a number of functions beyond what could reasonably be considered a routine task.

[0086] In certain example embodiments, the control engine 206 may include an interface that enables the control engine 206 to communicate with the other controllers 104, the sensor devices 160, the user systems 155, the network manager 180, and any other components of the overall system 100, including other components of components of the example system 190 for CCS site screening and characterization. For example, if a user system 155 operates under IEC Standard 62386, then the user system 155 may have a serial communication interface that will transfer data to the controller 104. Such an interface may operate in conjunction with, or independently of, the protocols 232 used to communicate between the controller 104 and the users 159 (including corresponding user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization.

[0087] The control engine 206 (or other components of the controller 104) may also include one or more hardware components and / or software elements to perform its functions. Such components may include, but are not limited to, a universal asynchronous receiver / transmitter (UART), a serial peripheral interface (SPI), a direct-attached capacity (DAC) storage device, an analog-to-digital converter, an inter-integrated circuit (I2C), and a pulse width modulator (PWM).

[0088] The map integration module 241 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to process (e.g., generate, format, filter, organize, merge, combine) data (e.g., from a table, from one or more existing maps) in order to generate a map. In addition, or in the alternative, the map integration module 241 may be configured to generate one or more maps using measurements from one or more sensor devices 160 (e.g., seismic data). In addition, or in the alternative, the map integration module 241 may be configured to update, revise, and / or otherwise alter a map.

[0089] In addition, or in the alternative, the map integration module 241 may be configuredto integrate, merge, and / or otherwise combine two or more maps (or tables or other data associated with a map) into a single map. In addition, or in the alternative, the map integration module 241 may be configured to process (e.g., generate, format, filter, organize, merge) other forms (e.g., tables, graphs, datasets) of data aside from map formats. The map integration module 241 may operate using one or more algorithms 233, one or more protocols 232, and / or stored data 234 (e.g., measurements made by one or more sensor devices 160). As an example, the map integration module 241 may use one or more algorithms 233, one or more protocols 232, and stored data 234 to assist the controller 104 to use the values associated with the measurements made by the sensor devices 160 to generate and / or combine different types of maps (e.g., geometry maps, foot -mean-square (RMS) maps) of the subterranean formation 110 at a point in time and / or over time. The map integration module 241 may operate continuously or periodically.

[0090] The translation module 243 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to translate one type of data and / or data directed to a certain parameter into another type of data and / or data directed to another parameter. The translation module 243 may be configured to recognize and perform a translation with respect to any type of data and / or data directed to any parameter associated with CCS site screening and characterization. The translation module 243 may operate using one or more algorithms 233, one or more protocols 232, and / or stored data 234 (e.g., measurements made by one or more sensor devices 160). As an example, the translation module 243 may use one or more algorithms 233, one or more protocols 232, and stored data 234 to assist the controller 104 to translate seismic data (associated with the measurements made by a sensor device 160) to rock properties at a point in time and / or over time. The translation module 243 may operate continuously or periodically.

[0091] The simulation module 244 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to execute certain models among the algorithms 233. Examples of such models may include, but are not limited to, IP models, basin models, and Darcy models. The simulation module 244 may operate using one or more algorithms 233, one or more protocols 232, and / or stored data 234 (e.g., measurements made by one or more sensor devices 160). As an example, the simulation module 244 may use oneor more algorithms 233, one or more protocols 232, and stored data 234 to assist the controller 104 to simulate, in an IP model, the injection of carbon 147 into part of the subterranean formation 110 and determine whether the translation of seismic data to rock properties (as performed by the translation module 243) needs adjustment. The simulation module 244 may operate continuously or periodically.

[0092] The calibration module 245 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to calibrate (e.g., add or subtract values to uncalibrated column heights, apply a scaling factor to uncalibrated column heights). In some cases, the calibration module 245 identifies which data requires calibration and how such calibration should be executed (e.g., which scaling factor to apply). The calibration module 245 may operate using one or more algorithms 233, one or more protocols 232, and / or stored data 234 (e.g., measurements made by one or more sensor devices 160). As an example, the calibration module 245 may use one or more algorithms 233, one or more protocols 232, and stored data to assist the controller 104 to calibrate the uncalibrated carbon plume modeled by the initial IP simulation to generated calibrated column heights. The calibration module 245 may operate continuously or periodically.

[0093] The recommendation module 242 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to output a final 3D plume prediction. In addition, or in the alternative, the recommendation module 242 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to assess one or more algorithms based on data received from one or more injection operations, determine whether an algorithm needs to be modified based on the actual data, and modify the algorithm based on the actual data.

[0094] In addition, or in the alternative, the recommendation module 242 of the controller 104 of the example system 190 for CCS site screening and characterization may be configured to recommend actions to be taken (e.g., make an injection at a different wellbore 111, change the position of a valve, change the chemical composition of the fluid carbon 147, add an additive to the unprocessed carbon 127) based on a final 3D plume prediction and / or actual data obtained during an injection operation. The recommendation module 242 may operate using one or more algorithms 233, one or more protocols 232, and / or stored data 234 (e.g., measurements madeby one or more sensor devices 160). As an example, the recommendation module 242 may use one or more algorithms 233, one or more protocols 232, and stored to assist the controller 104 to generate a final three-dimensional carbon plume forecast using IP simulations.

[0095] As another example, the recommendation module 242 may use one or more algorithms 233, one or more protocols 232, and stored data 234 to assist the controller 104 to direct and / or control operation of the carbon sources 128, the carbon preparation apparatus 193, and / or the carbon injection equipment 194 during a CCS operation. For instance, the recommendation module 242 may use one or more algorithms 233, one or more protocols 232, and stored data 234 to assist the controller 104 to use the values associated with the measurements made by the sensor devices 160 to control the operation of one or more of the carbon sources 128, the carbon preparation apparatus 193, and / or the carbon injection equipment 194 during a CCS operation.

[0096] In some cases, the recommendation module 242 may provide recommendations to a user 159 (including an associated user system 155), another controller 104, the network manager 180, and / or some other entity. The recommendation module 242 may provide recommendations automatically (e.g., based on the passage of time (e.g., every 6 hours), based on the occurrence of some event (e.g., when the measurement of a parameter falls outside a range of acceptable values), when an algorithm 233 (e.g., a model) has been run, upon request from a user 159, and / or on some other basis. The recommendation module 242 may operate continuously or periodically.

[0097] The communication module 207 of the controller 104 determines and implements the communication protocol (e.g., from the protocols 232 of the storage repository 231) that is used when the control engine 206 communicates with (e.g., sends signals to, obtains signals from) the user systems 155, the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization.

[0098] The timer 235 of the controller 104 may track clock time, intervals of time, an amount of time, and / or any other measure of time. The timer 235 may also count the number of occurrences of an event, whether with or without respect to time. Alternatively, the control engine 206 may perform a counting function. The timer 235 is able to track multiple timemeasurements and / or count multiple occurrences concurrently. The timer 235 may track time periods based on an instruction obtained from the control engine 206, based on an instruction obtained from a user 159, based on an instruction programmed in the software for the controller 104, based on some other condition (e.g., the occurrence of an event) or from some other component, or from any combination thereof. In certain example embodiments, the timer 235 may provide a time stamp for each packet of data obtained from another component (e.g., a sensor device 160) of the example system 190 for CCS site screening and characterization.

[0099] The power module 230 of the controller 104 may be configured to obtain power from a power source 189 and manipulate (e.g., transforms, rectifies, inverts) that power to provide the manipulated power to one or more other components (e.g., the timer 235, the control engine 206) of the controller 104, where the manipulated power is of a type (e.g., alternating current, direct current) and level (e.g., 12V, 24V, 120V) that may be used by the other components of the controller 104. In some cases, the power module 230 may also provide power to one or more of the sensor devices 160 of the example system 190 for CCS site screening and characterization.

[0100] The hardware processor 221 of the controller 104 executes software, algorithms (e.g., algorithms 233), and firmware in accordance with one or more example embodiments. Specifically, the hardware processor 221 may execute software on the control engine 206 or any other portion of the controller 104, as well as software used by the users 159 (including associated user systems 155), the other controllers 104, the network manager 180, and / or other components of the overall system 100, including other components of components of the example system 190 for CCS site screening and characterization.

[0101] In one or more example embodiments, the hardware processor 221 executes software instructions stored in memory 222. The memory 222 includes one or more cache memories, main memory, and / or any other suitable type of memory. The memory 222 may include volatile and / or non-volatile memory. The memory 222 may be discretely located within the controller 104 relative to the hardware processor 221. In certain configurations, the memory 222 may be integrated with the hardware processor 221.

[0102] In certain example embodiments, the controller 104 does not include a hardware processor 221. In such a case, the controller 104 may include, as an example, one or more fieldprogrammable gate arrays (FPGA), one or more insulated-gate bipolar transistors (IGBTs), and / or one or more integrated circuits (ICs). Using FPGAs, IGBTs, ICs, and / or other similar devices known in the art allows the controller 104 (or portions thereof) to be programmable and function according to certain logic rules and thresholds without the use of a hardware processor. Alternatively, FPGAs, IGBTs, ICs, and / or similar devices may be used in conjunction with one or more hardware processors 221.

[0103] The transceiver 224 of the controller 104 may send and / or obtain control and / or communication signals. Specifically, the transceiver 224 may be used to transfer data between the controller 104 and the users 159 (including associated user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100, including other components of components of the example system 190 for CCS site screening and characterization.

[0104] Optionally, in one or more example embodiments, the security module 223 secures interactions between the controller 104, the users 159 (including associated user systems 155), the other controllers 104, the sensor devices 160, the network manager 180, and any other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization. More specifically, the security module 223 authenticates communication from software based on security keys verifying the identity of the source of the communication. For example, user software may be associated with a security key enabling the software of a user system 155 to interact with the controller 104. Further, the security module 223 may restrict receipt of information, requests for information, and / or access to information.

[0105] A user 159 (including an associated user system 155), the other controllers 104, the sensor devices 160, the network manager 180, and the other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization, may interact with the controller 104 using the application interface 226. Specifically, the application interface 226 of the controller 104 obtains data (e.g., information, communications, instructions, updates to firmware) from and sends data (e.g., information, communications, instructions) to the user systems 155 of the users 159, the other controllers 104, the sensor devices 160, the network manager 180, and / or the other components of theoverall system 100, including other components of the example system 190 for CCS site screening and characterization.

[0106] Examples of an application interface 226 may be or include, but are not limited to, an application programming interface, a web service, a data protocol adapter, some other hardware and / or software, or any suitable combination thereof. Similarly, the user systems 155 of the users 159, the other controllers 104, the sensor devices 160, the network manager 180, and / or the other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization, may include an interface (similar to the application interface 226 of the controller 104) to obtain data from and send data to the controller 104 in certain example embodiments.

[0107] In addition, as discussed above with respect to a user system 155 of a user 159, one or more of the controllers 104, one or more of the sensor devices 160, the network manager 180, and / or one or more of the other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization, may include a user interface. Examples of such a user interface may include, but are not limited to, a graphical user interface, a touchscreen, a keyboard, a monitor, a mouse, some other hardware, or any suitable combination thereof.

[0108] The controllers 104, the users 159 (including associated user systems 155), the sensor devices 160, the network manager 180, and the other components of the overall system 100, including other components of the example system 190 for CCS site screening and characterization, may use their own system or share a system in certain example embodiments. Such a system may be, or contain a form of, an Internet-based or an intranet-based computer system that is capable of communicating with various software. A computer system includes any type of computing device and / or communication device, including but not limited to the controller 104. Examples of such a system may include, but are not limited to, a desktop computer with a Local Area Network (LAN), a Wide Area Network (WAN), Internet or intranet access, a laptop computer with LAN, WAN, Internet or intranet access, a smart phone, a server, a server farm, an android device (or equivalent), a tablet, smartphones, and a personal digital assistant (PDA). Such a system may correspond to a computer system as described below with regard to FIG. 3.

[0109] Further, as discussed above, such a system may have corresponding software (e.g., user system software, sensor device software, controller software). The software may execute on the same or a separate device (e.g., a server, mainframe, desktop personal computer (PC), laptop, PDA, television, cable box, satellite box, kiosk, telephone, mobile phone, or other computing devices) and may be coupled by the communication network (e.g., Internet, Intranet, Extranet, LAN, WAN, or other network communication methods) and / or communication channels, with wire and / or wireless segments according to some example embodiments. The software of one system may be a part of, or operate separately but in conjunction with, the software of another system within and / or outside of the overall system 100.

[0110] FIG. 3 shows a block diagram of a computing device 318 according to certain example embodiments. Specifically, FIG. 3 illustrates one embodiment of a computing device 318 that implements one or more of the various techniques described herein, and which is representative, in whole or in part, of the elements described herein pursuant to certain example embodiments. For example, a controller 104 (including components thereof, such as a control engine 206, a hardware processor 221, a storage repository 231, a power module 230, and a transceiver 224) may be considered a computing device 318. The computing device 318 of FIG. 3 is one example of a computing device and is not intended to suggest any limitation as to scope of use or functionality of the computing device and / or its possible architectures. Neither should the computing device 318 of FIG. 3 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the example computing device 318 of FIG. 3.

[0111] The computing device 318 includes one or more processors or processing units 314, one or more memory / storage components 315, one or more input / output (I / O) devices 316, and a bus 317 that allows the various components and devices to communicate with one another. The bus 317 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. The bus 317 includes wired and / or wireless buses.

[0112] The memory / storage component 315 represents one or more computer storage media. The memory / storage component 315 includes volatile media (such as random accessmemory (RAM)) and / or nonvolatile media (such as read only memory (ROM), flash memory, optical disks, magnetic disks, and so forth). The memory / storage component 315 includes fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) as well as removable media (e.g., a Flash memory drive, a removable hard drive, an optical disk, and so forth).

[0113] One or more I / O devices 316 allow a user 159 to enter commands and information to the computing device 318, and also allow information to be presented to the user 159 and / or other components or devices. Examples of input devices 316 include, but are not limited to, a keyboard, a cursor control device (e.g., a mouse), a microphone, a touchscreen, and a scanner. Examples of output devices include, but are not limited to, a display device (e.g., a monitor or projector), speakers, outputs to a lighting network (e.g., DMX card), a printer, and a network card.

[0114] Various techniques are described herein in the general context of software or program modules. Generally, software includes routines, programs, objects, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. An implementation of these modules and techniques is stored on or transmitted across some form of computer readable media. Computer readable media is any available non-transitory medium or non-transitory media that is accessible by a computing device. By way of example, and not limitation, computer readable media includes “computer storage media”.

[0115] “Computer storage media” and “computer readable medium” include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, computer recordable media such as RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which is used to store the desired information and which is accessible by a computer.

[0116] The computer device 318 (also sometimes called a computer system herein) is connected to a network (not shown) (e.g., a LAN, a WAN such as the Internet, cloud, or any other similar type of network) via a network interface connection (not shown) according to some example embodiments. Those skilled in the art will appreciate that many different typesof computer systems exist (e.g., desktop computer, a laptop computer, a personal media device, a mobile device, such as a cell phone or personal digital assistant, or any other computing system capable of executing computer readable instructions), and the aforementioned input and output means take other forms, now known or later developed, in other example embodiments. Generally speaking, the computer device 318 includes at least the minimal processing, input, and / or output means necessary to practice one or more embodiments.

[0117] Further, those skilled in the art will appreciate that one or more elements of the aforementioned computer device 318 is located at a remote location and connected to the other elements over a network in certain example embodiments. Further, one or more embodiments are implemented on a distributed system having one or more nodes, where each portion of the implementation (e.g., the example system 190 for CCS site screening and characterization) is located on a different node within the distributed system. In one or more embodiments, the node corresponds to a computer system. Alternatively, the node corresponds to a processor with associated physical memory in some example embodiments. The node alternatively corresponds to a processor with shared memory and / or resources in some example embodiments.

[0118] FIG. 4 shows a flowchart 498 of a method for screening and characterizing CCS sites according to certain example embodiments. While the various steps in this flowchart 498 are presented sequentially, one of ordinary skill will appreciate that some or all of the steps may be executed in different orders, may be combined or omitted, and some or all of the steps may be executed in parallel. Further, in one or more of the example embodiments, one or more of the steps shown in this example method may be omitted, repeated, and / or performed in a different order. Some or all of the steps of the method of FIG. 4 may be performed off site (e.g., in a laboratory remote from a field operation). In addition, or in the alternative, some or all of the steps of the method of FIG. 4 may be performed on site (e.g., in the field, adjacent to a wellbore 111) where a field operation is being performed or planned.

[0119] In addition, a person of ordinary skill in the art will appreciate that additional steps not shown in FIG. 4 may be included in performing this method. Accordingly, the specific arrangement of steps should not be construed as limiting the scope. Further, a particular computing device, such as the computing device 318 discussed above with respect to FIG. 3,may be used to facilitate (e.g., direct, control, provide instructions, provide recommendations, perform, execute) performance of one or more of the steps (or portions thereof) for the methods shown in FIG. 4 in certain example embodiments. Any of the functions performed below by or using a controller 104 (an example of which is discussed above with respect to FIG. 2) may involve the use of one or more protocols 232, one or more algorithms 233, and / or stored data 234 stored in a storage repository 231. In addition, or in the alternative, any of the functions (or portions thereof) in the method may be performed by a user (e.g., user 159).

[0120] The method shown in FIG. 4 is merely an example that may be performed by using an example system described herein. In other words, systems for screening and characterizing CCS sites may perform other functions using other methods in addition to and / or aside from those described with respect to FIG. 4. Referring to the description above with respect to FIGS. 1 through 3, the method shown in the flowchart 498 of FIG. 4 begins at the START step and proceeds to step 461, where data about the subterranean formation (e.g., subterranean formation 110) is obtained. As used herein, the term “obtaining” may include collecting, receiving, retrieving, accessing, generating, etc. or any other manner of obtaining information, which in this case is data about the subterranean formation.

[0121] The data about the subterranean formation 110 may be obtained, directly or indirectly, from one or more sensor devices 160. The data may include, but is not limited to, seismic data. The data may be or include values of one or more parameters associated with the subterranean formation 110. Examples of such parameters may include, but are not limited to, depth, temperature, pressure, density, and formation type. The data about the subterranean formation 110 may be obtained by or using a controller 104 (including the control engine 206 and / or other module thereof) of the example system 190 for CCS site screening and characterization. The data about the subterranean formation 110 may be obtained using one or more algorithms 233, one or more protocols 232, and / or communication links 105. The data that is obtained may become stored data 234.

[0122] In step 462, an initial fluid density map of the subterranean formation 110 is generated. The initial fluid density map is generated using at least some of the data obtained in step 461 above. The fluid that is subject to the mapping may be or include a greenhouse gas that is planned for injection into the subterranean formation 110. For example, the fluid maybe or include carbon dioxide. The initial fluid density map of the subterranean formation 110 may be generated by or using a controller 104 (including the control engine 206 and / or other module thereof) of the example system 190 for CCS site screening and characterization. The initial fluid density map of the subterranean formation 110 may be generated using one or more algorithms 233, one or more protocols 232, and / or stored data 234.

[0123] Regional and local basin models are used at time in the oil and gas industry to predict pressure and temperature during the exploration phase, as described, for example, in Nakayama et al., “Estimation of Paleo Pore pressure and time of hydrocarbon expulsion - Computerized simulation model”, AAPG Bulletin, Vol. 66, Issue 5, p. 611 (1982). Pressure and temperature predictions may be the basis for hydrocarbon property predictions, which are used for prospect evaluations during the building phase of an exploration portfolio. Basin models used for purposes of exploration may be important, similar to how reservoir models are useful for the appraisal, development, and / or production phases.

[0124] Above the ground, in the air, the density of a fixed volume of carbon dioxide is taken as a reference point, and the carbon dioxide is in gaseous form. Carbon dioxide density is dependent on pressure, and as the graph 797 of FIG. 7 shows, at around 500 meters within the subterranean formation, the density of carbon dioxide is increased to approximately 100 kilograms per cubic meter (where the volume is about 20% of the volume of carbon dioxide in air), and the carbon dioxide remains in gaseous form. At a subsurface depth of approximately 800 meters, a sudden increase in density of carbon dioxide (approximately 600 kilograms per cubic meter) occurs, where the hydrostatic pressure is about 8 MPa. At that depth, the volume of the carbon dioxide is only 3.8% of the volume at surface conditions, and the carbon dioxide is supercritical, as described, for example, in Benson et al., “Underground geological storage, in Intergovernmental Panel on Climate Change Special Report on Carbon Dioxide Capture and Storage”, coordinating author P. Freund, pp. 195-276, Cambridge Univ. Press, Cambridge, U. K. Berg, R. R.

[1975] , Capillary pressures in stratigraphic traps: AAPG Bulletin, 59, no. 6, pp. 939-956 (2005).

[0125] From that subterranean depth, any increases in subterranean depth results in substantially small increases in density of the carbon dioxide. For example, at a subterranean depth of approximately 2400 meters, the density of carbon dioxide is approximately 700kilograms per cubic meter, and the volume of carbon dioxide is approximately 2.7% of the volume of carbon dioxide in air. Also, at this increased depth, the carbon dioxide remains in a supercritical state. In other words, at depths greater than 1500 meters, the density of carbon dioxide stays almost constant.

[0126] Returning to the method captured in the flowchart 498 of FIG. 4, in step 463, an injection zone is identified using the initial fluid density map of step 462. In some cases, multiple injection zones are identified. Each injection zone is identified using the depth at which the injected fluid (e.g., carbon dioxide) becomes supercritical as the upper boundary. Each injection zone may be identified by or using a controller 104 (including the control engine 206 and / or other module thereof) of the example system 190 for CCS site screening and characterization. The lower boundary of each injection zone may be identified using one or more algorithms 233, one or more protocols 232, and / or stored data 234.

[0127] In some cases, at depths beyond where the carbon dioxide (or other injected fluid) becomes supercritical, the density of the carbon dioxide may decrease (rather than continue to increase) depending on factors such as thermal gradients, as described, for example, in Ringrose, “Storage of Carbon Dioxide in Saline Aquifers”, SEG 2023 Distinguished Instructor Short Course Series, No. 26 (2023). The cost of compressing and injecting carbon dioxide at higher pressures is higher than the cost of compressing and injecting carbon dioxide at relatively lower pressures. Further, the deeper the injection target of carbon dioxide, the higher the chance of encountering overpressure, decreasing the effective stress margin, which is the operational window for a wellbore (e.g., wellbore 111) to be operated safely, as described, for example, in Zhang, “Pore pressure prediction from well logs: methods, modifications, and new approaches”, Earth Science Review; Vol 108; pp. 50-63 (2011). Therefore, CCS projects may attempt to ensure that pressures are always high enough so that CO2 (or some other fluid) stays in the supercritical phase (in the case of the graph 797 of FIG. 7, the depth must be greater than 800 meters).

[0128] At the same time, attempts are made to not inject carbon dioxide into overpressured zones, the depth of which may be considered the maximum depth (sometimes referred to as the seal) for CCS injection, as described, for example, in Meckel et al., “Characterization and prediction of CO2 saturation resulting from modeling buoyant fluidmigration in 2D heterogenous geologic fabrics”, International Journal of Greenhouse Gas Control, Vol. 34, pp. 85-96 (2021). Predicting the top of overpressure zones in a subterranean formation is something that basin models (types of algorithms 233) can perform for local or regional scales because basin models consider the depositional and burial history of a basin, including the associated rock properties, as described, for example, in Audet et al., “Forward modeling of porosity and pore pressure evolution in sedimentary basins”, Research, Vol. 4, pp. 147-162 (1992). Basin models can be used to identify and predict the depth of the top of the overpressure zones, even for complex salt basins. As a result, basin models may be used for pressure predictions in the early screening phase for CCS sites, as described, for example, in Alkawai et al., “Combining seismic reservoir characterization workflows with basin modeling in the deepwater Gulf of Mexico Mississippi Cayon area”, AAPG Bulletin, V. 102, No. 4 (April 2018, pp. 629-652 (2018).

[0129] In step 464, one or more injection locations within each injection zone are identified. Each injection location within an injection zone is identified by a depth or range of depths within an injection zone. Each injection zone from step 463 can have a single injection location or multiple injection locations. Each injection location is a specific area within an injection zone in which a fluid (e.g., carbon dioxide) is target for injection. Each injection location within an injection zone may be identified by or using a controller 104 (including the control engine 206 and / or other module thereof) of the example system 190 for CCS site screening and characterization. Each injection location within an injection zone may be identified using one or more algorithms 233, one or more protocols 232, and / or stored data 234.

[0130] In certain example embodiments, once an injection window has been identified based on pressure predictions, temperature may be used to select an injection location within the injection zone (also sometimes called an injection window herein). With increasing temperatures within the subterranean formation 110, the density of supercritical CO2 decreases. As a result, different geological settings can have an impact on the density of carbon dioxide, which consequently results in an impact on storage capacity. With typical subsurface CCS sites, the impact of temperature can increase or decrease the storage capacity for carbon dioxide by as much as approximately 20%. By considering temperatures (e.g., part of the data of step 461) at various points within the subsurface, particularly within each injection zone, theinjections locations may be identified. Temperature analysis may application to different locations within the same basin and / or to different basins (e.g., when considered on a global scale).

[0131] The phase diagram 597 of FIG. 5 shows an example of how subsurface temperature can influence the density of carbon dioxide at different subsurface pressures. Specifically, FIG. 5 shows a carbon dioxide phase diagram 597 according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 4, the carbon dioxide phase diagram 597 of FIG. 5 includes a left vertical axis of subsurface pressure (in MPa), a top horizontal axis of temperature (in °C), and a right vertical axis continuing to a bottom horizontal axis of density (in kilograms per cubic meter). The phase diagram 597 also shows areas where carbon dioxide is in liquid phase, in vapor (gaseous) phase, and in supercritical phase.

[0132] Returning to the method captured in the flowchart 498 of FIG. 4, in step 465, pressure and temperature maps of each injection location is generated. Each injection location is based on what is identified in step 464. Generating pressure and temperature maps of each injection location within an injection zone may be performed by or using a controller 104 (including a map integration module 241 thereof) of the example system 190 for CCS site screening and characterization. Generating pressure and temperature maps of each injection location within an injection zone may be performed using one or more algorithms 233, one or more protocols 232, and / or stored data 234.

[0133] Whether a regional CCS study is performed to evaluate the static storage capacity or in preparation for a permit application to be filed with regulators, a CO2 density map for the main storage interval may be useful and / or required. However, considering that a reservoir model is typically not available during the early screening phase and that reservoir models are designed to operate under isothermal conditions, it can be difficult to generate a density map in the current art, as described, for example, in Fanchi, Principles of Applied Reservoir Simulations (Fourth Edition), Chapter 7, Multiphase fluid flow equations, p. 121- 137, Elsevier (2018). According to example embodiments, using a 3D pressure and temperature calibrated local or regional basin model can provide very detailed temperature and pressure maps for the target interval of a CCS project.

[0134] In some cases, generating temperature and pressure maps may be a multi-stage process. For example, one stage may be to generate temperature and pressure depth targets, and a subsequent stage may be to generate the temperature and pressure maps using the temperature and pressure depth targets. In such a case, the minimum requirements to generate the temperature and pressure maps may include: i) generating a representative depth map for the target injection reservoir, ii) generating a temperature-depth profile based on well data and, iii) generating a pressure-depth profile based on regional or local well data. One or more algorithms 233 of a controller 104 of the example system 190 for CCS site screening and characterization may be used to integrate the depth map for the target injection reservoir with the temperature-depth profile based on well data to generate one or more temperature maps. In addition, or in the alternative, one or more algorithms 233 of a controller 104 of the example system 190 for CCS site screening and characterization may be used to integrate the depth map for the target injection reservoir with the pressure-depth profile based on well data to generate one or more pressure maps.

[0135] An example of the temperature and pressure maps produced using one or more algorithms 233 of a controller 104 of the example system 190 for CCS site screening and characterization is shown in FIG. 6. Specifically, FIG. 6 shows temperature and pressure maps derived from a main target depth map according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 5, the depth map of the target injection reservoir is represented by the graph 697-1 of FIG. 6. The temperature-depth profile is represented by graph 697-2 of FIG. 6. The temperature map is represented by graph 697-3 of FIG. 6. The pressure-depth profile is represented by graph 697-4 of FIG. 6. The pressure map is represented by graph 697-5 of FIG. 6.

[0136] In certain example embodiments, the controller 104 of the example system 190 for CCS site screening and characterization uses one or more algorithms 233, one or more protocols 232, and stored data 234 to use the data for graph 697-1 and graph 697-2 to generate the data used to produce graph 697-3. Similarly, the controller 104 of the example system 190 for CCS site screening and characterization uses one or more algorithms 233, one or more protocols 232, and stored data 234 to use the data for graph 697-1 and graph 697-4 to generate the data used to produce graph 697-5. Put another way, example embodiments, after definingthe temperature-depth curve and the pressure-depth curve, apply these curves to the reservoir depth map to construct a temperature map and a pressure map for the main target.

[0137] Returning to the method captured in the flowchart 498 of FIG. 4, in step 466, a final fluid density map is generated. The final fluid density map is generated using at least some of the data obtained in one or more of the steps (e.g., step 465) above. The fluid that is subject to the mapping may be or include a greenhouse gas that is planned for injection into the subterranean formation 110. For example, the fluid may be or include carbon dioxide. The final fluid density map of the subterranean formation 110 may be generated by or using a controller 104 (including a map integration module 241 thereof) of the example system 190 for CCS site screening and characterization. The final fluid density map of the subterranean formation 110 may be generated using one or more algorithms 233, one or more protocols 232, and / or stored data 234.

[0138] In certain example embodiments, the final fluid density map (also sometimes called a final fluid phase diagram herein) is digitized and made available as a look-up table so that pairs of temperature and pressure values can be used to determine the corresponding fluid (e.g., CO2) density. In some cases, the final fluid density map may be used to perform a static storage capacity calculation for regional or local evaluations during the early screening phase. As a result, a final fluid density map can help to identity the CCS opportunity window in the subsurface. Even with a relatively small amount of data, it is possible to construct an accurate pressure and temperature dependent CO2 (or other fluid) density map.

[0139] An example of a final fluid density map of the subterranean formation 110 is shown in FIG. 7. Specifically, FIG. 7 shows a carbon dioxide density map 797 derived from the temperature and pressure maps of FIG. 6 and the graph of FIG. 5 according to certain example embodiments. Steps 461 through 466 are an example of how the top seal location, the top reservoir, and the base reservoir structural depth or time maps are generated, including a general understanding of how deep the top seal is buried in the subterranean formation and the depth of the top and bottom of the reservoir under the top seal. These various maps define the seal and the reservoir / inj ection target. Both layers can be subdivided into multiple sublayers, which can be populated with seismic data.

[0140] In step 467, seismic data is translated into uncalibrated rock properties. In somecases, the seismic data is translated into rock properties according to certain schemes (e.g., corporate standard schemes) of how to convert a particular seismic signal into a standard (e.g., shale volume fraction (Vshale) rock property). As discussed below, this translation does not have to be perfect or calibrated to a wellbore and / or subterranean formation at this stage. Put another way, the translation may be generic and / or uncalibrated (e.g., using an uncalibrated method). Recognizing that there are a number of different ways to concert seismic into rock properties, the goal in this step is to capture the information (e.g., the end-members of shale, the sand) through a translation at this stage of the process. The calibration may be performed at a later time in the process. The seismic data may be translated into uncalibrated rock properties by or using a controller 104 (including a translation module 243 thereof) of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, and / or stored data 234.

[0141] In step 468, an initial plume analysis (also called an uncalibrated plume analysis herein) is performed using an IP model. The uncalibrated plume analysis and / or running the invasion percolation model (a type of algorithm 233) may be performed by or using a controller 104 (including a simulation module 244 thereof) of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, and / or stored data 234. In certain example embodiments, the invasion percolation model has geometry populated with the uncalibrated rock properties derived from seismic data with respect to step 467 above.

[0142] In certain example embodiments, CO2 (or some other fluid) is virtually injected into the invasion percolation (IP) model, and the column heights of all sub-accumulations are measured and evaluated probabilistically. Among the driving forces of IP are fluid buoyancy (see FIG. 9 below) as the upwards force and rock capillary entry pressure (see FIG. 10 below) as the counteracting force to hold fluids back. IP considers the invasion process when a wetting fluid (e.g., water) gets replaced by a non-wetting fluid (e.g., CO2, oil, gas). Because only two forces are considered in the IP method, the IP model is extremely fast to run and can be applied to very large models (e.g., large areas, large amounts of seismic data). In addition, in some cases, capillary entry pressure curves are simplified and may consist of a few discrete points to calculate a level of saturation. As discussed below, this level of saturation can be auto scaledto column height for each cell, which accelerates an output of the IP simulation. In some cases, capillary pressures as the main input for the IP simulation can be derived from seismic and fluid properties based on pressure and temperature conditions, as discussed above.

[0143] For subsurface CCS evaluations in the current art, two workflows are often applied to assess a subterranean storage capacity: i) regional static storage capacity evaluation; and ii) injection site specific dynamic storage capacity estimates or simulations, typically performed with reservoir models using detailed stratigraphic information and seismic interpretations calibrated and quality controlled with wells and their associated data. Due to the data and time needed to build and run dynamic reservoir models in the current art, dynamic storage estimates are typically not available during the intake screening phase. Consequently, predictions during the intake screening phase in the current art rely mainly on static storage estimates, which tend to be inaccurate due to reliance on poorly defined storage efficiency (SE) numbers, as defined by the following equation:Equation 1: M CO2 = BRV * N:G * Porosity * p CO2* SE where BRV is bulk rock volume (in cubic meters), N:G is a net to gross ratio (unitless), porosity is given as a percentage, and p is density (in kilograms per cubic meters).

[0144] There are empirically derived SE numbers available based on first principles and concepts. However, such estimates lack any location specific geological control and are often tied only to the depositional environment of the main stratigraphic storage interval. To better understand how much of the static rock volume can be accessed for storing CO2, it is important to understand an accurate representation of the SE number. IP modeling is a technology that can provide a solution since IP modeling only needs seismic data as an input. In addition, the IP simulations can be run relatively fast, and IP modeling can rank injection sites relative to each other based on plume location, shape, and / or size within the early screening phase.

[0145] Seismic data, either as 2D or 3D, is frequently available during the early screening phase, and the seismic data can lead to uncalibrated seismic rock properties in its simplest form of RMS amplitudes. According to certain example embodiments, combining seismic attributes with a fast migration algorithm like IP can be applied to large seismic data sets to perform uncalibrated CO2 migration evaluations in a relatively short period of time. In addition, IP models are relatively easy to build and / or revise. Further, IP models shorten thetechnical evaluation time from weeks (for reservoir models) to days. This makes IP modeling an appealing screening method for use with example embodiments.

[0146] Normally, the fact that pressure is not used as a driving force for CCS evaluation would be considered a drawback. However, according to certain example embodiments, after analyzing the migration domain of CO2 plumes in detail, carbon dioxide plumes are accurately represented by IP modeling, as confirmed after injection has been completed. An example of this is shown in FIG. 8 to better understand the migration of injected CO2 within the subsurface by looking at a simplified cross section. Specifically, FIG. 8 shows a graph 897 of a cross section of carbon dioxide plume in a subterranean formation using Darcy and IP simulations according to certain example embodiments.

[0147] Referring to the description above with respect to FIGS. 1 through 7, the graph 897 of FIG. 8 shows a near-field on the left of the graph 897 and a far-field on the right of the graph 897. Close to the wellbore in the near-field, the CO2 is pushed into the formation with much higher pressure compared to the formation pressure causing a strong pressure gradient around the well, which makes the CO2 flow horizontally along the pressure gradient from the well into the formation. This movement is dominated by viscous forces, which can be represented by Darcy flow. Hence, at the well the CO2 movement is substantially 100% pressure driven.

[0148] In contrast to that the far-field near the plume edge (and especially under equilibrated circumstances after injection has stopped), the CO2 no longer moves along a pressure-gradient. Instead, the CO2 moves substantially vertically following buoyancy unless there is a capillary barrier diverting or stopping the flow. Hence, at the plume edge the CO2 movement is substantially 100% buoyancy driven. Somewhere in between the far-field and the near-field, the dominating force switches from pressure dominated to capillary dominated. The transition from pressure to buoyancy may occur substantially where the CO2 plume hull shows an angle of 45 degree.

[0149] How far away from the wells this transition zone is located can be difficult to assess because reservoirs are not homogenous, and mapping the exact shape of the CO2 plumes in the subsurface can be difficult. According to certain example embodiments, gas fields may be evaluated with respect to well spacing. The evaluated gas field developments may be locatedin shallow water and have clastic reservoirs of Cenozoic ages. Research and data analysis reveals that 1 well per approximately 200 acres is required for those gas field developments on average. In other words, a new gas producer may be placed every 800 meters to 1000 meters for a typical gas development. That means that by placing wellbores (e.g., wellbores 111) 425 meters to 500 meters away from each other, the movement of the gas is not much influenced by the well-induced pressure drop (at least on a production scale, which is similar to the injection time scale of a CCS well).

[0150] Gas (e.g., dry gas) can be used as analog for migration purposes because, at least in part, gas and supercritical CO2 have similar leakage behavior in the subsurface. On the one hand, methane has a much lower density compared to supercritical CO2 and therefore has less buoyancy force compared to gas. An example of this is shown in FIG. 9, which shows a graph 997 of buoyancy pressure versus depth of various fluids in a subterranean formation according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 8, the graph 997 of FIG. 9 shows plots of oil, carbon dioxide, and gas (e.g., methane) in terms of buoyancy pressure versus depth from contact. In this way, the buoyance pressure represents an upward force applied in the subsurface.

[0151] On the other hand, the capillary entry pressure for supercritical CO2 -water system is more like an oil-water system and requires less capillary pressure to break through in comparison to the gas-water system. This may be counter-intuitive because most of the time, only the buoyancy force (and not the capillary pressure) is assessed. Combining both the buoyancy and capillary effects (including interfacial tension (IFT)) reveals that supercritical CO2 leaks like gas, but leaks very different to oil. An example of this is shown in FIG. 10, which shows a graph 1097 of capillary pressure (in MPa) versus IFT (in nM / m) of various fluids in a subterranean formation according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 9, the graph 1097 of FIG. 10 shows plots of oil, carbon dioxide, and methane in terms of capillary pressure versus IFT. In this way, the capillary pressure represents a counter force applied in the subsurface.

[0152] FIG. 11 shows a graph 1197 of column height of various fluids in a subterranean formation according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 10, the graph 1197 of FIG. 11 shows column height (in meters) forgas, carbon dioxide, and oil. Specifically, the graph 1197, when combined with the graph 997 of FIG. 9 and the graph 1097 of FIG. 10, shows why supercritical CO2 leaks like gas, and why the column heights of supercritical CO2 and gas are similar to each other. The following values were used for Washbum’s equation (known to those of ordinary skill in the art and shown in FIG. 11): IFT (N / m) = y = 0.056 (gas), 0.026 (CO2), 0.026 (oil); Simplistic contact angle (°) = 0 = 45 (gas, CO2, oil); Radii difference between reservoir and seal (m) = X = 9.9E=6; density difference (kg / m3) = pw-pf = 790 (gas), 190 (CO2), 190 (oil); gravitational acceleration (m / s2) = g = 9.8.

[0153] Another approach to determine the dominant flow domain is based on capillary numbers and viscosity ratios. Reservoir model outputs from CO2 plumes may be evaluated in terms of a capillary number (Ca = CO2 dynamic viscosity* flow rate / IFT) and the viscosity ratio (M = CO2 dynamic viscosity / H2O dynamic viscosity). Plume-wide flow rates from reservoir models using Darcy migration may be determined by measuring the distance from the wellbore to the edge over the injection years. Viscosities are determined using pressure and temperature information in combination with one or more algorithms 233. IFTs may be approximated based on the graphs shown in FIGS. 12 through 14.

[0154] An example of results of Darcy simulations are shown in FIG. 12. Specifically, FIG. 12 shows a graph 1297 using capillary numbers and viscosity ratios to characterize carbon dioxide plume regimes according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 11, the graph 1297 of FIG. 12 shows that CO2 plumes are dominated by capillary and buoyancy forces. The graph 1297 of FIG. 12 also shows the conditions for typical oil producer wells and the near well conditions of CO2 injection wells for comparison. CO2 plumes (far field of CO2 injection wells) clearly plot in the area, which is capillary dominated. The graph 1297 also shows that the injection and producer wells do not plot in an area of 100% viscous domain, but rather in between flow domains.

[0155] Model validation confirms that IP simulation is not as effective at describing the flow close to the injection well (i.e., near field) compared to Darcy reservoir simulations. Model validation also confirms that IP simulation is extremely effective at describing the flow further from the injection well (i.e., far field). The dominant flow regime across the entire plume is capillary and buoyancy driven. Large parts of the plume follow flow physics, whichis similar to the natural flow and filling process of oil and gas fields. For example, as discussed above with respect to FIG. 8, the transition between viscous and capillary forces may occur some distance (e.g., around 500 meters) away from injection wells. This causes a Darcy footprint of about 0.8 km2per injection well. Comparing this number per well to the actual size of CO2 plumes concludes that only around 15% of the areal footprint of CO2 plumes are dominated by viscous forces.

[0156] Returning to the method captured in the flowchart 498 of FIG. 4, in step 471, the uncalibrated column heights (sometimes more simply referred to as uncalibrated column heights herein) are calibrated. Put another way, the uncalibrated column heights that resulted from the initial run of the IP model are adjusted and / or scaled. Once the IP modeled uncalibrated column heights have been calibrated to make them generally as large as the mean exploration gas column heights, the initial CO2 plume can be called “calibrated” and should reflect realistic spatial extends in vertical and horizontal directions. Calibrating the uncalibrated column heights may be include adjusting the values of the uncalibrated column heights and / or applying a scaling factor (part of the stored data 234) to the values of the un calibrated column heights. As defined herein, gas column heights found during exploration (or similar terms describing gas column heights or exploration gas column heights) mean column heights that are found during hydrocarbon exploration (e.g., using the wellbores 111).

[0157] In certain example embodiments, the calibration (e.g., application of a scaling factor) may be based on gas column heights found during exploration. For example, a scaling factor may be applied to all gas column heights in such a way that the p90 (or some other percentage along the probability scale) plume height (the gas column heights before scaling or calibration of the uncalibrated column heights) matches the p50 (or some other percentage along the probability scale) exploration gas column heights.

[0158] The uncalibrated column heights may be calibrated by or using the calibration module 245 of a controller 104 of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, and / or stored data 234. The scaling factor (e.g., the p (probability) values applied to the uncalibrated column heights and / or to the gas column heights during exploration may be determined by the calibration module 245 using one or more protocols 232, one or more algorithms 233, and / orstored data 234 (e.g., scaling factors). In addition, or in the alternative, any shifting of the uncalibrated column heights relative to the gas column heights from exploration may be determined and / or executed by the calibration module 245 using one or more protocols 232, one or more algorithms 233, and / or stored data 234.

[0159] If none of the modelled sub-accumulations of the CO2 plume (in other words, if the plO (or some other percentage along the probability scale of all uncalibrated column heights) uncalibrated column height of the simulated CO2 plume) is lower compared to observed average (e.g., p50) of the gas / methane column heights from hydrocarbon exploration, the uncalibrated column heights that result from seismic to uncalibrated rock translation needs calibration. A rationale for this is that exploration gas column heights are typically available only for large columns, and so data from exploration is heavily biased towards large column heights.

[0160] In certain example embodiments, rock properties may be defined and populated across a basin model to deploy IP as a migration method. Lateral and vertical sand-shale presence and associated rock properties can be defined using seismic data and associated attributes (e.g., RMS amplitude maps, sweetness maps). An example of RMS amplitude maps may be found, for example, in DeAngelo et al., “A seismic-based CCh-sequestration regional assessment of Miocene section, northern Gulf of Mexico, Texas and Louisiana”, International Journal of Greenhouse Gas Control, v. 81, pp. 29-37 (2019). An example of sweetness maps may be found, for example, in Hart, “Channel detection in 3-D seismic data using sweetness”, AAPG Bulletin, v.92, pp. 733-742 (2008). In addition, or in the alternative, example embodiments may use high-resolution computational stratigraphy models to populate rock properties along 2D and / or 3D seismic, as described, for example, in Amaru et al., “Integration of computational stratigraphy models and seismic data for subsurface characterization”, The Leading Edge, Volume 36, Issue 11, Nov, pp: 874-960 (2017).

[0161] Heterogenous rocks trap more CO2 due to many small stratigraphic and structural traps, and also due to tortuous migration pathways causing high amounts of capillary trapped CO2. This heterogeneity can be represented by seismic at the right scale. This complex and heterogenous trapping may be referred to as “composite confining system” (as described, for example, in Bump et al., “Fetch-trap Pairs: Exploring definition of carbon storage prospectsto increase capacity and flexibility in areas with competing uses”, International Journal of Greenhouse Gas Control, v. 122, p. 103817 (2023)) and emphasizes that losses along the migration pathways are so high in heterogenous rocks that eventually the plume will stop spreading and substantially all CO2 is permanently and safely trapped in the subsurface.

[0162] Because CCS projects target subsurface depth intervals that are often deeper than 800 meters to 1000 meters, shales are typically harder than sands unless the sands have been affected by diagenesis, which most often occurs in clastic sediments at greater depths. These greater depths are typically avoided in CCS for geological and economic reasons, as outlined above. As a result, seismic to rock property conversion for CCS projects is simpler compared to many oil and gas projects. However, a calibration of rock properties may be necessary, even in the absence of nearby wells.

[0163] CO2 plumes resulting from IP simulations consist of hundreds or thousands of small accumulations, which are controlled by the sealing potential and their spatial distribution of the rocks. There is not one single seal but rather multiple confining elements in saline aquifers that traps the CO2. The composite sealing potential of the rocks can be determined based on one or more of a number of factors, including but not limited to regionally known gas column heights and by looking at gas column heights from analog rocks. At least a few single columns within the modelled CO2 plume may reflect the field observed average gas column height. If not, rock properties (e.g., capillary entry pressure and / or porosity) may be scaled accordingly to adjust the composite sealing potential.

[0164] The seismic to uncalibrated rock property translation or scale rock properties are adjusted until the plO (or some other percentage along the probability scale) column height from the modelled CO2 plume matches the mean (e.g., P50) gas column height from exploration. This can be achieved by applying a scaling factor to the capillary entry pressures and / or porosities of all lithologies to adjust the overall composite sealing potential so that gas column heights from exploration are considered as are general porosity ranges from analogs for the same environments of depositions of the main injection interval.

[0165] In certain example embodiments, the calibration module 245 of the controller 104 calibrates the uncalibrated plume using a scaling factor to generate calibrated column heights, where the scaling factor is based on a comparison of a p50+ (i.e., p50 or greater) of theuncalibrated column heights and gas column heights found during exploration. In such cases, calibrating the uncalibrated column heights is based on an average (e.g., p50) of the gas column heights found during exploration.

[0166] In step 472, a final (calibrated) 3D plume prediction is generated. The calibrated 3D plume prediction may be generated using the processed data discussed above in the prior steps of this method, including the calibrated column heights. The calibrated 3D plume prediction may be generated by or using the recommendation module 242 of a controller 104 of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, and / or stored data 234.

[0167] IP simulation results show the equilibrated footprint of the CO2 plume for the given mass injected. As a result, it is possible to mimic the timing effects of a growing plume by injecting in multiple steps. IP results inform about plume location, shape, and size for as many injection wells as needed. The evaluation is most comprehensive and reliable when using 3D seismic data, but IP simulations can also be performed on multiple 2D lines for injection site characterization purposes when using instantaneous attributes like amplitude, envelope, and / or coherence to link to rock heterogeneity and sand shale distributions, as described, for example, in Ha et al., “Pitfalls and implementation of data conditioning, attribute analysis, and self-organizing maps to 2D data: Application to the Exmouth Plateau, North Carnarvon Basin, Australia”, Interpretation Vol. 7, Issue 3, August 2019.

[0168] In addition to the calibrated CO2 plume geometrical evaluation, example embodiments may be used to assess the storage density either in reference to an area or rock volume (e.g., tons per km2or km3). Furthermore, IP results may inform about the dominant trapping mechanism (e.g., structural, stratigraphic, residual, dissolution) and the percentage contribution. Depending on the tools used for the IP analysis, the output may be more limited or more comprehensive. Some tools may be useful in providing spatially referenced meta-data such as column height, CO2 saturation, and rock properties. Other tools may be more sophisticated in changing rock properties globally for a fast and efficient uncertainty analysis. There may also be a difference in considering temperature, pressure, and the impact onto the CO2 while applying IP simulation.

[0169] In step 473, the calibrated 3D plume prediction is presented. The calibrated 3Dplume prediction may be presented to one or more users 159, including any associated user systems 155. The calibrated 3D plume prediction may be presented by or using the recommendation module 242 of a controller 104 of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, stored data 234, the communication links 105, the communication module 207 of the controller 104, and / or the application interface 226 of the controller 104. The calibrated 3D plume prediction may be presented in one or more of any number of suitable formats, including but not limited to a display on a screen, a physical model using a 3D printing process, an attachment in an email, and a printout. The format of the calibrated 3D plume prediction may be selected by a user 159 (including an associated user system 155), part of the user preferences (among the stored data 234), a default setting, and / or based on some other factor.

[0170] In some cases, the calibrated 3D plume prediction may be presented with details as to how an injection operation may be performed to achieve the CCS objective. Such details may include, but are not limited to, pressures, wellbores 111 used, how each wellbore I l l is used, timing of injections, flow rates, chemical composition of the fluid (e.g., carbon dioxide), and temperatures. In some cases, the calibrated 3D plume prediction may take an active part (e.g., provide real time advise, monitor and / or control equipment (e.g., the carbon preparation apparatus 193, the carbon injection equipment 194), receive and / or process measurements made by sensor devices 160) in field operations to achieve the CCS objective.

[0171] In step 474, the algorithms 233 are assessed based on injection operations. Specifically, the algorithms 233 used to directly or indirectly generate the calibrated 3D plume prediction are assessed by comparing actual data (e.g., measurements from sensor devices 160 during field operations to achieve the CCS objective) with expected results. The algorithms 233 may be assessed by or using the control engine 206 of a controller 104 of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, stored data 234, the communication links 105, the communication module 207 of the controller 104, and / or the application interface 226 of the controller 104. In assessing the algorithms 233 used to generate the final 3D plume prediction, other algorithms 233 of the same controller 104 or a different controller 104 of the system 190 for CCS site screening and characterization.

[0172] In step 475, a determination is made as to whether one or more of the algorithms 233 should be modified. The determination may be made by or using the control engine 206 of a controller 104 of the example system 190 for CCS site screening and characterization using one or more protocols 232, one or more algorithms 233, and / or stored data 234. As an example, a determination may be made to modify the IP model based on stratigraphic data available from a stratigraphic well that is drilled before the CCS operation.

[0173] As another example, a determination may be made to modify the IP model based on a difference between expected results and the actual data during the CCS operation, whether for the same site and / or using data from a CCS operation at another site that has some formation similarities. In any case, if one or more of the algorithms are modified, the modified algorithms may be used to generate a calibrated final plume prediction. In such a case, a revised calibrated plume prediction may be generated in real time relative to assessing / modifying the IP model. If one or more algorithms should be modified, the control engine 206 of a controller 104 of the example system 190 for CCS site screening and characterization makes the appropriate modification, and the process reverts to step 472. In such a case, step 472, step 473, and step 474 are performed using the modified algorithms 233. If none of the algorithms 233 should be modified, the process proceeds to the END step.

[0174] The method captured by the flowchart 498 of FIG. 4 has been applied to multiple locations from different basins and different saline aquifers for which reservoir models are available. This provides the opportunity to directly compare IP simulations to Darcy results. The locations have varying characteristics, from Cenozoic to Mesozoic reservoir ages, from small to very large injection masses, form very flat to strongly tilted monoclines, and from single injection to multi -injection well development scenarios. Table 1 summarizes the key outcomes from the case studies, which are discussed in detail below. In Table 1, the gross plume area describes the hull area of the entire plume, including unsaturated areas inside the plume.TABLE 1

[0175] The following case studies / examples are intended to be non-limiting and demonstrate, among other things, the utility of the use of IP for CCS site screening and characterization in accordance with the workflows described herein.

[0176] Case Study 1 :

[0177] In the public domain, Sleipner is the best studied CO2 injection site for saline aquifers. A fit for purpose IP evaluation was performed to compare internal results to the publicly available results and determine the validity of the workflow according to example embodiments. The main lessons learned is that IP as a migration analysis method provides reasonable estimates for the CO2 plume location, shape, and size. Especially during the screening phase when relatively little data is available, and when no detailed stratigraphic model exists. IP modeling can be even more accurate compared to traditional reservoir model outputs during these early stages. Put another way, the seismic data and other applicable data used to generate the final plume prediction is insufficient to generate an accurate traditional reservoir model.

[0178] FIG. 13 shows the final 3D plume prediction according to certain example embodiments by plotting uncalibrated Sleipner plumes representing the early screening phase in comparison to seismic outlines. Specifically, FIG. 13 shows a graph 1397 comparing carbon dioxide plumes based on various simulations according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 12, an IP model was designed to be tested at Sleipner with an injection of about 12 million Tons of CO2 into one wellrepresenting the year 2010 in terms of seismic plume outlines. The reservoir has a temperature of roughly 40 °C and a pressure of 10 MPa.

[0179] As shown in the graph 1397 of FIG. 13, the IP simulation reveals that the plume expands 3000 meters away from the injection well. The area covered by a gross plume is around 3.5 km2, and the area covered by a net plume is around 2.7 km2. The area of the gross plume represents a general hull of the entire plume, whereas the area of the net plume has been discounted for areas within the plume where CO2 could not saturate the rock. The net IP plume area in the graph 1397 is similar to the Darcy plume area and covers approximately 2.3 km2for an uncalibrated reservoir model mimicking a data poor environment during the early screening phase.

[0180] For validation of the IP method used herein, the seismic derived plume outline for Sleipner is also shown in the graph 1397 ofFIG. 13. The IP storage density from this quick test is around 3.5 MT / km2, whereas the uncalibrated Darcy storage density is around 5.2 MT / km2due to a much smaller footprint of the Darcy plume. The greatest difference between the uncalibrated Darcy and the IP plumes is the maximum plume extend and actual shape. The length of the IP plume (about 4.5 km long) is more than twice as long as the Darcy plume (about 2 km). Also, the IP plume is more elongated compared to the more circular Darcy plume. When comparing IP and Darcy results to the seismic data, the IP simulation represents the CO2 plume shape and extend more accurately in the situation of an uncalibrated Darcy model.

[0181] Case Study 2:

[0182] This case study evaluates the CO2 plume size for a two well injection concept in a shallow water Cenozoic era formation with siliciclastic settings as part of a saline aquifer system. The evaluation tests 60 million Tons (MT) of CO2 injected across two wells over 30 years into a reservoir at roughly 75 °C temperature and 25 MPa pressure. The reservoir is gently dipping with some characteristic internal facies changes identified on RMS maps expressed as Vshale proportions.

[0183] FIG. 14 shows graphs of column height probability and spatial distribution of IP accumulations for CO2 plume for this case study according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 13, the rock types and properties used for the IP evaluation are calibrated towards a regional average gas columnheight of 27 meters, representing the plO in the IP modeled CO2 plume. The net area covered by the IP plume is similar to the gross Darcy plume (around 16 km2). However, the gross IP plume is around 1.5 times larger compared to the Dracy plume area. Following the gross IP plume, the storage density is around 2.5 MT / km2, whereas the Darcy and net IP plumes indicate a storage density of almost 4 MT / km2.

[0184] The Darcy plume is more circular shaped, whereas the IP plume shows more elongation. The equilibrated CO2 plume evaluated with IP migrated a maximum distance of about 8000 meters away from the injection wells, which is almost twice as long as the maximum distance evaluated by the reservoir model using Darcy. These observations are very similar to what was observed in Case Study 1. The entire evaluation was performed in about 70 hrs. RMS maps may identify sand fairways, which may impact model results. Limited column height information from exploration is sufficient to constrain rock properties locally.

[0185] Case Study 3:

[0186] This case study evaluates the potential CO2 plume for a multi-well injection development into Mesozoic rocks. The 7 wells together are projected to inject about 300 MT over 30 years into a reservoir with an average temperature of about 97° C and an average virgin pressure of about 25 MPa. FIG. 15 shows a graph 1597 of probabilities of carbon dioxide plumes for case study 3 according to certain example embodiments. FIG. 16 shows a graph 1697 of the 7 well development of case study 3 with a 300 metric ton carbon injection according to certain example embodiments.

[0187] In the graph 1597 of FIG. 15, a plot of calibrated column heights in terms of column height (in meters) along a linear scale versus probability (as a percentage) along a logarithmic scale. The graph 1597 also shows that the average (e.g., p50) of gas column heights from exploration fall toward the high end of the distribution of the calibrated column heights. This concept is shown in different terms in the graphs of FIGS. 22A through 24 below.

[0188] Referring to the description above with respect to FIGS. 1 through 14, several reservoir model outputs as part of an uncertainty study were available, providing a range to which the IP model outputs are compared. In terms of plume size and maximum lateral migration distance, IP models show different trends relative to the Darcy models. The IP models use facies from seismic data, with these data indicating high reservoir heterogeneityaround the injection wells. Further away and updip from the injection wells, the reservoir becomes cleaner and more homogenous, impacting the plume size and shape.

[0189] As long as the CO2 stays close to the wells within the heterogenous reservoir, storage densities are high, and the footprint is relatively small compared to the reservoir Darcy models. However, once the IP plume reaches the clean sands updip, CO2 spreads exponentially into much larger plumes compared to the Darcy results. The reservoir model, which uses Darcy, does not consider lateral trends and changes of rock properties. Instead, the reservoir model uses multi point statistics to populate lithologies across the entire area of interest in the same way, causing no large-scale lithology trends or changes.

[0190] The IP base case evaluates storage densities of around 2.7 MT / km2with a maximum migration distance of around 23 km after 30 years of injection and a gross plume size of 110 km2. The maximum migration distance in the Darcy models ranges between about 13 km and 65 km and with a base case of around 30 km and an average storage density of roughly 2.2 MT / km2, depending on the rock properties and boundary conditions. The total time invested to build, test, and evaluate the IP model is about 60 hours. The mean exploration gas column height, which is used as a p05 calibration point for the plume column distribution, is around 100 meters. From a geological perspective, the updip reservoir trapping potential is significantly limited compared to the downdip area due to changes in rock properties and overall stratigraphic dip. Gas column heights from exploration were useful to constrain the rock properties and hence the CO2 plume.

[0191] Case Study 4:

[0192] This case study risks and ranks different injection sites within the same basin relative to each other based on the location (e.g., encountering faults, lease boundaries, proximity to existing wells etc.), plume extent (e.g., long-distance CO2 migration less preferred compared to short distance migration), and storage density of the CO2 plumes (e.g., mass per footprint). To get an understanding of the most sensitive parameters, rock properties are scaled, and migration is tested almost instantaneously using IP models.

[0193] For this case study, the capillary entry pressures and the porosities for all lithologies are scaled independently of each other. Scaling the capillary entry pressure is similar to changing the Vshale. Scaling the porosity emphasizes or de-emphasizes the structuraltrapping component. Thus, scaling porosity down provides less volume available for CO2 storage, causing higher column heights and more seals to be breached, which de-emphasizes the capillary and stratigraphic trapping and emphasizes the structural trapping.

[0194] FIGS. 17A through 20B the column height distribution and some anonymized and conceptualized CO2 plumes with their characteristic shapes and extensions for different scaling factor pairs for porosity and capillary entry pressure for four scenarios in this case study. Specifically, FIGS. 17A and 17B show a graph 1797 of column height distribution and a graph 1796 of carbon dioxide plume outlines, respectively, for one scenario with a set of porosity and capillary scalars according to certain example embodiments. FIGS. 18A and 18B show a graph 1897 of column height distribution and a graph 1896 of carbon dioxide plume outlines, respectively, for another scenario with a set of porosity and capillary scalars according to certain example embodiments. FIGS. 19A and 19B show a graph 1997 of column height distribution and a graph 1996 of carbon dioxide plume outlines, respectively, for yet another scenario with a set of porosity and capillary scalars according to certain example embodiments. FIGS. 20A and 20B show a graph 2097 of column height distribution and a graph 2096 of carbon dioxide plume outlines, respectively, for still another scenario with a set of porosity and capillary scalars according to certain example embodiments.

[0195] Referring to the description above with respect to FIGS. 1 through 16, scenario A (shown in FIGS. 17A and 17B) and scenario D (shown in FIGS. 20A and 20B) use lower porosity scalers and higher capillary scalers relative to each other, which emphasizes the capillary entry pressure and facies trapping. Because there are no dramatic facies boundaries, the resulting CO2 plumes are located around the injection wells evenly. Scenario B (shown in FIGS. 18A and 18B) and scenario C (shown in FIGS. 19A and 19B) use higher porosity scalers, which emphasize the structural trapping. The results show that calibrating both composite sealing potential / column heights and composite porosity based on analogs for the environment of deposition is important to define the CO2 plume outlines.

[0196] FIG. 21 shows a workflow diagram 2197 of an embodiment of the method of FIG. 4 according to certain example embodiments. Referring to the description above with respect to FIGS. 1 through 20B, the workflow diagram 2197 of FIG. 21 is for basin modelling using IP methods during the intake screening phase of CCS projects before reservoir modelsare available to evaluate plume size and shape. Step 1 to 5 lead through a workflow of building and calibrating CO2 plumes. Specifically, the workflow diagram 2197 of FIG. 21 shows an example of how to build and calibrate an IP model for a CO2 plume during the early screening phase before detailed geological information is available. Put another way, the workflow diagram 2197 of FIG. 21 describes the full IP workflow, including calibration for CCS evaluations during the early intake screening phase.

[0197] A basic characteristic of heterogenous rocks is that they trap more hydrocarbons or more CO2 in comparison to homogenous rocks due to many small stratigraphic and structural traps, and due to tortuous migration pathways causing high amounts of capillary trapped CO2. This heterogeneity of rocks can be represented by seismic data at the right scale. This complex and heterogenous trapping is called “composite confining system” and emphasizes the fact that losses along the migration pathways are so high in heterogenous rocks that eventually the plume will stop spreading and all CO2 is permanently and safely trapped in the subsurface, as described, for example, in Bump et al., as discussed above.

[0198] As in nature, modeled CO2 plumes from IP simulations consist of hundreds or even thousands of accumulations. Most of these accumulations are small and only a few are large. The larger accumulations are often controlled by the sealing potential of more than one grid cell. As a result, rather than a single capillary entry pressure, multiple confining elements often contribute to trapping a CO2 accumulation / plume. Determining this sealing potential is important to predict reasonable CO2 plume locations, shapes, and sizes using IP models.

[0199] In order to determine this composite sealing potential of the storage rocks for CO2 plume modeling, regionally known or analog gas column heights from oil and gas exploration may be used in certain example embodiments. At least a few single columns within the simulated CO2 plume should reflect the field observed average gas column height. This is because exploration gas column heights are typically available only for large columns, and hence data from exploration is heavily biased towards large column heights. If all simulated CO2 accumulations have smaller column heights than the average gas column heights from the same reservoir or an analogous reservoir, rock properties (e.g., capillary entry pressure, porosity) should be scaled up accordingly to adjust the composite sealing potential.

[0200] In step 1 of the workflow diagram 2197, geometry and RMS maps are combined.Top seal, top reservoir, and / or base (bottom) reservoir structural depth or time maps may be required, including a general understanding of how deep (e.g., depth, location) the top seal is buried and the depth of the top and bottom of the reservoir (also called the depth or location of the reservoir herein) under the top seal. Such maps define two layers: The seal and the reservoir / inj ection target. Both layers can be subdivided into multiple sublayers, which can be populated with seismic data.

[0201] In step 2 of the workflow diagram 2197, seismic to uncalibrated rock property translations occur. Specifically, seismic data is translated into uncalibrated rock properties according to corporate standard schemes used to convert a particular seismic signal into a standard (e.g., Vshale rock property). As discussed above, this translation does not have to be perfect or calibrated to particular wells because of step 3. Put another way, the translation may be generic and / or uncalibrated (e.g., using an uncalibrated method). Recognizing that there are a number of different ways to concert seismic into rock properties, the goal in this step is to capture the information (e.g., the end-members of shale, the sand) through a translation at this stage of the process. The calibration may be performed at a later time in the process.

[0202] In step 3 of the workflow diagram 2197, an uncalibrated column analysis is performed using an IP model. For example, CO2 is virtually injected into the IP model (e.g., geometry populated with seismic derived uncalibrated rock properties), and the uncalibrated column heights of all sub-accumulations are measured and evaluated probabilistically. If the output of the IP model produces no modelled sub-accumulations of the CO2 plume (in other words, if the plO (or some other percentage along the probability distribution) column height of the simulated uncalibrated CO2 gas columns is lower compared to the observed average (e.g., p50) gas / methane column heights from hydrocarbon exploration, the uncalibrated column heights that result from the seismic to uncalibrated rock translation needs adjustment and / or calibration. Gas column heights from exploration may be used for these adjustment and / or calibration purposes because the subsurface leakage behavior of supercritical CO2 and gas are substantially similar.

[0203] In step 4 of the workflow diagram 2197, the uncalibrated column heights are calibrated with known the gas column heights of methane from exploration. Calibration may involve shifting the uncalibrated column heights and / or applying a scaling factor to theuncalibrated column heights. For example, the uncalibrated column heights that result from seismic to uncalibrated rock property translation or scale rock properties are adjusted until the plO column height from the modelled and uncalibrated CO2 column heights matches the mean (e.g., P50) gas column height from exploration. This can be achieved by simply applying a scaling factor to the capillary entry pressures of all lithologies to adjust the overall composite sealing potential.

[0204] Graphical examples of calibration of the uncalibrated column heights are shown with respect to FIGS. 22A through 24 according to certain example embodiments. Referring to the description with respect to FIGS. 1 through 21, all of the graphs of FIG. 22A through 24B show frequency (in terms of the number of occurrences) along the vertical axis and column height (in meters) along the horizontal axis. In the graphs of FIG. 22A and 22B, plot 2282 represents the distribution of uncalibrated column heights, and plot 2283 represents the distribution of gas (e.g., methane) column heights found during exploration.

[0205] In FIG. 22A, there is little to no overlap between plot 2282 and plot 2283, where most if not all of the column heights of plot 2282 are lower than the lowest of the column heights of plot 2283. Part of the calibration process in such cases according to certain example embodiments is to shift (e.g., by the calibration module 245 of the controller 104) the entire plot 2282 to the right (thus increasing all of the values of the uncalibrated column heights by uniform increments) until there is some amount of overlap (e.g., p90 for plot 2282 aligns with p50 for plot 2283) between plot 2282 and plot 2283. Such a shift may be based on one or more of a number of factors. For example, such a shift may be based on aligning a column height of 60 meters for both plot 2282 and plot 2283. The result of this shift is shown in FIG. 22B.

[0206] In FIG. 23 A, there is little to no overlap between plot 2382 (representing the distribution of uncalibrated column heights) and plot 2383 (representing the distribution of gas column heights found during exploration). In this case, most if not all of the column heights of plot 2382 are higher than the highest of the column heights of plot 2383. Part of the calibration process in such cases according to certain example embodiments is to shift (e.g., by the calibration module 245 of the controller 104) the entire plot 2382 to the left (thus decreasing all of the values of the uncalibrated column heights by uniform increments) until there is some amount of overlap (e g., p25 for plot 2382 aligns with p55 for plot 2383) between plot 2382 andplot 2383. Such a shift may be based on one or more of a number of factors. For example, such a shift may be based on aligning a column height of 25 meters for both plot 2382 and plot 2383. The result of this shift is shown in FIG. 23B.

[0207] In FIG. 24, there is overlap between plot 2482 (representing the distribution of uncalibrated column heights) and plot 2483 (representing the distribution of gas column heights found during exploration). In this case, dashed vertical line designates with the approximate p50 of plot 2483, which coincides with some column height (e.g., 50 meters). The vertical line also designates the approximate p90 of plot 2482, which coincides with the same column height (in this example, 50 meters). When plot 2492 and plot 2483 are aligned in this way, a scaling factor may be applied (e.g., by the calibration module 245 of the controller 104) to the uncalibrated column heights to generate calibrated column heights.

[0208] In step 5 of the workflow diagram 2197, a final (calibrated) 3D plume prediction is generated using the IP model and the calibrated gas columns. Once the IP model column heights have been calibrated and at least a few columns are as large as the mean exploration gas column heights, the simulated CO2 gas columns can be called “calibrated”, and the resulting final 3D plume prediction should reflect realistic spatial extends in vertical and horizontal directions.

[0209] Example embodiments use seismic and IP modeling to provide fast CO2 plume simulations when there is not much data aside from seismic. This is important for early feasibility studies. In addition, or in the alternative, example embodiments use gas column heights from exploration to calibrate CO2 plume simulations when there is not much other data. This may also be important for early feasibility studies. In addition, or in the alternative, example embodiments use calibrated CO2 plume simulation outputs, using seismic as input and IP as the method, to get site specific storage efficiency numbers, which are important for early economic forecasts.

[0210] The relative risk assessment based on IP modeling for each injection site substantially matches the geological risk assessment made manually by the geologists using structure, reservoir, and seal. The relative risk assessment based on IP modeling for each injection site substantially matches the assessment based on the reservoir model. All three methods identify the same location as the lowest risk injection location with highest storagepotential.

[0211] Technical effects of the present methods that use IP include but are not limited to extremely fast simulations, and results can be evaluated after seconds of starting the simulation. This speed provides the opportunity to perform sensitivity analysis on the maximum extend of the CO2 plume for any of a number of different factors (e.g., different injection amounts, different seismic attributes, different rock property translations and scaling, different fault interpretations). Similar to hydrocarbon exploration, where basin models are used to support the risk assessment and ranking of individual prospects to build a portfolio, the present methods of using IP can be used in carbon sequestration to assess risk and rank individual injection sites and to compare them relative to each other within a CCS portfolio. Once a hydrocarbon prospect has been drilled and converted into a discovery, a reservoir model is built to further pursue the opportunity. Similary, in CCS, the reservoir model is built after acreages have been secured around the best ranked injection sites based on IP evaluations.

[0212] The IP methods described herein have the ability to assess risk and rank many potential CCS injection sites quickly before a reservoir model is available. Hence, IP may be used as a screening tool for the early phase of CCS site characterization and can be performed with 3D or multiple 2D models. Using IP simulations as discussed herein results in repaid processing and output, which can be evaluated after seconds of starting the simulation. This speed provides the opportunity to perform sensitivity analysis on the maximum extend of a CO2 plume for different injection amounts, for different seismic attributes and different rock property translations, different analog column heights for calibration, or even for different fault interpretations.

[0213] During the early screening phase of injection sites, IP predictions can be calibrated with regionally known gas column heights. IP predictions are close to what has been observed on seismic, and the plume outlines are consistently larger and more elongated compared to uncalibrated Darcy reservoir models. Example embodiments using the IP method lies in its ability to assess risk and rank many potential CCS injection sites based on plume location shape and size, as well as storage efficiencies, before a calibrated reservoir model is available. Hence, IP modeling is an ideal screening tool for the early phase of CCS site characterization and can be performed with 3D or multiple 2D models.

[0214] In addition to saving time, IP modeling as described herein saves resources, costs, and operational risk. For example, in order to obtain enough data to generate a calibrated Darcy reservoir model from the point in time where the data used to generate an IP prediction is obtained and processed, one or more stratigraphic wells must be drilled into the subterranean formation so that such additional information can be obtained and processed using one or more sensor devices (e.g., sensor devices 160). With example embodiments, the IP analysis is performed using seismic data and any other data that lacks information associated with a stratigraphic well. In some cases, results of the IP model described herein may be used to help inform how subsequent stratigraphic wells may be drilled.

[0215] The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms, and can also be used in any appropriate combination. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.

[0216] Although embodiments described herein are made with reference to example embodiments, it should be appreciated by those skilled in the art that various modifications are well within the scope and spirit of this disclosure. Those skilled in the art will appreciate that the example embodiments described herein are not limited to any specifically discussed application and that the embodiments described herein are illustrative and not restrictive. From the description of the example embodiments, equivalents of the elements shown therein will suggest themselves to those skilled in the art, and ways of constructing other embodiments using the present disclosure will suggest themselves to practitioners of the art. Therefore, the scope of the example embodiments is not limited herein.

Claims

CLAIMSWhat is claimed is:

1. A method for screening and characterizing carbon capture and storage (CCS) sites, the method comprising: combining, by a controller, a plurality of maps, wherein at least one of the plurality of maps is generated using seismic data, and wherein the plurality of maps comprises a location of a reservoir and a top seal; translating, by the controller, the seismic data to uncalibrated rock properties; performing, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model; measuring, by the controller, uncalibrated column heights of subaccumulations within the uncalibrated plume analysis; calibrating, by the controller, the uncalibrated column heights using a scaling factor to generate calibrated column heights, wherein the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration; and generating, by the controller, a calibrated plume prediction based on the calibrated column heights.

2. The method of Claim 1, wherein one of the plurality of maps comprises a fluid density map.

3. The method of Claim 2, further comprising: identifying an injection zone in the subterranean formation using the fluid density map.

4. The method of Claim 3, wherein the injection zone allows a fluid used for injection to be in a supercritical phase.

5. The method of Claim 4, wherein the fluid comprises carbon dioxide.

6. The method of Claim 3, further comprising: identifying an injection location within the injection zone.

7. The method of Claim 6, further comprising: generating a pressure map and a temperature map of the injection location within the injection zone.

8. The method of Claim 7, wherein the plurality of maps further comprises the temperature map and the pressure map.

9. The method of Claim 1, further comprising: presenting the final plume prediction.

10. The method of Claim 1, further comprising: obtaining data associated with a subterranean formation, wherein the data comprises the seismic data, and wherein the data lacks information associated with a stratigraphic well.11 . The method of Claim 1 , wherein calibrating the uncalibrated column heights is based on an average of the gas column heights found during exploration.

12. The method of Claim 1, further comprising: assessing the IP model based on actual data obtained from a CCS operation.

13. The method of Claim 12, further comprising: modifying the IP model based on a difference between expected results and the actual data during the CCS operation.

14. The method of Claim 13, further comprising: generating a calibrated final plume prediction after modifying the IP model.

15. The method of Claim 14, wherein the revised calibrated plume prediction is generated in real time relative to assessing the IP model.

16. The method of Claim 12, further comprising: modifying the IP model based on stratigraphic data available from a stratigraphic well before the CCS operation.

17. The method of Claim 1, wherein the seismic data used to generate the calibratedplume prediction, without additional data from a stratigraphic well, is insufficient to generate an accurate traditional reservoir model.

18. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor, enables the computer processor to: combine, by a controller, a plurality of maps, wherein at least one of the plurality of maps is generated using seismic data, and wherein the plurality of maps comprises a location of a reservoir and a top seal; translate, by the controller, the seismic data to uncalibrated rock properties; perform, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model; measure, by the controller, uncalibrated column heights of subaccumulations within the uncalibrated plume analysis; calibrate, by the controller, the uncalibrated column heights using a scaling factor to generate calibrated column heights, wherein the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration; and generate, by the controller, a calibrated plume prediction based on the calibrated column heights.

19. The non-transitory computer readable medium of Claim 18, wherein combining the plurality of maps comprises: generating a fluid density map; identifying an injection zone in the subterranean formation using the fluid density map; identifying an injection location within the injection zone; generating a pressure map and a temperature map of the injection location within the injection zone; and combining the fluid density map, the pressure map, and the temperature map.

20. A system comprising: a plurality of sensor devices measuring a plurality of parameters associated with a subterranean formation being screened and characterized as a site for carbon capture andstorage (CCS), wherein one of the plurality of sensor devices generates seismic data; and a controller communicably coupled to the plurality of sensor devices, wherein the controller is configured to: combine, by a controller, a plurality of maps, wherein at least one of the plurality of maps is generated using the seismic data, and wherein the plurality of maps comprises location of a reservoir and a top seal; translate, by the controller, the seismic data to uncalibrated rock properties; perform, by the controller, an uncalibrated plume analysis using the uncalibrated rock properties, the plurality of maps, and an invasion percolation (IP) model; measure, by the controller, uncalibrated column heights of sub accumulations within the uncalibrated plume analysis; calibrate, by the controller, the uncalibrated plume using a scaling factor to generate calibrated column heights, wherein the scaling factor is based on a comparison of a p50+ of the uncalibrated column heights and gas column heights found during exploration; and generate, by the controller, a calibrated plume prediction based on the calibrated column heights.

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