Automated workflow to optimize parameters for formation pressure measurements utilizing memoization

Abstract

The disclosed methods include: determining distribution data for a subsurface environment of interest; generating, based on the distribution data, a set of test scenarios; combining, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data; combining, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data; generating, based on the first combination of fluid rate data and fluid volume data, a first pressure curve; generating, based on the second combination of fluid rate data and fluid volume data, a second pressure curve; determining, based on the first pressure curve or the second pressure curve, convergence data; generating, based on the convergence data, optimal data values for configuring energy exploration equipment.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 623,900, filed on Jan. 23, 2024, titled “An Automated Workflow To Optimize Parameters For Formation Pressure Measurements Utilizing Memoization,” which is incorporated herein by reference in its entirety for all purposes.TECHNICAL FIELD

[0002] The present disclosure relates to systems and methods for optimizing subsurface characterizations during energy development.BACKGROUND

[0003] Accurately characterizing subsurface formations such as reservoirs can be challenging considering that subsurface parameters derived from, for example, porosity data and / or fluid permeability data need to be optimally determined and combined to arrive at such characterizations. In particular, because secondary subterranean structures like reservoirs may be part of primary subterranean structures like basins (e.g., a sedimentary basin) it is needful to consider structural relationships between both the primary and secondary subterranean structures based on correctly measured or modeled subsurface parameters.

[0004] Furthermore, because primary subsurface structures such as basins can be further defined by geological features like depressions (e.g., depressions caused by plate tectonic activity, subsidence activity, etc.) which can accumulate, for example, sediments, water, or geological energy systems (e.g., hydrocarbons), it is needful to factor or otherwise optimally account for subsurface parameters for such geological energy systems while considering hydrocarbon source rock data and / or depth data and / or duration of burial data to further inform exploratory considerations for developing the aforementioned primary and / or secondary subsurface structures.SUMMARY

[0005] Disclosed are methods, systems, and computer programs for pressure testing to generate configuration data for energy development. According to an embodiment, a method for pressure testing to generate configuration data for energy development comprises: determining distribution data for a subsurface environment of interest, the distribution data comprising probabilistic data values associated with analyzing the subsurface environment of interest based on one or more subsurface parameters; generating, based on the distribution data, a set of test scenarios for the subsurface environment of interest, the set of test scenarios comprising data settings configured to approximate a likelihood that a first pressure test or a second pressure test is implementable at a random position about the subsurface environment of interest based on the one or more subsurface parameters; combining, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data; combining, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data; generating, based on the first combination of fluid rate data and fluid volume data, a first pressure curve for the subsurface environment of interest; generating, based on the second combination of fluid rate data and fluid volume data, a second pressure curve for the subsurface environment of interest; determining, based on the first pressure curve and the second pressure curve, convergence data indicating a similarity between: a first pressure value comprised in the first pressure curve relative to a formation pressure value associated with the subsurface environment of interest, and a second pressure value comprised in the second pressure curve relative to the formation pressure value associated with the subsurface environment of interest; generating, based on the convergence data relative to the one or more subsurface parameters, optimal data values for energy exploration equipment associated with developing the subsurface environment of interest; and initiating configuring the energy exploration equipment based on the optimal data values.

[0006] In other embodiments, a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features.

[0007] In one embodiment, the set of test scenarios for the subsurface environment of interest comprises a plurality pressure tests for the subsurface area of interest. In addition, the first pressure test and the second pressure test are comprised in the plurality of pressure tests.

[0008] In some implementations, a memoization process is implemented, based on the first pressure curve or the second pressure curve, to exclude the first pressure test or the second pressure test from subsequent pressure test determinations for the subsurface environment of interest relative to other pressure tests comprised in the plurality of pressure tests.

[0009] In addition, the first pressure test and the second pressure test are disentangled based on: a first environmental response associated with the first pressure test being unaffected by the second pressure test; and a second environmental response associated with the second pressure test depending on a starting pressure associated with the second test independent of how the starting pressure is reached.

[0010] In some cases, the first pressure test and the second pressure test are disentangled based on outcome data of the second pressure test depending on a final pressure value associated with the first pressure test and fluid rate data or fluid volume data of the second pressure test.

[0011] Moreover, the subsurface environment of interest comprises a primary subsurface structure having one or more secondary subsurface structures.

[0012] Furthermore, the primary subsurface structure comprises a basin while the one or more secondary subsurface structures comprise a reservoir.

[0013] In some cases, the one or more subsurface parameters comprise: a fluid drawdown volume parameter configured for controlling or indicating a volume of fluid extracted from the subsurface environment of interest; a fluid drawdown rate parameter configured for controlling or indicating a rate at which the volume of fluid is extracted; and a fluid buildup time parameter configured for controlling or indicating a time for fluid build-up during extracting the fluid from the subsurface environment of interest relative to stabilizing a formation fluid pressure associated with the subsurface environment of interest.

[0014] According to one embodiment, the one or more parameters are associated with: an over balance parameter indicating a difference between mud pressure and formation pressure of the subsurface environment of interest; a formation pressure indicating a fluid pressure of a formation under consideration associated with the subsurface environment of interest; a density parameter indicating a bulk density of rock in the formation under consideration associated with the subsurface environment of interest; a porosity parameter indicating a porosity of the rock in the formation under consideration associated with the subsurface environment of interest; a viscosity parameter indicating a viscosity of fluid filling a pore space in the rock in the formation under consideration associated with the subsurface environment of interest; a horizontal permeability parameter indicating a horizontal component of rock permeability; a vertical permeability versus horizontal permeability ratio parameter indicating a ratio between a vertical permeability and the horizontal permeability of the rock in the formation under consideration associated with the subsurface environment of interest; and a mud compressibility permeability parameter indicating a compressibility of mud filtrate associated with the subsurface environment of interest.

[0015] In some cases, the method outlined in above further comprises generating, based on at least the first pressure curve or the second pressure curve, optimization data for improving executing one or more pressure tests associated with the first pressure curve or the second pressure curve.

[0016] Furthermore, the optimization data can be applied to a machine learning engine to control generation of a third pressure curve associated with a resource site similar to, or distinct from the subsurface environment of interest.

[0017] It is appreciated that the subsurface environment of interest is comprised in, or associated with an oil field.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is emphasized that various features may not be drawn to scale and the dimensions of various features may be arbitrarily increased or reduced for clarity of discussion.

[0019] FIG. 1A depicts an exemplary computing or network system within which the disclosed methods, systems, and computer programs can be implemented according with some embodiments of this disclosure.

[0020] FIGS. 1B-1E illustrate exemplary schematic views of a resource site for which gas storage capacities may be determined according to some embodiments of this disclosure.

[0021] FIG. 2 illustrates a first exemplary cross section of a resource site such as an oil field for performing the disclosed methods and systems.

[0022] FIG. 3 illustrates a second exemplary cross section of a resource site for performing production operations in accordance with implementations of various embodiments of this disclosure.

[0023] FIG. 4 shows an exemplary visualization indicating how pressure tests can be conducted, according to some embodiments.

[0024] FIG. 5 shows pressure measurements associated with two individual tests T1 and T2.

[0025] FIG. 6 provides an exemplary tabulation of 4 pressure tests, based on the disclosed methods and systems.

[0026] FIG. 7 provides an exemplary workflow for pressure test optimization according to some embodiments.

[0027] FIGS. 8A and 8B show exemplary rate / volume combinations for the disclosed methods and systems.

[0028] FIG. 9 shows a pareto front of solution rate versus test time, where the illustrated dots show the pareto front.

[0029] FIG. 10 shows an exemplary detailed workflow for methods, systems, and computer programs for pressure testing to generate configuration data for energy development.DESCRIPTION OF EMBODIMENTS

[0030] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed technology. However, it will be apparent to one of ordinary skill in the art that the disclosed embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0031] It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the disclosure. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

[0032] The terminology used in the description of the disclosed techniques is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of this disclosure and the appended claims, the singular forms “a,”“an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms “includes,”“including,”“comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0033] As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0034] Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all of the components of a wavefield, all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and / or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.Computing Systems

[0035] FIG. 1A depicts an example computing system 100 in accordance with some embodiments. The computing system 100 can be an individual computer system 101A or an arrangement of distributed computer systems. The computer system 101A includes one or more geosciences analysis modules 102 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the geosciences analysis module 102 executes independently, or in coordination with, one or more processors 104, which is (or are) connected to one or more storage media 106. The processor(s) 104 is (or are) also connected to a network interface 108 to allow the computer system 101A to communicate over a data network 110 with one or more additional computer systems and / or computing systems, such as 101B, 101C, and / or 101D (note that computer systems 101B, 101C and / or 101D may or may not share the same architecture as computer system 101A, and may be located in different physical locations relative to each other or to computer system 101A. For example, computer systems 101A and 101B may be on a ship underway on the ocean, while in communication with one or more computer systems such as 101C and / or 101D that are located in one or more data centers on shore, other ships, and / or located in varying countries on different continents). Note that data network 110 may be a private network and may use portions of public networks and may include local or remote storage and / or application processing capabilities (e.g., cloud computing).

[0036] A processor can include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0037] The storage media 106 can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 1A storage media 106 is depicted as within computer system 101A, in some embodiments, storage media 106 may be distributed within and / or across multiple internal and / or external enclosures of computing system 101A and / or additional computing systems. Storage media 106 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes and / or non-transitory storage means. Such computer-readable or machine-readable storage medium or media can be considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0038] It is appreciated that computer system 101A is one example of a computing system, and that computer system 101A may have more or fewer components than those shown and may combine additional components not depicted in the example embodiment of FIG. 1A, and / or computer system 101A may have a different configuration or arrangement of components relative to the components depicted in FIG. 1A. The various components shown in FIG. 1A may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0039] It is appreciated that while no user input / output peripherals are illustrated with respect to computer systems 101A, 101B, 101C, and 101D, many embodiments of computing system 100 include computer systems with keyboards, mice, touch screens, displays, and other user peripheral systems or other input-output systems. Some computer systems in use in computing system 100 may be desktop workstations, laptops, tablet computers, smartphones, server computers, etc.

[0040] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in an information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of protection of the disclosed subject-matter.

[0041] FIGS. 1B-1E illustrate exemplary schematic views of a resource site (e.g., an oilfield 100) having subterranean formation 102 containing reservoir 104 therein in accordance with implementations of various technologies and techniques described herein. FIG. 1B illustrates a survey operation being performed by a survey tool, such as seismic truck 106.1, to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In FIG. 1B, one such sound vibration, e.g., sound vibration 112 is generated by source 110 such that the sound vibration reflects off horizons 114 in the earth formation 116. A set of sound vibrations may be received by sensors (e.g., geophone-receivers 118) situated on the earth's surface. The data received 120 may be provided as input data to a computer 122.1 of a seismic truck 106.1, and responsive to the input data, computer 122.1 may generate seismic data output 124. This seismic data output may be stored, transmitted or further processed as the case may require.

[0042] FIG. 1C illustrates a drilling operation being performed by drilling tools 106.2 suspended by rig 128 and advanced into subterranean formations 102 to form wellbore 136. Mud pit 130 may be used to draw drilling mud into the drilling tools via flow line 132 to circulate drilling mud down to the drilling tools, then up the wellbore 136 and back to the surface. The drilling mud is typically filtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling mud. The drilling tools are advanced into subterranean formations 102 to reach reservoir 104. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging systems while drilling. The logging systems may also be adapted for taking core (e.g., soil) sample 133 as shown according to some embodiments.

[0043] Computer facilities may be positioned at various locations about the oilfield 100 (e.g., the surface unit 134) and / or at remote locations. Surface unit 134 may be used to communicate with the drilling tools and / or offsite operations, as well as with other surface or downhole sensors. Surface unit 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit 134 may also collect data generated during the drilling operation and produce data output 135, which may then be stored or transmitted.

[0044] Sensors, such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. In one embodiment, a sensor may be positioned in one or more locations around the drilling tools and / or at rig 128 to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and / or other parameters of the field operation. The sensors may also be positioned at one or more locations in the circulating system according to some embodiments.

[0045] Drilling tools 106.2 may include a bottom hole assembly (BHA) (not shown), near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly may also include capabilities for measuring, processing, and storing information, as well as communicating with surface unit 134. The bottom hole assembly may further include drill collars for performing various other measurement functions.

[0046] The bottom hole assembly may include a communication subassembly that communicates with surface unit 134. The communication subassembly may be adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, wireless technology, or a wired drill pipe communications system. The communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other telemetry systems.

[0047] According to one embodiment, the wellbore may be drilled according to a drilling plan that is established prior to drilling. The drilling plan may set forth equipment data, pressure data, trajectory information and / or other data parameters that define or otherwise specify the drilling process for a given wellsite associated with the resource site (e.g., oilfield 100). The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may be optimized or updated to, for example, deviate from the drilling plan to satisfy efficient drilling operations. Additionally, as drilling or other operations are performed, the subsurface conditions may change. An earth model associated with the resource site may also updated or adjusted to account for the new information being collected about the resource site.

[0048] The data gathered by the sensors disposed about the resource site may be received by surface unit 134 and / or other data collection sources for analysis or other processing. The data collected by the sensors may be used alone or in combination with other data. The data may be received by, and / or stored in one or more databases and / or transmitted to an onsite location or an offsite as the case may require. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time operations, or stored for later use. The real-time data may also be combined with historical data or other inputs for further analysis. According to one embodiment, the data collected at the resource site may be stored in separate databases, or combined within a single database.

[0049] Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations. Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 may then send command signals to oilfield 100 in response to data received. Surface unit 134 may receive commands via transceiver 137 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and / or actuate the controller. In this manner, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and / or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.

[0050] FIG. 1D illustrates a wireline operation being performed using wireline tool 106.3 suspended by rig 128 and into wellbore 136 of FIG. 1C. Wireline tool 106.3 is adapted for deployment into wellbore 136 for generating well logs, performing downhole tests and / or collecting samples. Wireline tool 106.3 may be used to provide another method and apparatus for performing a seismic survey operation. Wireline tool 106.3 may, for example, have an explosive, radioactive, electrical, or acoustic energy source 144 that sends and / or receives electrical signals to surrounding subterranean formations 102 and fluids therein.

[0051] Wireline tool 106.3 may be operatively connected to, for example, geophones 118 and a computer 122.1 of a seismic truck 106.1 of FIG. 1B. Wireline tool 106.3 may also provide data to surface unit 134. Surface unit 134 may collect data generated during the wireline operation and may produce data output 135 that may be stored or transmitted. Wireline tool 106.3 may be positioned at various depths in the wellbore 136 to provide a survey or other information relating to the subterranean formation 102.

[0052] Sensors, such as gauges, may be positioned about the resource site (e.g., oilfield 100) to collect data relating to various field operations as described previously. According to one embodiment, the sensor may be positioned within wireline tool 106.3 to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and / or other parameters of the field operation.

[0053] FIG. 1E illustrates a production operation being performed by production tool 106.4 deployed from a production unit or Christmas tree 129 and into completed wellbore 136 for drawing fluid from the downhole reservoirs into surface facilities 142. The fluid flows from reservoir 104 through perforations in the casing (not shown) and into production tool 106.4 in wellbore 136 and to surface facilities 142 via gathering network 146. According to one embodiment, sensors, such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. For example, the sensors may be positioned within production tool 106.4 or within or about an associated equipment, such as Christmas tree 129, gathering network 146, surface facility 142, and / or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and / or other parameters of the production operation. In one embodiment, one or more injection wells may be fluidly coupled to the reservoir for added fluid recovery. Furthermore, one or more gathering facilities may be operatively connected to one or more of the wellsites within the resource site (e.g., oilfield 100) for selectively collecting downhole fluids from the wellsite(s).

[0054] While FIGS. 1C-1E illustrate tools used to measure properties of a resource site (e.g., oilfield 100), it is appreciated that the tools may be used in connection with non-oilfield operations, such as gas fields, mineral mines, aquifers, storage, or other subterranean facilities. Also, while certain data acquisition tools are depicted, it is appreciated that various measurement tools capable of sensing parameters, such as seismic two-way travel time, density, resistivity, production rate, etc., of the subterranean formation and / or its geological formations may be used. Various sensors may be located at various positions along the wellbore and / or coupled to or be situated within the monitoring tools to collect and / or monitor the desired data. Other sources of data may also be provided from offsite locations to supplement or otherwise enhance data captured at the resource site.

[0055] The field configurations of FIGS. 1B-1E are intended to provide a brief description of an example of a resource site usable with oilfield application frameworks. Part of, or the entirety, of oilfield 100 may be on land, water, and / or sea. Also, while data associated with a single resource site is indicated as being measured and / or processed at a single location within these figures, oilfield applications may be used with any combination of one or more resource sites (e.g., a plurality of oilfields 100), one or more processing facilities, and one or more similar or dissimilar wellsites.

[0056] FIG. 2 illustrates a schematic view, and in particular, a partial cross section of the resource site (e.g., referenced as oilfield 200 elsewhere herein) that has data acquisition tools 202.1, 202.2, 202.3 and 202.4 positioned at various locations about the resource site for collecting data of subterranean formation 204 in accordance with implementations of various technologies and techniques described herein. Data acquisition tools 202.1-202.4 may be the same as data acquisition tools 106.1-106.4 of FIGS. 1B-1E, respectively, or others not depicted. As shown, data acquisition tools 202.1-202.4 may generate data plots or measurements 208.1-208.4, respectively. These data plots are depicted along the resource site (e.g., oilfield 200) to demonstrate the data generated by the various operations.

[0057] Data plots 208.1-208.3 are examples of static data plots that may be generated by data acquisition tools 202.1-202.3, respectively; however, it is appreciated that data plots 208.1-208.3 may include data plots that are updated in real time or near-real time. These measurements may be analyzed to better define the properties of the formation(s) and / or determine the accuracy of the measurements and / or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.

[0058] Static data plot 208.1 is a seismic two-way response over a period of time. Static plot 208.2 is core sample data measured from a core sample of the formation 204. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot 208.3 is a logging trace that can provide, for example, a resistivity measurement or some other measurements of the formation at various depths. Also shown in the figure is a production decline curve or graph 208.4 which indicates a dynamic data plot of the fluid flow rate over time. The production decline curve can provide the production rate as a function of time. As fluid flows through the wellbore, measurements may be taken of fluid properties, such as flow rates, pressures, composition, etc., according to some embodiments.

[0059] According to some implementations, other data may also be collected or captured or associated with the resource site, such as historical data, user input data, economic data, and / or other sensor data and / or other parametric data associated with one or more models of the resource site. As described below, static and dynamic measurements may be analyzed and / or used to generate models of the subterranean formation to determine characteristics thereof. Similar or dissimilar measurements may also be used to measure or track changes a geological formation associated with the resource over time.

[0060] In one embodiment, the subterranean structure 204 may have a plurality of geological formations 206.1-206.4. As shown in FIG. 2, this geological formations may comprise several formations or layers, including a shale layer 206.1, a carbonate layer 206.2, a shale layer 206.3 and a sand layer 206.4. A fault 207 may extend through the shale layer 206.1 and the carbonate layer 206.2. In addition, the static data acquisition tools may be adapted to take measurements and detect characteristics of the aforementioned formations and / or other geological structures within the subterranean structure 204. While a specific subterranean formation with specific geological structures is depicted FIG. 2, it is appreciated that the resource site (e.g., oilfield 200) may contain a variety of geological structures and / or formations, sometimes having extreme complexity than those depicted. In some locations within the subterranean structure 204 may be below the water line such that fluid may occupy pore spaces of the one or more formations depicted. Each of the measurement devices may be used to measure properties of the formations and / or other geological features within the subterranean structure 204. While each acquisition tool is shown as being in specific locations at the resource site (e.g., oilfield 200), it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and / or for analysis and / or for integration with data captured at the resource site. The data captured from various sources, such as the data acquisition tools of FIG. 2, may then be processed and / or evaluated. In some embodiments, seismic data may be displayed in a static data plot 208.1 from the data acquisition tool 202.1 and may be used to determine characteristics of the subterranean formations and other geological features associated with the resource site. The core data shown in the static plot 208.2 and / or log data from the well log 208.3 may be used to determine various characteristics of the subterranean formation. The production data from graph 208.4 may also be used to determine fluid flow reservoir characteristics as the case may require. In one embodiment, the captured data from the resource site may be used to generate models that facilitate additional analysis of the subterranean structure 204 of the resource site.

[0061] FIG. 3 illustrates a resource site (e.g., oilfield 300) for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the resource site has a plurality of wellsites 302 operatively connected to central processing facility 354. The resource site configuration of FIG. 3 is not intended to limit the scope of the oilfield application system. Part, or all, of the resource site may be on land and / or sea. Also, while a single resource site with a single processing facility and a plurality of wellsites is depicted, any combination of one or more resource sites, one or more processing facilities 354 and one or more wellsites 302 may be present according to some embodiments.

[0062] Each wellsite 302 may have equipment associated with one or more wellbores 336 within the subterranean formation 306 of the resource site. In particular, the wellbores 336 may extend through or into the subterranean formations 306 including reservoirs 304. These reservoirs 304 may contain liquid and / or gaseous fluids, such as hydrocarbons. In one embodiment, the wellsites 302 may draw fluid to and / or from the reservoirs and may pass said fluids to processing facilities via surface networks 344. The surface networks 344 may have tubing and control mechanisms for controlling the flow of fluids from the wellsite 302 to processing facility 354.

[0063] Attention is now directed to methods, techniques, and workflows for processing and / or transforming collected data that are in accordance with some embodiments of this disclosure. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed. Those with skill in the art will recognize that in the geosciences and / or other multi-dimensional data processing disciplines, various interpretations, sets of assumptions, and / or domain models such as velocity models, may be refined in an iterative fashion; this concept may be applicable to the procedures, methods, techniques, and workflows as discussed herein. This iterative refinement can include use of feedback loops executed on a computation logic basis, such as at a computing device (e.g., computing system 100 of FIG. 1A), and / or through control mechanisms based on determinations or inputs regarding whether a given step, action, template, or model has become sufficiently accurate.Parameter Optimization Workflow

[0064] Subsurface pore pressure measurements can be essential over the life of resource sites such as oilfields, providing critical information from energy exploration through energy development. However, designing pressure tests with a formation tester (e.g., on a Wireline tool or a logging while drilling (LWD) tool) might not be straight forward as it can require leveraging variables or parameters including an overbalance parameter, a permeability (both horizontal and vertical) parameter, a viscosity parameter, a porosity parameter, a compressibility parameter, etc. According to one embodiment, the aforementioned parameters can be modeled to predict the behavior of a pressure response under multiple variable combination. Typically, trial and error approaches may be used to adjust a given pressure test to determine optimal parameter selection. Considerations for such optimal determinations include: fluid drawdown volume considerations or criteria; fluid drawdown rate considerations or criteria; and fluid buildup time considerations or criteria. According to one embodiment, one objective of the disclosed methods and systems is to optimize fluid drawdown volume, and / or fluid drawdown rate, and / or fluid buildup time in such a way that a fully stabilized pressure measurement is obtained in a minimum time. In practice this is difficult since each test has to be evaluated over many different variable or parameter combinations encountered.

[0065] According to one embodiment, the disclosed methods and systems enable or otherwise automate the process of determining optimal pressure test parameters, given an expected distributions of the key variables. This optimization includes a trade-off between the probability a given test would work for a random variable combination and the time each test takes, which can be based on system configurations. Additionally, the disclosed methods and systems can enable generating multiple pressure tests, such that each pressure test is optimized to solve or evaluate scenarios previous tests failed to solve.Pressure Tests

[0066] FIG. 4 shows an exemplary visualization indicating how pressure tests can be conducted, according to some embodiments. As can be seen in this figure, subsurface pressure testing can comprise configuring or calibrating a formation tester 402 to acquire probe data 404 from a formation. To achieve this, a packer system or a probe is used to attach one or more sensors to the formation wall 413 by penetrating the mud cake 411. According to one embodiment, a given volume of fluid (e.g., liquid / gas) can be sucked into a pre-test piston / chamber 405 at a given rate. This ideally creates a lower pressure in the formation tester 402 than in the formation 413 while the borehole pressure itself can be higher than the formation pressure to avoid a borehole collapse. Furthermore, as the formation tester pressure falls below the formation pressure, fluid (e.g., gas / liquid) starts to flow from the formation into the tester until the two pressures equalize. By taking a measurement of the pressure inside the tester using, for example, a pressure gauge 403, the pressure in the formation 413 can be approximated or accurately determined. In practice, this pressure determination process can be repeated by pulling an additional fluid volume into the formation tester 402, which allows the final pressure to be measured at a second time. If the two measured pressures agree with each other the measurement is seen as a success. This assumes that each individual measurement clears a quality control process as needed.

[0067] By applying the approach outlined above in association with FIG. 4, the pressure gauge 403 constantly measures the pressure in the formation tester 402 leading to the curve shown in FIG. 5. In particular, FIG. 5 shows pressure measurements associated with two individual tests T1 and T2. Further, this figure provides insight into how the pressure starts at the level of the borehole 409, and then increases as the packer compresses against the mud cake 411. The pressure then drops as fluid volume in sucked into the formation tester 402, to then increase again to the formation pressure. In some cases, this process can be repeated before the pressure in the formation tester 402 is matched or allowed to return to the well pressure.Test Parameters

[0068] In some embodiments, parameters that control each pressure test (e.g., small pressure test) comprise:

[0069] A first parameter controlling or indicating the fluid volume of extracted.

[0070] A second parameter controlling or indicating the rate at which the fluid volume is extracted.

[0071] A third parameter controlling or indicating the fluid build-up time given to the system to stabilize to the formation pressure.

[0072] In some embodiments, a measurement is considered successful if:

[0073] 1. For each test, the pressure reaches a lowest value and then increase to the final value indicating that the formation pressure has been arrived at.

[0074] 2. The pressure converges to a threshold value such that the threshold value remains stable or constant for a minimal amount of time.

[0075] 3. A first test and a second test must both substantially converge to the same value.

[0076] FIG. 6 provides an exemplary tabulation of 4 pressure tests, based on the disclosed methods and systems, named tests A-D with various parameter configurations for each test. In some embodiments, the choice of adding customized tests can be leveraged to account for various subsurface environments or structures.Environmental Parameters

[0077] When considering what tests might work best for a given environment or test location (e.g., subsurface environment), properties (e.g., surface or subsurface properties) in the test location can be characterized using one or more parameters including:

[0078] An over balance parameter—A parameter indicating the difference between Mud pressure and Formation pressure.

[0079] A formation pressure parameter—A parameter indicating the pressure of a formation under consideration.

[0080] A density parameter—A parameter indicating the bulk density of rock in the formation under consideration.

[0081] A porosity parameter—A parameter indicating the porosity of the rock in the formation.

[0082] A viscosity parameter—A parameter indicating the viscosity of fluid filling a pore space in the rock.

[0083] A horizontal permeability parameter—A parameter indicating the horizontal component of the rock permeability.

[0084] A vertical permeability vs horizontal permeability ratio parameter—A parameter indicating the ratio between the vertical permeability and the horizontal permeability of the rock.

[0085] A mud compressibility permeability-A parameter indicating the compressibility of the mud filtrate.It is appreciated that for energy development structures such as wells, it is possible to estimate a value distribution or a data distribution for each of the above parameters.

[0086] According to one embodiment, a testing or simulation tool may be used to determine optimal data values for each of the above parameters via, for example, one more computing simulations. According to some embodiments, the optimal data values enable parameterizing or configuring one or more testing equipment or systems at a resource site during energy exploration activities.

[0087] An issue with the tests discussed in association with FIG. 4 is that there are many environments where such tests do not work well. Typical, the tests associated with FIG. 4 can generate erroneous or problematic results in cases of high overbalance and low permeability subsurface characteristics. Thus, the foregoing methods and systems enable implementing customized and / or automatic pressure tests for a plurality of subsurface scenarios. This is where the need for customized pressure tests comes in. In particular, the disclosed methods and systems beneficially enable developing a plurality of combinations of data measurements and / or environmental parameters associated with the aforementioned parameters over short periods of time (e.g., one second or less) and does not rely on domain experts for validation. This minimizes complications when considering that a whole set of similar and / or dissimilar pressure tests can be determined such that the similar and / or dissimilar tests complement each other so that there is a working test for subsurface characteristics for a myriad of subsurface scenarios and / or different types of subsurface environments. Specifically, the disclosed methods and systems include a workflow that finds one or more optimal tests (e.g., pressure tests) in a structured way to aid in quickly and robustly defining and / or characterizing and / or determining pressure tests that meets the needs of an energy develop project.Success Criteria

[0088] According to one embodiment, a pressure test job comprising two pressure tests (e.g., two consecutive pressure tests) can be considered successful if both of the two tests fulfil a convergence criteria that indicates that the two pressure tests (e.g., in some cases two or more pressure tests) converge to the formation pressure, within some acceptable value threshold or tolerance threshold. The value threshold or tolerance threshold, for example, can indicate how close the first result from a first pressure test comprised in the two pressure tests is relative to a second result of a second pressure test comprised in the two pressure tests. In some cases, the first result and the second result are associated with a depreciating pressure value that goes below the formation pressure by a specific amount during a drawdown phase of the testing. Observance of the depreciating pressure value beneficially enables confirms or otherwise validates both the first pressure test and the second pressure test.Time Complexity

[0089] One of the main challenges in determining, suggesting, or forecasting data values for test parameters is that the search space for said data values is expansive or substantially large. According to one embodiment, each pressure test job setup may be defined by two pressure tests (e.g., two or more tests), each with a rate parameter, a volume parameter, and a time parameter, giving a total of six parameter combinations. In a brute force approach, each parameter may be configured to have 10 different data values which are tested in parameter pairs for a given test project. Applying this approach can result in up to 106 parameter combinations to test for each job. Assuming that there are 1000 test problems drawn from a given distribution, where each simulation takes about 1 second or less meaning a testing tool like a similar would have to run test for approximately 32 years:1⁢06×1⁢0⁢0⁢0×1.0=1⁢09⁢ sek≈32⁢ years

[0090] This is clearly not feasible. Additionally, one benefit of the disclosed approach is that two tests comprised in a pressure test job can be individually deconstructed or disentangled to individually analyze each of the two tests even if the two tests are concurrently executed. In addition, the optimal execution time for the two tests can furthermore be deduced a posteriori relative to an evaluation for a given ratio / volume combination, meaning that it is enough to select a time long enough to be longer than the longest time that can be considered. Still assuming 10 possible values for each rate parameter and volume parameter thereby reducing the simulation time to approximately 2 days:2×1⁢02×1⁢0⁢0⁢0×1.0=2×1⁢05⁢ sek≈2.3 days

[0091] This is much more feasible. As such the computation and / or financial costs for a given subsurface environment is substantially reduced, and in some cases, optimizing pressure tests for the subsurface environment in a matter of minutes. This can even be extended to be a one-time cost for testing any environment.Disentanglement of Tests

[0092] To disentangle the two tests referenced above, the following assumptions may be made:

[0093] 1. The environmental response to the first test is not affected by the second test.

[0094] 2. The environmental response to the second test depends mainly on a starting pressure, and not on how the first test reached the starting pressure.

[0095] The first assumption suggests that the first test cannot be affected by the second test, as the second test occurs after the first test. The second assumption rests on the “Markov property”, meaning that the response to an action only depends on the state in which the action is taken, and not on how that state was reached. Consider a given environmental scenario where the environmental parameters are set to some specific values for two different pressure test jobs, each consisting of two pressure tests. The table below, for example, provides two exemplary pressure tests, both of which have the same second test C but different first tests A and B:Setting test 1Setting test 2Pressure test 1ACPressure test 2BC

[0096] The assumptions are that if test A and B converge to the same pressure value p, then the outcome of test C will be identical in both cases. In practice this pressure p would be the formation pressure since the point of having two tests is for the two tests to confirm each other. For this reason, if the first test does not converge to the formation pressure, then the outcome of the second test would not be informative. In practice, tests may not converge to the formation pressure exactly or precisely, but there will have to be some threshold or tolerance value close to, or approximately equal to the formation pressure value. For successful tests, the pressure value associated with the outcome of said successful tests may approach a value of the formation pressure as indicated in FIG. 5, meaning that the final pressure may be the value of the formation pressure minus the offset / threshold / tolerance value.

[0097] According to one embodiment, the smaller the offset value is, the faster the second test will converge to the formation pressure during the second test. This means that if the second test is started based on subtracting the threshold or offset value from the formation pressure and the second test succeeds (e.g., results from the second test also reaches the formation pressure minus an offset smaller than the threshold), then it can be concluded that the second test would also have succeeded if the offset of the first test had been even smaller. Note that this is only an approximation, but empirical tests validate the foregoing. Also note that the disclosed threshold values of the two tests might be different, and their scale may be significantly lower than the scale of an over balance pressure value. As an example, the over balance pressure value might be around 2000 psi, but the threshold could be in the scale of 0.5 psi or smaller.Method

[0098] FIG. 7 provides an exemplary 700 workflow for pressure test optimization according to some embodiments. It is appreciated that a data engine stored in a memory device may cause a computer processor to execute or initiate executing the various processing stages of the workflow 700. For example, the disclosed techniques may be implemented as a data engine of a computing platform associated with a geological software tool such that the data engine enables optimally executing pressure tests associated with subterranean structures at a resource site.

[0099] At block 702, distribution data is provided across or over key variables or parameters in a subsurface environment (e.g., an expected subsurface environment) and used to generate a scenario distribution. The scenario distribution may then be used to generate a large set of likely test scenarios.

[0100] Given a set of rate / volume data combinations 706, each test scenario 704 is evaluated for each rate / volume combination using a simulator tool to generate one or more pressure curves 708. The resulting one or more pressure curves 708 may be saved in a database.

[0101] Given that the database of the two tests and the set of exemplary problems or tests that need to be executed (e.g., all the tests), optimal fluid rate data and volume rate data may be determined for each total test time. This can create a “pareto front” where there is a trade-off between the proportion of exemplary problems or tests conducted for the total test time. A memoization process 710 may be implemented to leverage previously computed results in subsequent tests instead of recalculating the aforementioned fluid rate and fluid volume data.

[0102] In some cases, after a solution 712 and / or 714 is selected, the workflow of FIG. 5 may be used to solve problems associated pressure tests where the exemplary problems solved by the previous pressure tests are now removed. This allows a new setting to be found that optimally targets the example problems that the initial tests failed to solve. In some instances, this can be done multiple times, generating a toolbox of multiple customized tests, equipped for every situation.

[0103] At block 716, data results or solutions 712 and / or 714 may analyzed or optimized to determine unsolved scenarios from or associated with the scenario distribution generated from block 702. In some cases, solved scenarios 718 may be fed to the data engine for analysis at block 716 in tandem with, or independently of, the results or solutions 712 and / or 714.

[0104] Outputs from block 716 may be used to generate performance analysis charts 720 which in turn can be used to drive executing or implementing new solutions 722 for new or updated scenarios.Expected Environmental Distribution

[0105] As previously discussed in association with block 702, a description of a subsurface environment may be provided which may or may not have optimal tests (e.g., pressure tests) already established. This can be accomplished using an input computing device to provide the description of the subsurface environment. In one embodiment, this is may be done by providing an expected data or scenario distribution (e.g., environmental distribution) over the relevant environment parameters. One relatively intuitive process of doing this is to provide pressure values P0, P10, P50, P90 and P100 for all parameters concerned. Assuming a sample s is drawn randomly from the distribution, then the following probabilistic relationship may apply:P(s≤PX)=X %In particular, the pressure values can be probability figures which are typically already approximated before any pressure test job, which means that these values can be supplied without too much additional work on the side of the stakeholder.Creation of Test ScenariosUsing the scenario / environmental distribution, a large set of test scenarios may be created. This set may be used to approximate the probability that a given pressure test job will work in a random position in a well at a resource site under consideration. If there are 1000 scenarios and a given pressure test works for 100 of them, it can be approximated that the particular test job should work in 10% of the real cases, as these tests are supposedly drawn from the same distribution. To generate the scenarios from the distribution, the following method may be applied:1. Draw a random number x uniformly so that x˜U(0, 100).

[0108] 2. Find the 2 closest percentiles PA and PB to the number, so that A<x<B. For example, if x=45 these are P10 and P50.

[0109] 3. Define an environmental value y by linear interpolation, so that:y←B-xB-A⁢(PA)+x-AB-A⁢PB

[0110] This creates a uniform distribution between the given percentile values. If one expects that the distributions are logarithmic, for example (P10, P50, P90)=(1,10,100), it could be worth doing the above method in logarithmic scale, so that:y←exp [B-xB-A⁢ln⁡(PA)+x-AB-A⁢ln⁡(PB)]Memoization of Simulation Results

[0111] This step can be divided into several parts:

[0112] 1. Defining volume / ratio combinations to evaluate for each of the two tests.

[0113] 2. Simulate all test scenarios for all chosen test setups.

[0114] 3. Save the results in a database.Defining Volume / Ratio Combinations

[0115] To define volume / ratio combinations for the disclosed methods and systems, it is needful to consider that not all parameter combinations are valid. For instance, the volume fluid volume v is determined by the fluid rate r and the time t the drawdown is performed, so that v=rt. The rates and times cannot be set to any value either. In the disclosed embodiments, the following constraints are applied:

[0116] r∈{0.2, 0.4, . . . , 2.0} with increments of 0.2.

[0117] t∈{2.5, 3.0, . . . , ∞} with increments of 0.5.

[0118] v=rt∈[0.5, 15]v1+v2≤24, where vi is the fluid volume of test i.

[0119] Using these constraints, a grid may be generated including all legal points. From this set of possible combinations, a smaller set of combinations may be evaluated and / or selected to give a good coverage over all combinations. One way to do this is to build the smaller set point by point, where each new point is found by taking the grid point furthest away from all previous points in logarithmic space. The set is then initialized with the tests referenced in association with FIG. 6. as the set serves as referent points for which performance is yet to be determined. Exemplary rate / volume combinations for the disclosed methods and systems are illustrated in FIGS. 8A and 8B.Simulate all Test Scenarios for all Chosen Test Setups

[0120] To understand the performance of a rate / volume combination for one of two tests (e.g., two smaller tests) associated with a specific test scenario, a simulation tool may be used to generate performance data for analysis. When investigating the first test, a fluid rate parameter and a fluid volume parameter may be configured or set to have data values together with a time value relative to an acceptable largest time value. The second test is then configured to have a shorter execution time relative to the first test. In particular, the first test is configured or set to whatever rate / volume configuration that succeeds in the shortest amount of time and let the second test start exactly at that moment (e.g., when pressure results from the first test converges with the formation pressure) with the desired second test rate / volume, as well as the amount of acceptable execution time.In one embodiment, the first test(s) may be evaluated first, as the second test requires the results of the first tests to proceed. After each simulation, the pressure curve generated by the first / second test is saved into a database. Thus, the saved pressure response to each of the two pressure tests for each rate / volume combination for each scenario can be leveraged in future analysis of new subsurface environments. This allows approximating the outcome of any scenario i and complete pressure test job based on the following relationship for a given test (T):T=(r1,v1,t1,r2,v2,t2)by pasting the first t1 seconds of the (r1, v1) pressure curve to the first t2 seconds of the (r2, v2) of the second test, for the given scenario i.To evaluate the disclosed method, the approximated outcomes of a given pressure test setup can be compared with the outcomes of the simulation tool. The goal here is to determine if the approximation could be predictive of what scenarios the given test setup would fail in. In a test of 1000 cases, the approximation worked in 990 of the cases. All failed predictions were cases very close to the accepted boundary value (e.g., prediction 0.11, true value 0.08, with threshold of 0.1). Of the errors, 8 were false negatives, and 2 were false positives. False negatives were the cases where the second test was thought to not reach the threshold in time, even though it actually did. This likely happened since the first test came closer than expected to the formation pressure than the allowed threshold, giving the second test a better starting position than expected. In the two cases where the approximation assumed it would work while it did not, it was the first test that failed as the simulation deviated ever so slightly from the initial recorded value.Pareto Front OptimizationResults up until now can be made in advance. The goal here is to compute the optimal pressure tests for a distribution received by the disclosed systems, while allowing the systems flexibility in defining optimality. Considerations for this process can include:A trade-off between total time of the pressure test, and the proportion of test scenarios solved by the test.

[0124] Possible subsets of all test scenarios that needs to be solved (e.g., scenarios with a variable like over balance) in a given range, scenarios for which some other pressure tests do not work, etc.

[0125] Optimal tests for different success criteria.

[0126] Given the above considerations, a pareto-front may be generated showing the best tests, for the evaluated test scenarios, as a function of the total time of the test as shown in FIG. 9. In particular, FIG. 9 shows a pareto front of solution rate vs test time, where the illustrated dots show the pareto front. Each datapoint indicated in this figure corresponds to a particular (r1, v1, r2, v2) setup. The solution rate, as shown in this figure, represents the proportion of tested scenarios for which a setting is successful. To efficiently generate this front, the following process may be used:

[0127] 1. Given thresholds for tests 1 and 2, the data engine referenced elsewhere herein computes the time it takes for each recorded pressure curve to succeed, according to the success criteria referenced above. If the pressure curve does not succeed at any time step, the data engine registers infinity as the result. These results may be saved in data matrices M1, M2, where each row is a scenario, each column is the rate / volume combination, and the matrix data values represent the time to success, and infinity indicating failure cases. An example can be seen in “outcomes test 1” and “outcomes test 2” referenced in association with data results or solutions 712 and / or 714 of FIG. 7.

[0128] 2. The data engine creates a set of time spans t1, t2 to evaluate for each test, for example 100 values linearly spaced between 0 and 900 seconds.

[0129] 3. The data engine initializes two empty dictionaries: C and S, where:

[0130] a. C[t] contains a combination (r1, v1, r2, v2) for the total test time:t=t(1)+t(2)b. S[t] contains the solution rate of combination C[t].4. For each test time combo (t(1), t(2)) where t(i)∈ti:

[0133] a. The data engine computes what first test will be successful in time t(1) by checking what scenarios need less time to finish:B1=(t(1)>M1)b. The data engine computes the rate of scenarios for which the first test will work, by taking:s1=meancolumn(B1)where s1=[s1, s2, . . . , sn] where s1 is the proportion of scenarios for which the first test setting (rj, vj, t(1)) works.c. The data engine creates the variables (j*, k*, s*) where:i. j* is the index of a rate / volume combination of the first test.ii. k* is the index of a rate / volume combination of the second test.iii. s* is the solution rate for the two tests together, initialized to 0.

[0140] d. The data engine goes through or evaluates or analyzes the first rate / volume combinations by index j, in descending order of solution rate in s1.

[0141] i. If s1[j]<s* break loop. The reason is that there is minimal possibility that the data engine can find a better combined test when even the first half of that scenarios to the rate s* cannot be solved.

[0142] ii. The data engine computes B2=(t(2)>M2) & B1[j], where B1[j] is a Boolean list telling is a given scenario is solved by first test j. The new matrix B2 is thus a Boolean matrix saying a given second test configuration will solve a given scenario, if used together with first test j.

[0143] iii. The data engine computes the solution rates for all combinationss2=meancolumn(B2)iv. The data engine picks the best second test settingk=argmax(s2)v. If s2 [k]>s*, set (j*, k*)=(j, k) and s*=s2 [k].e. If s*>S[t(1)+t(2)], or if S[t(1)+t(2)] is undefined:i. ST[t(1)+t(2)]=s*ii. CT[t(1)+t(2)]=(j*,t(1),k*,t(2))5. The data engine then evaluates or goes through all keys in S and C in ascending order of time and remove key-value pairs where increased time did not improve the solution rate.The above process led to the pareto front discussed in association with FIG. 9, where the x-axis shows the total time, and the y-axis shows the solution rate. Each solution can be associated with a specific solution (j*, t(1), k*, t(2)) where j* and k* in turn are indices of specific rate / volume combos.Using the Pareto FrontGiven the pareto front, a trade-off between solution rate and total test time can be determined. This can either be done by defining a total test time or a required solution rate. The nearest member of the pareto front to that value is then selected. This means that if the time is specified, the test solving the largest number of scenarios in that time is selected, and if the solution rate is selected, the test needing the least time to reach that rate is selected.Visual FeedbackTo see for what scenarios work for a determined test (j*, t(1), k*, t(2)), the data engine can implement the following data relationship:b work=(t(1)>M1[j*])&⁢ (t(2)>M2[k*])It is then possible to display all test scenarios, for example, as data points defined by their overbalance and horizontal Permeability, where the data points indicate whether a problem associated with a test scenario will be resolved or not with the test, with alternatively some scenarios being designated as uninteresting with or without the support of a user. An example of such feedback can be seen as new solutions 722 in FIG. 7. Note that this feedback works for non-optimized test-setups as well, such as customized test setups.Building a Set of Solutions

[0152] In practice, a user would often not be looking for a single optimal setting, but several. In particular, the current generation of the hardware allows for multiple different customized tests (e.g., at least 4 tests) to be added. To use this capacity to the fullest, it is needful that these tests complement each other, so that when one test fails there is another that will work. One way of doing this is the sequential approach, where pressure tests are added sequentially to a list to optimally resolve the test scenarios that were not solved by the previous tests in the list. This is useful when the user wants to use the tests in a given order and do the least number of tests possible before having a successful reading.

[0153] If on the other hand the goal is to create a set of pressure tests so that the tests together resolve as many scenarios as possible, a parallel approach might be considered. In this case the list can potentially be initiated with the tests A-D of FIG. 6. For the remaining scenarios, a set of customized tests may be added according to the following process:

[0154] 1. The data engine initializes the set of tests, potentially with the sequential approach described above, or a custom approach, to bias the set of tests in a given direction.

[0155] 2. The data engine loops though the tests one by one:

[0156] a. To remove an unapplicable the test.

[0157] b. To find a new optimal test for the problems not solved by the other tests.

[0158] c. When all test returns themselves, the data engine determines pressure convergence following which is stops this process.

[0159] In an empirical test, the two approaches (e.g., sequential and parallel approaches) were compared and results indicated in the following table:Test 1Test 1 + 2Sequential approach79.40% (solution rate)82.30%Parallel approach77.60%84.00%As can be seen in the table, the first test is more likely to succeed in the sequential approach, but the chance of having a working test to any given scenario is higher in the parallel approach.Alternate Disentanglement Method

[0160] In the approach described so far, an assumption is made that the two tests can be disentangled from each other, in so far that the outcome of the second test only depends on the final pressure of the initial test and the rate and volume of the second test. If this does not hold then the second test needs to be investigated with a particular first tests as basis. One way to approach this, without the need of exploring all test combinations and without taking too much time, is to do a depth-first search. Assuming only searching for the very best setup to a set of scenarios, the data engine can:

[0161] 1. Order the first tests by the proportion of test scenarios for which they work.

[0162] 2. Remember the highest proportion of tests any combination has solved, starting with 0.

[0163] 3. Go through the first tests, starting with the best:

[0164] a. If the first test solves less than the best combo, the data engine stops or breaks this workflow.

[0165] b. For second tests, the data engine:

[0166] i. Simulates test problems with the first test followed by the second.

[0167] ii. Records the proportion of test scenarios solved.

[0168] c. If the best (test1, test2) combination is better than the previous best, the previous test is replaced by the best (test1, test2) combination.This will lead to the best setting, and by memoizing all results, it is likely that there is also enough data to simulate combined tests after having removed the scenarios solved by the first test. Note however that this approach will take much longer than the one where the two problems can be disentangled, by a factor of the number of first tests under consideration. Usually, pressure test jobs are prepared for months in advance, and the spent time would still be acceptable. To speed this up, the number of test problems and rate / volume combinations can be reduced (e.g., at the cost of precision). It is also possible to break the test execution loop earlier if the solution rate reaches some required level.Generalization to Multiple Domains

[0169] The current embodiments show an approach aimed at a particular environment with an expected variable or parameter distribution. In some embodiments, the memoization step, which is the time-consuming step, needs to be redone for each new environment. An alternative approach would be a larger set of example scenarios with data points evenly sampled over every environmental attribute. Memoization would then be done just like before, over all these problems with all rate / volume combinations. The difference is that now, when a given environmental distribution is given, each test scenario is given a weight representing the probability that that scenario would be seen in the new environment. This weight could, for example, correspond to the density function's value in a given location, derived from the distribution. Using these weights, a weighted solution rate could be computed using:sr,v=∑ i=1 nwi⁢bi(r,v)∑ i=1 nwiWhere sr,v is the solution rate for a given rate and volume, wi is the weight of example scenario i, and bi(r,v) is a Boolean indicating whether the rate and volume values r, v worked. This way, no new memoization needs to be done for new domains, and optimization to any domain can be done at an instance. Because of this, it is possible to do memoization over a very long time, building an extensive data base.From Memoization to Machine Learning

[0171] As the database of stored test results grows large, it becomes possible to use said database (e.g., based on optimal data stored therein and which is referenced in association with the FIG. 10) to train a neural network that could replace it to generate test results.Exemplary Detailed Workflow

[0172] FIG. 10 shows an exemplary detailed workflow 1000 for methods, systems, and computer programs for pressure testing to generate configuration data for energy development. It is appreciated that a data managing module or a data engine stored in a memory device may cause a computer processor to execute the various processing stages of the workflow 1000. For example, the disclosed techniques may be implemented as a data manager or signal processing engine within a geological software tool such that the data manager or signal processing engine enables pressure testing to generate configuration data for energy development for a subsurface environment of interest.

[0173] At block 1002, the data engine determines distribution data for a subsurface environment of interest. The distribution data, for example, can comprise probabilistic data values associated with analyzing the subsurface environment of interest based on one or more subsurface parameters

[0174] Turning to block 1004, the data engine generates, based on the distribution data, a set of test scenarios for the subsurface environment of interest. In one embodiment, the set of test scenarios comprise data settings configured to approximate a likelihood that a first pressure test or a second pressure test is implementable at a random position about the subsurface environment of interest based on the one or more subsurface parameters.

[0175] At block 1006, the data engine combines, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data.

[0176] Similarly, the data engine combines, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data as indicated at block 1008.

[0177] Turning to block 1010, the data engine generates, based on the first combination of fluid rate data and fluid volume data, a first pressure curve for the subsurface environment of interest.

[0178] The data engine may generate, based on the second combination of fluid rate data and fluid volume data, a second pressure curve for the subsurface environment of interest as indicated at block 1012.

[0179] At block 1014, the data engine determines, based on the first pressure curve and the second pressure curve, convergence data indicating a similarity between: a first pressure value comprised in the first pressure curve relative to a formation pressure value associated with the subsurface environment of interest; and a second pressure value comprised in the second pressure curve relative to the formation pressure value associated with the subsurface environment of interest.

[0180] At block 1016, the data engine generates, based on the convergence data relative to the one or more subsurface parameters, data values (e.g., optimal data values) for energy exploration equipment associated with developing the subsurface environment of interest.

[0181] In one embodiment, the data engine initiates configuring or configures the energy exploration equipment based on the optimal data values as indicated at block 1018. This can involve: configuring or controlling settings of one or more valves comprised in the energy exploration equipment based on the optimal data values; controlling or configuring settings of one or more pumps comprised in the energy exploration equipment; configuring safety equipment comprised in the energy exploration equipment; configuring or controlling wellhead maintenance; configuring or controlling equipment that monitor pipeline integrity; etc.

[0182] In other embodiments, a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features.

[0183] In one embodiment, the set of test scenarios for the subsurface environment of interest comprises a plurality pressure tests for the subsurface area of interest. In addition, the first pressure test and the second pressure test are comprised in the plurality of pressure tests.

[0184] In some implementations, a memoization process is implemented, based on the first pressure curve or the second pressure curve, to exclude the first pressure test or the second pressure test from subsequent pressure test determinations for the subsurface environment of interest relative to other pressure tests comprised in the plurality of pressure tests.

[0185] In addition, the first pressure test and the second pressure test are disentangled based on: a first environmental response associated with the first pressure test being unaffected by the second pressure test; and a second environmental response associated with the second pressure test depending on a starting pressure associated with the second test independent of how the starting pressure is reached.

[0186] In some cases, the first pressure test and the second pressure test are disentangled based on outcome data of the second pressure test depending on a final pressure value associated with the first pressure test and fluid rate data or fluid volume data of the second pressure test.

[0187] Moreover, the subsurface environment of interest comprises a primary subsurface structure having one or more secondary subsurface structures.

[0188] Furthermore, the primary subsurface structure comprises a basin while the one or more secondary subsurface structures comprise a reservoir.

[0189] In some cases, the one or more subsurface parameters comprise: a fluid drawdown volume parameter configured for controlling or indicating a volume of fluid extracted from the subsurface environment of interest; a fluid drawdown rate parameter configured for controlling or indicating a rate at which the volume of fluid is extracted; and a fluid buildup time parameter configured for controlling or indicating a time for fluid build-up during extracting the fluid from the subsurface environment of interest relative to stabilizing a formation fluid pressure associated with the subsurface environment of interest.

[0190] According to one embodiment, the one or more parameters are associated with: an over balance parameter indicating a difference between mud pressure and formation pressure of the subsurface environment of interest; a formation pressure indicating a fluid pressure of a formation under consideration associated with the subsurface environment of interest; a density parameter indicating a bulk density of rock in the formation under consideration associated with the subsurface environment of interest; a porosity parameter indicating a porosity of the rock in the formation under consideration associated with the subsurface environment of interest; a viscosity parameter indicating a viscosity of fluid filling a pore space in the rock in the formation under consideration associated with the subsurface environment of interest; a horizontal permeability parameter indicating a horizontal component of rock permeability; a vertical permeability versus horizontal permeability ratio parameter indicating a ratio between a vertical permeability and the horizontal permeability of the rock in the formation under consideration associated with the subsurface environment of interest; and a mud compressibility permeability parameter indicating a compressibility of mud filtrate associated with the subsurface environment of interest.

[0191] In some cases, the method outlined in FIG. 10 further comprises generating, based on at least the first pressure curve or the second pressure curve, optimization data for improving executing one or more pressure tests associated with the first pressure curve or the second pressure curve.

[0192] Furthermore, the optimization data can be applied to a machine learning engine to control generation of a third pressure curve associated with a resource site similar to, or distinct from the subsurface environment of interest.

[0193] It is appreciated that the subsurface environment of interest is comprised in, or associated with an oil field.

[0194] The steps in the processing methods described above may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of protection this disclosure.

[0195] Of course, many processing techniques for collected data, including one or more of the techniques and methods disclosed herein, may also be used successfully with collected data types other than seismic data. While certain implementations have been disclosed in the context of seismic data collection and processing, those with skill in the art will recognize that one or more of the methods, techniques, and computing systems disclosed herein can be applied in many fields and contexts where data involving structures arrayed in a multi-dimensional space and / or subsurface region of interest may be collected and processed, e.g., medical imaging techniques such as tomography, ultrasound, MRI and the like for human tissue; radar, sonar, and LIDAR imaging techniques; mining area surveying and monitoring, oceanographic surveying and monitoring, and other appropriate multi-dimensional imaging problems.

[0196] Examples of equations and mathematical expressions have been provided in this disclosure. But those with skill in the art will appreciate that variations of these expressions and equations, alternative forms of these expressions and equations, and related expressions and equations that can be derived from the example equations and expressions provided herein may also be successfully used to perform the methods, techniques, and workflows related to the embodiments disclosed herein.

[0197] While any discussion of or citation to related art in this disclosure may or may not include some prior art references, applicant neither concedes nor acquiesces to the position that any given reference is prior art or analogous prior art.

[0198] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit this disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of the disclosed subject-matter and its practical applications, to thereby enable others skilled in the art to utilize the disclosed techniques and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A method for pressure testing to generate configuration data for energy development, the method comprising:determining distribution data for a subsurface environment of interest, the distribution data comprising probabilistic data values associated with analyzing the subsurface environment of interest based on one or more subsurface parameters;generating, based on the distribution data, a set of test scenarios for the subsurface environment of interest, the set of test scenarios comprising data settings configured to approximate a likelihood that a first pressure test or a second pressure test is implementable at a random position about the subsurface environment of interest based on the one or more subsurface parameters;combining, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data;combining, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data;generating, based on the first combination of fluid rate data and fluid volume data, a first pressure curve for the subsurface environment of interest;generating, based on the second combination of fluid rate data and fluid volume data, a second pressure curve for the subsurface environment of interest;determining, based on the first pressure curve and the second pressure curve, convergence data indicating a similarity between:a first pressure value comprised in the first pressure curve relative to a formation pressure value associated with the subsurface environment of interest, anda second pressure value comprised in the second pressure curve relative to the formation pressure value associated with the subsurface environment of interest;generating, based on the convergence data relative to the one or more subsurface parameters, optimal data values for energy exploration equipment associated with developing the subsurface environment of interest; andinitiating configuring the energy exploration equipment based on the optimal data values.

2. The method of claim 1, wherein:the set of test scenarios for the subsurface environment of interest comprises a plurality pressure tests for the subsurface area of interest, andthe first pressure test and the second pressure test are comprised in the plurality of pressure tests.

3. The method of claim 2, wherein a memoization process is implemented, based on the first pressure curve or the second pressure curve, to exclude the first pressure test or the second pressure test from subsequent pressure test determinations for the subsurface environment of interest relative to other pressure tests comprised in the plurality of pressure tests.

4. The method of claim 2, wherein the first pressure test and the second pressure test are disentangled based on:a first environmental response associated with the first pressure test being unaffected by the second pressure test, anda second environmental response associated with the second pressure test depending on a starting pressure associated with the second test independent of how the starting pressure is reached.

5. The method of claim 2, wherein the first pressure test and the second pressure test are disentangled based on outcome data of the second pressure test depending on a final pressure value associated with the first pressure test and fluid rate data or fluid volume data of the second pressure test.

6. The method of claim 1, wherein the subsurface environment of interest comprises a primary subsurface structure having one or more secondary subsurface structures.

7. The method of claim 6, wherein:the primary subsurface structure comprises a basin, andthe one or more secondary subsurface structures comprise a reservoir.

8. The method of claim 1, wherein the one or more subsurface parameters comprise:a fluid drawdown volume parameter configured for controlling or indicating a volume of fluid extracted from the subsurface environment of interest,a fluid drawdown rate parameter configured for controlling or indicating a rate at which the volume of fluid is extracted, anda fluid buildup time parameter configured for controlling or indicating a time for fluid build-up during extracting the fluid from the subsurface environment of interest relative to stabilizing a formation fluid pressure associated with the subsurface environment of interest.

9. The method of claim 1, wherein the one or more parameters are associated with:an over balance parameter indicating a difference between mud pressure and formation pressure of the subsurface environment of interest,a formation pressure indicating a fluid pressure of a formation under consideration associated with the subsurface environment of interest,a density parameter indicating a bulk density of rock in the formation under consideration associated with the subsurface environment of interest,a porosity parameter indicating a porosity of the rock in the formation under consideration associated with the subsurface environment of interest,a viscosity parameter indicating a viscosity of fluid filling a pore space in the rock in the formation under consideration associated with the subsurface environment of interest,a horizontal permeability parameter indicating a horizontal component of rock permeability,a vertical permeability versus horizontal permeability ratio parameter indicating a ratio between a vertical permeability and the horizontal permeability of the rock in the formation under consideration associated with the subsurface environment of interest, anda mud compressibility permeability parameter indicating a compressibility of mud filtrate associated with the subsurface environment of interest.

10. The method of claim 1, further comprising generating, based on at least the first pressure curve or the second pressure curve, optimization data for improving executing one or more pressure tests associated with the first pressure curve or the second pressure curve.

11. The method of claim 10, wherein the optimization data is applied to a machine learning engine to control generation of a third pressure curve associated with a resource site similar to, or distinct from the subsurface environment of interest.

12. The method of claim 1, wherein the subsurface environment of interest is comprised in an oil field.

13. A system for pressure testing to generate configuration data for energy development, the system comprising:a computer processor, andmemory storing a data processing engine that comprises instructions which are executable by the computer processor to:determine distribution data for a subsurface environment of interest, the distribution data comprising probabilistic data values associated with analyzing the subsurface environment of interest based on one or more subsurface parameters;generate, based on the distribution data, a set of test scenarios for the subsurface environment of interest, the set of test scenarios comprising data settings configured to approximate a likelihood that a first pressure test or a second pressure test is implementable at a random position about the subsurface environment of interest based on the one or more subsurface parameters;combine, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data;combine, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data;generate, based on the first combination of fluid rate data and fluid volume data, a first pressure curve for the subsurface environment of interest;generate, based on the second combination of fluid rate data and fluid volume data, a second pressure curve for the subsurface environment of interest;determine, based on the first pressure curve and the second pressure curve, convergence data indicating a similarity between:a first pressure value comprised in the first pressure curve relative to a formation pressure value associated with the subsurface environment of interest, anda second pressure value comprised in the second pressure curve relative to the formation pressure value associated with the subsurface environment of interest;generate, based on the convergence data relative to the one or more subsurface parameters, optimal data values for energy exploration equipment associated with developing the subsurface environment of interest; andinitiate configuring the energy exploration equipment based on the optimal data values.

14. The system of claim 13, wherein:the set of test scenarios for the subsurface environment of interest comprises a plurality pressure tests for the subsurface area of interest, andthe first pressure test and the second pressure test are comprised in the plurality of pressure tests.

15. The system of claim 14, wherein a memoization process is implemented, based on the first pressure curve or the second pressure curve, to exclude the first pressure test or the second pressure test from subsequent pressure test determinations for the subsurface environment of interest relative to other pressure tests comprised in the plurality of pressure tests.

16. The system of claim 14, wherein the first pressure test and the second pressure test are disentangled based on:a first environmental response associated with the first pressure test being unaffected by the second pressure test, anda second environmental response associated with the second pressure test depending on a starting pressure associated with the second test independent of how the starting pressure is reached.

17. The system of claim 14, wherein the first pressure test and the second pressure test are disentangled based on outcome data of the second pressure test depending on a final pressure value associated with the first pressure test and fluid rate data or fluid volume data of the second pressure test.

18. The system of claim 13, wherein the instructions are executable to generate, based on at least the first pressure curve or the second pressure curve, optimization data for improving executing one or more pressure tests associated with the first pressure curve or the second pressure curve.

19. The system of claim 18, wherein the optimization data is applied to a machine learning engine to control generation of a third pressure curve associated with a resource site similar to, or distinct from the subsurface environment of interest.

20. The system of claim 13, wherein the subsurface environment of interest is comprised in an oil field.

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