Geological Formation Neutron Porosity System

Abstract

A method may include receiving neutron data and density data for a borehole in a geological formation, determining a migration length value for a layer of the geological formation based at least in part on the neutron data, forward modeling at least the layer based at least in part on the migration length value and the density data, and outputting modeled neutron data for the layer based at least in part on the forward modeling.

Description

[0001] Related Applications

[0002] This application claims priority to and the benefit of U.S. Provisional Application Serial No. 62 / 670,166, filed May 11, 2018, which is incorporated herein by reference. Background Art

[0003] A geological formation is formed of a material, which may include a material as a matrix, which may include one or more other materials, such as fluids, solids, etc. A geological formation may include pores within the material matrix, which define a porosity that may be occupied by a material (e.g., a fluid, etc.). Porosity may be determined based on the effect of the formation on fast neutrons emitted by a source. For example, hydrogen may have an effect on neutrons, slowing them down and capturing them. Since hydrogen may be primarily present in the pore fluid, the neutron porosity log primarily responds to porosity; note that other factors may include formation matrix factors, fluid factors, chemical factors, geometric factors, etc.

[0004] Well logs can be calibrated under assumptions about the type of matrix (e.g., limestone, sandstone, dolomite, etc.) and the type of pore fluid (e.g., pores filled with a known material such as fresh water). Porosity can be expressed in porosity units (vol / vol or pu) for the selected matrix. Another method is to express neutron data in counts per second or American Petroleum Institute (API) units. The investigation depth of neutron measurements can be on the order of centimeters to tens of centimeters (e.g., 2 cm to 40 cm, etc.).

[0005] Neutron measurements can be based on phenomena such as thermal or epithermal neutron detection. Thermal neutrons tend to have about the same energy as the surrounding matter (e.g., less than about 0.4 eV), while epithermal neutrons tend to have higher energies (e.g., between about 0.4 and about 10 eV). Accuracy tends to improve with increasing count rate, which tends to occur at low porosity.

[0006] Neutron measurements can be used for interpretation. Interpretation is a process involving the analysis of well log data to identify and locate various subsurface structures (e.g., horizons, faults, geological bodies, etc.) in a geological environment. Various types of structures (e.g., formations) can indicate hydrocarbon traps or flow channels, such as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation can allow for the construction of more accurate models of subsurface regions, which in turn can improve the characterization of subsurface regions for the purpose of resource extraction. The characterization of one or more subsurface regions in a geological environment can guide, for example, the execution of one or more operations (e.g., field operations, etc.). Summary of the invention

[0007] A method may include receiving neutron data and density data for a borehole in a geological formation; determining a migration length value for a layer of the geological formation based at least in part on the neutron data; forward modeling at least the layer based at least in part on the migration length value and the density data; and outputting modeled neutron data for the layer based at least in part on the forward modeling. A system may include a processor; a memory operably coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive neutron data and density data for a borehole in a geological formation; and determining a migration length value for a layer of the geological formation based at least in part on the neutron data; forward modeling at least the layer based at least in part on the migration length value and the density data; and outputting modeled neutron data for the layer based at least in part on the forward modeling. One or more computer-readable storage media may include computer-executable instructions executable to instruct a computing system to: receive neutron data and density data for a borehole in a geological formation; and determine a migration length value for a layer of the geological formation based at least in part on the neutron data; forward model at least the layer based at least in part on the migration length value and the density data; and output modeled neutron data for the layer based at least in part on the forward modeling. Various other devices, systems, methods, etc. are also disclosed.

[0008] This summary is provided to introduce some concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to help limit the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The features and advantages of the described embodiments may be more readily understood by referring to the following description taken in conjunction with the accompanying drawings.

[0010] Figure 1 An example system is shown that includes various components for modeling a geological environment and various devices associated with the geological environment;

[0011] Figure 2 Examples of sedimentary basins, examples of methods, examples of formations, examples of boreholes, examples of drilling tools, examples of conventions, and examples of systems are shown;

[0012] Figure 3 Examples of techniques by which data can be acquired are shown;

[0013] Figure 4 Examples of apparatus are shown, including examples of downhole tools and examples of holes;

[0014] Figure 5 Examples of apparatus are shown, including examples of downhole tools;

[0015] Figure 6 An example of forward modeling and inversion of an earth model with respect to seismic data and acoustic impedance is shown;

[0016] Figure 7 An exemplary graphical user interface is shown that includes plots of data, lithology, and clusters;

[0017] Figure 8 Various geometries of boreholes are shown along with examples of data and examples of grid cell models;

[0018] Fig. 9 An example of a method is shown;

[0019] Fig.10 Examples of graphical user interfaces, various types of data, and models of portions of geological environments are shown;

[0020] Fig.11 An example of a method is shown;

[0021] Fig.12 An example of a method is shown;

[0022] Fig.13 An example of a method is shown;

[0023] Fig.14 An example of a method is shown;

[0024] Fig.15 An example diagram is shown;

[0025] Fig.16 An example of a method is shown;

[0026] Fig.17 An example of a graphical user interface is shown;

[0027] Fig.18 Shown include Fig.17 One of the GUIs, Fig.11 Methods and references Fig.12 An example of the workflow of the method;

[0028] Fig.19 shows an example of a graphical user interface; and

[0029] Fig. 20 Example components of systems and networked systems are shown. DETAILED DESCRIPTION

[0030] This description should not be taken in a limiting sense but is made merely for the purpose of describing the general principles of the embodiments. The scope of the described embodiments should be determined with reference to the appended claims.

[0031] Figure 1 An example of a system 100 is shown that includes various management components 110 to manage various aspects of a geological environment 150 (e.g., an environment including a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geological bodies 153-2, etc.). For example, the management components 110 can allow for direct or indirect management of sensing, drilling, injection, production, etc., with respect to the geological environment 150. Subsequently, more information about the geological environment 150 can become available as feedback 160. (e.g., optionally as input to one or more management components 110).

[0032] exist Figure 1 In the example of FIG. 1 , the management component 110 includes a seismic data component 112, an additional information component 114 (e.g., well / log data), a processing component 116, a simulation component 120, a property component 130, an analysis / visualization component 142, and a workflow component 144. In operation, the seismic data and other information provided by the components 112 and 114 can be input to the simulation component 120.

[0033] In an example embodiment, simulation component 120 may rely on entity 122. Entity 122 may include earth entities or geological objects such as wells, surfaces, terranes, reservoirs, etc. In system 100, entity 122 may include a virtual representation of an actual physical entity reconstructed for simulation purposes. Entity 122 may include an entity based on data acquired via sensing, observation, etc. (e.g., seismic data 112 and other information 114). An entity may be characterized by one or more attributes (e.g., a geometric cylindrical grid entity of an earth model may be characterized by a porosity attribute). Such attributes may represent one or more measurements (e.g., acquired data), calculations, etc.

[0034] In an example embodiment, simulation component 120 can operate in conjunction with a software framework such as an object-based framework. In such a framework, entities can include entities based on predefined classes to facilitate modeling and simulation. An example of an object-based framework is the MICROSOFT.NET framework (located in Redmond, Washington), which provides a set of extensible object classes. In the .NET framework, object classes encapsulate modules of reusable code and associated data structures. Object classes can be used to instantiate object instances for use by programs, scripts, etc. For example, a borehole class can define objects for representing boreholes based on well data.

[0035] exist Figure 1In an example, simulation component 120 may process information to conform to one or more properties specified by property component 130, which may include a property library. Such processing may occur prior to input to simulation component 120 (e.g., consider processing component 116). As an example, simulation component 120 may perform operations on input information based on one or more properties specified by property component 130. In an example embodiment, simulation component 120 may construct one or more models of geological environment 150, which may rely on simulating the behavior of geological environment 150 (e.g., in response to one or more natural or man-made behaviors). Figure 1 In the example of , the analysis / visualization component 142 can allow interaction with the model or model-based results (e.g., simulation results, etc.). As an example, the output from the simulation component 120 can be input to one or more other workflows, as indicated by the workflow component 144.

[0036] As an example, the simulation component 120 may include one or more features of a simulator, such as, for example, the ECLIPSE reservoir simulator (Schlumberger Limited, Houston, Texas), the INTERSECT reservoir simulator (Schlumberger Limited, Houston, Texas), etc. As an example, the simulation component, simulator, etc. may include features for implementing one or more gridless techniques (e.g., solving one or more equations, etc.). As an example, one or more reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., considering thermal processes such as SAGD, etc.).

[0037] In an example embodiment, the management component 110 may include features of a framework such as the PETREL seismic simulation software framework (Schlumberger Limited, Houston, Texas). The components provided by the PETREL framework can optimize exploration and development operations. The PETREL framework includes seismic simulation software components that can output information, such as improving reservoir performance by increasing the productivity of the asset team. By using such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework can be considered an application and can be considered a data-driven application (e.g., where data is input for the purpose of modeling, simulation, etc.).

[0038] In an example embodiment, various aspects of the management component 110 may include additional components or plug-ins that operate according to the specifications of the framework environment. For example, a framework environment such as the OCEAN framework environment (Schlumberger Limited, Houston, Texas) on the market allows additional components (or plug-ins) to be integrated into the PETREL framework workflow. The OCEAN framework environment utilizes .NET tools (Microsoft Corporation, Redmond, Washington) and provides a stable, user-friendly interface to achieve efficient development. In an example embodiment, various components can be implemented as additional components (or plug-ins) that comply with the specifications of the framework environment and operate according to the specifications of the framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0039] As an example, a framework may be implemented within or operably coupled to the DELFI cognitive exploration and production (E&P) environment (Schlumberger Limited, Houston, Texas), which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies such as artificial intelligence and machine learning. As an example, such an environment may provide operations involving one or more computing frameworks. For example, various types of computing frameworks may be utilized in the environment, such as a drilling planning framework, a seismic simulation framework (e.g., PETREL framework, Schlumberger Limited, Houston, Texas), a measurement framework (e.g., TECHLOG framework, Schlumberger Limited, Houston, Texas), a mechanical earth modeling (MEM) framework (PETROMOD framework, Schlumberger Limited, Houston, Texas), an exploration risk, resource and value assessment framework (e.g., GEOX, Schlumberger Limited, Houston, Texas), a reservoir simulation framework (INTERSECT, Schlumberger Limited, Houston, Texas), a surface facility framework (e.g., PIPESIM, Schlumberger Limited, Houston, Texas), a production stimulation framework (MANGROVE framework, Schlumberger Limited, Houston, Texas). As an example, one or more methods may be implemented at least in part via a framework (e.g., computing framework) and / or an environment (e.g., computing environment).

[0040] Figure 1Also shown is an example of a framework 170 that includes a model simulation layer 180 as well as a framework service layer 190, a framework core layer 195, and a module layer 175. The framework 170 may include an OCEAN framework, wherein the model simulation layer 180 is a PETREL model-centric software package that hosts an OCEAN framework application. In an example embodiment, the PETREL software may be considered a data-driven application. The PETREL software may include a framework for model building and visualization.

[0041] As an example, seismic data can be processed using a framework such as the OMEGA framework (Schlumberger Limited, Houston, Texas). The OMEGA framework provides features that can be implemented to process seismic data, for example, by prestack seismic interpretation and seismic inversion. The framework can be scalable so that it can be processed and imaged on a single workstation, a large-scale computing cluster, etc. As an example, one or more techniques, technologies, etc. described herein can be optionally implemented in conjunction with a framework such as, for example, the OMEGA framework.

[0042] The framework for processing data may include features for 2D line and 3D seismic surveys. The module for processing seismic data may include features for prestack seismic interpretation (PSI), optionally pluggable into a framework such as the OCEAN framework. The workflow may be specified to include processing via one or more frameworks, plug-ins, add-ons, etc. The workflow may include quantitative interpretation, which may include performing prestack and poststack seismic data conditioning, inversion (e.g., seismic to properties and properties to synthetic seismic), wedge modeling for thin layer analysis, amplitude and offset (AVO) and amplitude and angle (AVA) analysis, surveys, etc. As an example, the workflow may be intended to output rock properties based at least in part on the processing of seismic data. As an example, various types of data may be processed to provide one or more models (e.g., earth models). For example, consider processing one or more of seismic data, well data, electromagnetic and magnetic tellurium data, reservoir data, etc.

[0043] As an example, a framework may include features for implementing one or more grid generation techniques. For example, a framework may include an input component for receiving information from an interpretation of seismic data, one or more attributes based at least in part on seismic data, well log data, image data, etc. Such a framework may include a grid generation component that processes the input information (optionally in combination with other information) to generate a grid.

[0044] exist Figure 1In the example of , model simulation layer 180 may provide domain objects 182, act as a data source 184, provide presentation 186, and provide various user interfaces 188. Presentation 186 may provide a graphical environment in which applications may display their data while user interface 188 may provide a common look and feel for application user interface components.

[0045] As an example, domain objects 182 may include entity objects, attribute objects, and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, volumes, reservoirs, etc., while attribute objects may be used to provide attribute values ​​as well as data version and display parameters. For example, an entity object may represent a well, where the attribute object provides well log information as well as version information and display information (e.g., to display the well as part of a model).

[0046] exist Figure 1 In the example of , data can be stored in one or more data sources (or data stores, typically physical data storage devices), which can be located at the same or different physical sites and can be accessed via one or more networks. The model simulation layer 180 can be configured to model a project. In this way, a specific project can be stored, where the stored project information can include inputs, models, results, and cases. Therefore, when the modeling session is completed, the user can store the project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of related domain objects.

[0047] exist Figure 1 In the example of, the geological environment 150 may include layers (e.g., strata) including a reservoir 151 and one or more other features (e.g., fault 153-1, geological body 153-2, etc.). As an example, the geological environment 150 may be equipped with any of a variety of sensors, detectors, actuators, etc. For example, the device 152 may include communication circuits for receiving and sending information about one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment for acquiring information, assisting in resource recovery, etc. Other equipment 156 may be located at a location remote from the well site and include sensing, detection, transmission or other circuits. Such equipment may include storage and communication circuits for storing and transmitting data, instructions, etc. As an example, one or more satellites may be provided for purposes of communication, data acquisition, etc. For example, Figure 1 A satellite is shown in communication with a network 155 which may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imaging (eg, spatial, spectral, temporal, radiometric, etc.).

[0048] Figure 1Also shown is a geological environment 150, which optionally includes equipment 157 and 158 associated with a well, the well including a substantially horizontal portion that may intersect one or more fractures 159. For example, consider that a well in a shale formation may include natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination of natural fractures and artificial fractures. As an example, a well may be drilled on a laterally extending reservoir. In such an example, there may be lateral changes in properties, stresses, etc., where an assessment of such changes may help plan, operate, etc. to develop a laterally extending reservoir (e.g., by fracturing, injection, production, etc.). As an example, equipment 157 and / or 158 may include components, one or more systems, etc. for fracturing, seismic sensing, seismic data analysis, assessment of one or more fractures, etc.

[0049] As mentioned, the system 100 can be used to execute one or more workflows. A workflow can be a process that includes multiple work steps. The work steps can operate on well logging data, for example, create new data, update existing data, etc. As an example, one or more inputs can be operated on based on one or more algorithms and create one or more results. As an example, the system can include a workflow editor for creating, editing, executing, etc. workflows. In such an example, the workflow editor can provide a selection of one or more predefined work steps, one or more customized work steps, etc. As an example, the workflow can be a workflow that can be implemented in PETREL software, for example, it can operate on seismic data, seismic attributes. As an example, the workflow can be a process that can be implemented in the OCEAN framework, the DELFI environment, etc. As an example, the workflow can include one or more work steps of accessing a module (e.g., a plug-in (e.g., an external executable code, etc.)).

[0050] Figure 2 An example of a sedimentary basin 210 (eg, a geological environment), an example of a method 220 for model building (eg, for a simulator, etc.), an example of a formation 230, an example of a borehole 235 in the formation, an example of a routine 240, and an example of a system 250 are shown.

[0051] As an example, reservoir simulation, petroleum system modeling, etc. can be applied to characterize various types of subsurface environments, including such Figure 1 One or more operations can be performed in the environment based at least in part on such characterization of one or more subterranean environments (eg, via acquired data, simulations, modeling, etc.).

[0052] exist Figure 2In the present invention, a sedimentary basin 210 as a geological environment includes horizons, faults, one or more geological bodies, and facies formed during a certain geological period. These features are distributed in two or three dimensions in space, for example, relative to a Cartesian coordinate system (e.g., x, y and z) or other coordinate systems (e.g., cylindrical, spherical, etc.). As shown, the model building method 220 includes a data acquisition box 224 and a model geometry box 228. Some data may be involved in building an initial model, and thereafter, the model may be optionally updated in response to model output, time changes, physical phenomena, additional data, etc. As an example, the data used for modeling may include one or more of the following: depth or thickness maps and fault geometry and time of seismic, remote sensing, electromagnetic, gravity, outcrop, and well logging data. In addition, the data may include depth and thickness maps generated by facies changes (e.g., due to seismic unconformities) assumed to follow geological events ("iso" time), and the data may include lateral facies changes (e.g., due to lateral changes in sedimentary features).

[0053] In order to model geological processes, data can be provided, for example, data such as geochemical data (e.g., temperature, kerogen type, organic richness, etc.), timing data (e.g., from paleontology, radiometric dating, magnetic reversals, rock and fluid properties, etc.), and boundary condition data (e.g., heat flow history, surface temperature, paleo-water depth, etc.).

[0054] In basin and petroleum system modeling, quantities such as temperature, pressure, and porosity distribution in sediments can be modeled, for example, by solving partial differential equations (PDEs) using one or more numerical techniques. Modeling can also model geometry with respect to time, for example, to take into account changes resulting from geological events (e.g., deposition of material, erosion of material, movement of material, etc.).

[0055] Modeling frameworks such as the PETROMOD framework (Schlumberger Limited, Houston, Texas) on the market include features for inputting various types of information (e.g., earthquakes, wells, geology, etc.) to simulate the evolution of sedimentary basins. The PETROMOD framework models the petroleum system via inputting various data (e.g., seismic data, well data, and other geological data), for example, to simulate the evolution of sedimentary basins. The PETROMOD framework can predict whether and how to fill the reservoir with hydrocarbons, such as the source and time of hydrocarbon production, migration routes, quantity, pore pressure, and hydrocarbon type under underground or surface conditions. In combination with frameworks such as the PETREL framework, a workflow can be constructed to provide an exploration solution from a basin to a potential scale. Data exchange between frameworks can facilitate the construction of models, data analysis (e.g., PETROMOD framework data analyzed using the PETREL framework function) and the coupling of workflows.

[0056] like Figure 2 As shown, the formation 230 includes a horizontal surface and various underground layers. As an example, the borehole can be vertical. As another example, the borehole can be offset. Figure 2 In the example of , borehole 235 can be considered to be a vertical borehole, for example, where the z-axis normal extends downward from the horizontal surface of formation 230. As an example, tool 237 can be positioned in the borehole, for example, to acquire information. As mentioned, the borehole tool can be configured to acquire electric borehole images. As an example, a full-hole formation micro-imager (FMI) tool (Schlumberger Limited, Houston, Texas) can acquire borehole image data. The data acquisition sequence of such a tool can include running the tool into the borehole with the acquisition pad closed, opening the pad and pressing it against the borehole wall, delivering current into the material defining the borehole while translating the tool into the borehole, and remotely sensing current that is changed by interaction with the material.

[0057] As an example, a borehole can be vertical, deflected and / or horizontal. As an example, a tool can be positioned to obtain information in the horizontal portion of a borehole. Analysis of such information may find holes (vugs), dissolution planes (e.g., dissolution along bedding planes), features associated with stress, dip events, etc. As an example, a tool can obtain information that helps characterize fractured reservoirs, optionally wherein fractures can be natural and / or artificial (e.g., hydraulic fracturing). Such information can help completion, production enhancement treatment, etc. As an example, a framework such as the TECHLOG framework (Schlumberger Limited, Houston, Texas) can be used to analyze the information obtained by the tool. The TECHLOG framework includes: core system features such as BASE, C-Data-API, CoreDB, Real Time, TechData-Plus, TechStat and Viewer; geological features such as Advanced Mapping, Field Maps, Ipsom, K.mod and Wellbore Imaging (Wbi); geomechanical features such as completion geomechanics, pore pressure prediction and wellbore stability; geophysical features such as acoustics and geophysics; rock physics features such as 3D rock physics (3DP), acoustics, nuclear magnetic resonance (NMR), Quanti., Quanti.Elan, TechCore and thin bed analysis (TBA); production features such as cased hole, production logging and wellbore integrity; reservoir engineering features such as fluid contacts, formation pressure, saturation height model (SHM) and TechCore; and shale features such as unconventional and Quanti.Elan.

[0058] Regarding the convention 240 for inclination, as shown in the figure, the three-dimensional orientation of a plane can be defined by its inclination and strike. The inclination is the angle of inclination of a plane relative to a horizontal plane (e.g., an imaginary plane) measured on a vertical plane in a specific direction. The inclination can be defined by magnitude (e.g., also known as angle or amount) and azimuth (e.g., also known as direction). Figure 2 As shown in convention 240, the various angles φ represent angles of downward tilt, for example, from an imaginary horizontal plane (e.g., a flat upper surface); however, the inclination refers to the direction in which the inclined plane tilts (e.g., which can be given relative to an angle, a compass direction, etc.). Figure 2 Another feature shown by the convention is the strike, which is the orientation of the line created by the intersection of the inclined plane and the horizontal plane (e.g., treating the flat upper surface as an imaginary horizontal plane).

[0059] For example, some additional terms related to dip and strike may be applicable to the analysis, depending on the situation, the orientation of the data collected, etc. One term is "true dip" (see, for example, Figure 2 Dip in Convention 240T True dip is the dip of a plane directly perpendicular to strike (see, for example, a plane toward north labeled "strike" and angle α). 90 Another term is "apparent dip" (see, for example, Figure 2 Dip in Convention 240 A The apparent tilt can be the tilt of a plane measured in any direction other than the true tilt direction (see, e.g., φ A Dip as angle α A ); however, the apparent dip may be equal to the true dip (see, for example, φ as the angle α about strike 90 Dip A =Dip T ). In other words, where the term apparent dip is used (e.g., in a method, analysis, algorithm, etc.), for a particular dip plane, the value used for the "apparent dip" may be equivalent to the true dip for that particular dip plane.

[0060] like Figure 2 As shown in the convention 240, the dip of the plane seen in the cross section perpendicular to the strike is the true dip (see, for example, φ as the angle α about the strike). 90 Dip A =Dip T As shown in the figure, the dip angle observed in any other direction in the cross section is the apparent dip angle (see, for example, the A In addition, Figure 2 As shown in convention 240, the apparent inclination angle can be approximately 0 degrees (eg, a horizontal surface extending in the strike direction parallel to the edge of the cutting plane).

[0061] In terms of observing inclination in a wellbore, the true inclination is observed in a well drilled vertically. In a well drilled at any other orientation (or deviation), the observed inclination is an apparent inclination (e.g., which is referred to by some as relative inclination). To determine the true inclination value of a plane observed in such a borehole, as an example, a vector calculation (e.g., based on the borehole deviation) may be applied to one or more of the apparent inclination values.

[0062] As mentioned, another term that finds use in the sedimentological interpretation of borehole images is “relative dip” (e.g., R ). The value of the true dip measured from a borehole image in a rock deposited in a very quiet environment can be subtracted (e.g., using vector subtraction) from the dip in the sand body. In such an example, the resulting dip is called the relative dip and can be found to interpret the sand body orientation.

[0063] Conventions such as convention 240 may be used for analysis, interpretation, attributes, etc. (see, e.g., Figure 1 As an example, various types of features (e.g., sedimentary bedding, faults and fractures, monocline ridges, igneous dikes and sills, metamorphic rock stratification, etc.) can be described in part by dip. As an example, the dip may change spatially as the layer approaches the geological body. For example, consider a salt body that may rise due to various forces (e.g., buoyancy, etc.). In such an example, the dip may tend to rise as the salt body moves upward.

[0064] The data-based interpretation may be directed to identifying and / or classifying one or more subsurface boundaries based at least in part on one or more dip parameters (e.g., angle or magnitude, azimuth, etc.). As an example, various types of features (e.g., sedimentary bedding, faults and fractures, monocline ridges, igneous dikes and sills, metamorphic rock stratification, etc.) may be described at least in part by angle, at least in part by azimuth, etc.

[0065] As an example, equations for oil displacement and migration can be provided, which can be modeled and simulated, for example, relative to a period of time. The migration of oil from raw materials (e.g., primary migration or displacement) may include the use of a saturation model, wherein the migration saturation value controls the displacement. Determination of the secondary migration of oil (e.g., oil or natural gas) can include utilizing the hydrodynamic potential of the fluid and considering the driving force that promotes fluid flow. Such forces can include buoyancy gradients, pore pressure gradients, and capillary pressure gradients.

[0066] like Figure 2 As shown, system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260, and one or more sets of instructions 270. As for one or more computers 254, each computer may include one or more processors (e.g., processing cores) 256 and memory 258 for storing, for example, instructions (e.g., one or more sets of instructions 270) that can be executed by at least one of the one or more processors 256. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, display interfaces (e.g., wired or wireless), etc. As an example, images such as surface images (e.g., satellite, geological, geophysical, etc.) can be stored, processed, communicated, etc. As an example, data can include SAR data, GPS data, etc., and can be stored, for example, in one or more storage devices 252.

[0067] As an example, one or more sets of instructions 270 may include instructions (e.g., stored in memory 258) that are executable by one or more of the one or more processors 256 to instruct system 250 to perform various actions. As an example, system 250 may be configured such that one or more sets of instructions 270 provide for establishing Figure 1 As an example, one or more sets of instructions may be used to perform one or more methods, techniques, etc., for example, the instructions may be Figure 2 One or more of one or more sets of instructions 270.

[0068] As described above, seismic data can be acquired and analyzed to better understand the underground structure of a geological environment. Reflection seismology is used in geophysics, for example, to estimate the characteristics of underground strata. As an example, reflection seismology can provide seismic data representing elastic energy waves (e.g., transmitted by P waves and S waves, with a frequency range of about 1 Hz to about 100 Hz or optionally less than about 1 Hz and / or optionally greater than about 100 Hz). For example, seismic data can be processed and interpreted to better understand the composition, fluid content, extent, and geometry of underground rocks.

[0069] Figure 3 An example of an acquisition technique 340 for acquiring seismic data (e.g., see data 360) is shown. As an example, the system can process the data acquired by the technique 340, for example, to allow sensing, drilling, injection, production, etc. to be managed directly or indirectly with respect to the geological environment. Subsequently, more information about the geological environment can become available as feedback (e.g., optionally as input to the system). As an example, the operation can involve a reservoir present in the geological environment, such as, for example, a reservoir. As an example, a technique can provide information (e.g., as an output) that can specify one or more location coordinates of a feature in the geological environment, one or more characteristics of a feature in the geological environment, etc.

[0070] exist Figure 3, technique 340 may be implemented with respect to a geological environment 341. As shown, an energy source (e.g., a transmitter) 342 may emit energy, where the energy propagates in the form of waves that interact with the geological environment 341. As an example, the geological environment 341 may include a hole 343, and one or more sensors (e.g., receivers) 344 may be positioned in the hole 343. As an example, the energy emitted by the energy source 342 may interact with a layer (e.g., structure, interface) 345 in the geological environment 341, so that a portion of the energy is reflected, which may then be sensed by the one or more sensors 344. Such energy may be reflected as an upward primary wave (e.g., a "primary" or "single" reflection wave). As an example, a portion of the emitted energy may be reflected by more than one structure in the geological environment and is referred to as a multiple reflection wave (e.g., "multiple"). For example, the geological environment 341 is shown as including a layer 347 located below a surface layer 349. Given such an environment and the arrangement of the source 342 and the one or more sensors 344, the energy may be sensed as being associated with a particular type.

[0071] As an example, seismic data may include evidence of multiple reflections from rock interfaces, multiple reflections from water interfaces (e.g., the interface between water and the bottom of rock or its underlying sediments), or multiple reflections from air-water interfaces, etc.

[0072] like Figure 3 As shown, the acquired data 360 may include data related to the downgoing direct arrival wave, the reflected upgoing primary wave, the downgoing multiple reflection wave, and the upgoing multiple reflection wave. The acquired data 360 is also shown along the time axis and the depth axis. As indicated, in a manner that depends at least in part on the characteristics of the medium in the geological environment 341, the wave travels at a certain speed over a certain distance, so that there can be a relationship between time and space. Therefore, the time information associated with the sensed energy can allow the spatial relationship of the layers, interfaces, structures, etc. in the geological environment to be understood.

[0073] Figure 3Also shown is a diagram 380, which illustrates various types of waves including P, SV and SH waves. For example, a P wave can be an elastic body wave or an acoustic wave, in which particles vibrate in the direction of propagation of the wave. As an example, a P wave incident on an interface (e.g., with a direction other than normal incidence, etc.) can produce reflected and transmitted S waves (e.g., "converted" waves). For example, an S wave or shear wave can be an elastic body wave, for example, in which particles oscillate perpendicular to the direction of wave propagation. S waves can be generated by a seismic energy source (e.g., in addition to an air gun). As an example, an S wave can be converted into a P wave. S waves tend to propagate more slowly than P waves and do not propagate through fluids that do not support shear. Typically, the recording of S waves involves the use of one or more receivers operably coupled to the earth (e.g., capable of receiving shear forces relative to time). As an example, the interpretation of S waves can allow rock properties such as fracture density and orientation, Poisson's ratio, and rock type to be determined, for example, by cross-plotting P-wave and S-wave velocities and / or by other techniques.

[0074] As an example of parameters that can characterize the anisotropy of a medium (e.g., seismic anisotropy, etc.), consider the Thomsen parameters ε, δ, and γ. The Thomsen parameter δ can describe the effect of an offset (e.g., short offset). Regarding the Thomsen parameter ε, it can describe the effect of an offset (e.g., long offset) and can be related to the difference between vertical and horizontal compressional waves (e.g., P or P-waves or quasi-compressional waves qP or qP-waves). Regarding the Thomsen parameter γ, it can describe the shear wave effect. For example, consider the effect of a horizontal shear wave with horizontal polarization relative to a vertical shear wave.

[0075] As an example, inversion techniques may be applied to generate a model that may include one or more parameters, such as one or more of the Thomson parameters. For example, one or more types of data may be received and used to solve an inversion problem that outputs a model (e.g., a reflectivity model, an impedance model, a fluid flow model, etc.).

[0076] exist Figure 3In the example of , diagram 390 shows an acquisition device 392 that transmits energy from a source (e.g., a transmitter) and receives reflected energy via one or more sensors (e.g., a receiver) suspended in a straight direction. Since the area includes a layer 393, for example, a geological body 395, the energy emitted by the transmitter of the acquisition device 392 can be reflected from the layer 393 and the geological body 395. Evidence of such reflections can be found in the acquired trace. Regarding the portion of the trace 396, the received energy can be discretized by an analog-to-digital converter operating at a sampling rate. For example, the acquisition device 392 can convert the energy signal sensed by the sensor Q into a digital sample at a rate of approximately one sample per 4ms. Given the speed of sound in one or more media, the sampling rate can be converted into an approximate distance. For example, the speed of sound in rock can be about 5km per second. Therefore, a sampling time interval of approximately 4ms will correspond to a sampling "depth" interval of approximately 10 meters (e.g., assuming a path length from the source to the boundary and from the boundary to the sensor). For example, the duration of a trace may be about 4 seconds; thus, for a sampling rate of one sample at about 4 milliseconds, such a trace will include about 1000 samples, with later acquired samples corresponding to deeper reflecting boundaries. If the 4 second trace duration of the previous example is divided by 2 (e.g., to account for reflections), then for vertically aligned sources and sensors, the deepest boundary depth can be estimated to be about 10 km (e.g., assuming a sound speed of about 5 kilometers per second).

[0077] 4D seismic surveying involves acquiring 3D seismic data at different times in a particular area. Such methods can allow for the evaluation of changes in produced hydrocarbon reservoirs relative to time. As an example, changes can be observed in one or more of fluid position and saturation, pressure, and temperature. 4D seismic data can be considered a form of time-lapse seismic data.

[0078] As an example, seismic surveys and / or other data acquisition can be used in onshore and / or offshore geological environments. With respect to offshore, streamers, submarine cables, nodes, and / or other equipment can be utilized. As an example, nodes can be used as a substitute and / or supplement to submarine cables that have been installed in several regions to acquire 4D seismic data. Nodes can be deployed to acquire seismic data (e.g., 4D seismic data) and are retrievable after acquiring the seismic data. As an example, a 4D seismic survey may require one or more processes for needle logging data repeatability. A 4D survey can include two phases: a baseline survey phase and a monitor survey phase.

[0079] As an example, seismic data may be processed using a technique known as "depth imaging" to form an image of reflection amplitudes in the depth domain (eg, a depth image) for a particular target structure (eg, a geological subsurface region of interest).

[0080] As an example, seismic data may be processed to obtain an elastic model related to the elastic properties of a geological subsurface region. For example, elastic properties such as density, compression (P) impedance, compression velocity (v p ) and shear velocity (v s ), anisotropy, etc. As an example, elastic models can provide various insights into the lithology, reservoir quality, fluids, etc. of the surveyed area.

[0081] Figure 4 An example of a wellsite system 400 is shown (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 400 may include a mud box 401 for containing mud and other materials (e.g., where the mud may be drilling fluid); a suction line 403 that serves as an inlet for a mud pump 404 to pump mud from the mud box 401 so that the mud flows to a vibrating hose 406; a winch 407 for winching up one or more drill lines 412; a riser 408 that receives mud from the vibrating hose 406; and a kelly hose 409 that receives mud from the riser 408. ; one or more goosenecks 410; a traveling block 411; a crown block 413 for carrying the traveling block 411 via a drill line 412; a derrick 414; a kelly 418 or a top drive 440; a kelly drive bushing 419; a rotary table 420; a drill floor 421; a bell 422; one or more blowout preventers (BOPs) 423; a drill string 425; a drill bit 426; a casing head 427 and a flow tube 428 for transporting mud and other materials to, for example, a mud box 401.

[0082] exist Figure 4 In the example system of FIG. 4 , a borehole 432 is formed in a subsurface formation 430 by rotary drilling; note that various example embodiments may also use directional drilling.

[0083] like Figure 4 As shown in the example of FIG. 4 , a drill string 425 is suspended in a borehole 432 and has a drill string assembly 450 including a drill bit 426 at its lower end. As an example, the drill string assembly 450 may be a bottom hole assembly (BHA).

[0084] Wellsite system 400 may provide for the operation of drill string 425 and other operations. As shown, wellsite system 400 includes platform 411 and derrick 414 positioned above borehole 432. As described above, wellsite system 400 may include rotary table 420, wherein drill string 425 passes through an opening in rotary table 420.

[0085] like Figure 4As shown in the example of , the wellsite system 400 may include a kelly 418 and related components, or a top drive 440 and related components. Regarding the example of a kelly, the kelly 418 may be a square or hexagonal metal / alloy rod with holes drilled therein for mud flow paths. The kelly 418 may be used to transfer rotational motion from a rotary table 420 to a drill string 425 via a kelly drive bushing 419, while allowing the drill string 425 to be lowered or raised during rotation. The kelly 418 may pass through the kelly drive bushing 419, which may be driven by the rotary table 420. As an example, the rotary table 420 may include a main bushing that is operably coupled to the kelly drive bushing 419, so that rotation of the rotary table 420 may cause the kelly drive bushing 419 to rotate, thereby causing the kelly 418 to rotate. The kelly drive bushing 419 may include an inner profile that matches the outer profile of the kelly 418 (eg, square, hexagonal, etc.); however, it is slightly larger so that the kelly 418 can move freely up and down within the kelly drive bushing 419 .

[0086] With respect to the example of a top drive, a top drive 440 can provide the functions performed by a kelly and a rotary table. The top drive 440 can rotate the drill string 425. As an example, the top drive 440 can include one or more motors (e.g., electric and / or hydraulic) connected by an appropriate transmission to a short pipe section called a casing shaft, which in turn can be screwed into a protective nipple or the drill string 425 itself. The top drive 440 can be suspended from the traveling block 411 so that the rotating mechanism can move freely up and down the derrick 414. As an example, the top drive 440 can allow drilling with more joint columns than the kelly / rotary table method.

[0087] exist Figure 4 In the example of, mud box 401 can contain mud, and mud can be one or more types of drilling fluids. As an example, a wellbore can be drilled to produce fluids, inject fluids or both (e.g., hydrocarbons, minerals, water, etc.).

[0088] exist Figure 4In the example of FIG. 4 , a drill string 425 (e.g., including one or more downhole tools) may be composed of a series of tubes threaded together to form a long tube, with a drill bit 426 at its lower end. When the drill string 425 is advanced into the wellbore for drilling, at some point in time before or concurrently with drilling, mud may be pumped from the mud box 401 (e.g., or other source) to a port of the kelly 418, or, for example, to a port of the top drive 440, via lines 406, 408, and 409 by pump 404. The mud may then flow through passages (e.g., one or more passages) in the drill string 425 and out of a port located at the drill bit 426 (e.g., see directional arrows). When the mud leaves the drill string 425 via the port in the drill bit 426, it may then circulate upward through an annular region between the outer surface of the drill string 425 and the surrounding wall (e.g., an open borehole, casing, etc.), as indicated by the directional arrows. In this manner, the mud lubricates the drill bit 426 and carries heat energy (e.g., friction or other energy) and formation cuttings to the surface where, for example, the mud (e.g., and the cuttings) can be returned to the mud box 401, for example, for recycling (e.g., for processing to remove the cuttings, etc.).

[0089] The mud pumped into the drill string 425 by the pump 404 may form a mud cake after leaving the drill string 425, which lines the wellbore and, among other functions, reduces the friction between the drill string 425 and the surrounding walls (e.g., borehole, casing, etc.). The reduction in friction may facilitate the advancement or retraction of the drill string 425. During the drilling operation, the entire drill string 425 may be pulled out of the wellbore and optionally replaced with, for example, a new or sharpened drill bit, a smaller diameter drill string, etc. As described above, the action of pulling the drill string out of the hole or replacing it in the hole is called tripping. Depending on the tripping direction, tripping may be referred to as tripping upward or tripping outward or as tripping downward or tripping inward.

[0090] As an example, consider drilling down, where, when the drill bit 426 of the drill string 425 reaches the bottom of the wellbore, the pumping of mud begins to lubricate the drill bit 426 for drilling to expand the wellbore. As described above, the mud can be pumped by the pump 404 into the channel of the drill string 425, and when filling the channel, the mud can be used as a transmission medium to transmit energy, for example, energy that can encode information as in mud pulse telemetry.

[0091] As an example, a mud pulse telemetry device may include a downhole device configured to implement pressure changes in the mud to generate one or more acoustic waves on which information can be modulated. In such an example, information from the downhole device (e.g., one or more modules of the drill string 425) can be transmitted uphole to the uphole device, which can relay the information to other equipment for processing, control, etc.

[0092] As an example, the telemetry device may operate via energy transmission through the drill string 425 itself. For example, consider a signal generator that transmits a coded energy signal to the drill string 425 and a repeater that may receive such energy and repeat the energy to further transmit the coded energy signal (e.g., information, etc.).

[0093] As an example, the drill string 425 may be equipped with a telemetry device 452, which includes a rotatable drive shaft; a turbine wheel mechanically coupled to the drive shaft so that mud can rotate the turbine wheel; a modulator rotor mechanically coupled to the drive shaft so that rotation of the turbine wheel causes the modulator rotor to rotate; a modulator stator mounted adjacent to or near the modulator rotor so that rotation of the modulator rotor relative to the modulator stator generates pressure pulses in the mud; and a controllable brake for selectively braking rotation of the modulator rotor to modulate the pressure pulses. In such an example, an alternator may be coupled to the drive shaft, wherein the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short-circuit the at least one stator winding to electromagnetically brake the alternator, thereby selectively braking rotation of the modulator rotor to modulate pressure pulses in the mud.

[0094] exist Figure 4 In the example of , the uphole control and / or data acquisition system 462 may include circuitry to sense pressure pulses generated by the telemetry device 452 and to transmit the sensed pressure pulses or information derived therefrom for processing, control, etc., for example.

[0095] The assembly 450 of the illustrated example includes a logging while drilling (LWD) module 454 , a measurement while drilling (MWD) module 456 , an optional module 458 , a rotatably steerable system and motor 460 , and the drill bit 426 .

[0096] The LWD module 454 can be housed in a suitable type of drill collar and can include one or more selected types of logging tools. It should also be understood that more than one LWD and / or MWD module can be employed, for example, as represented by module 456 of drill string assembly 450. Where the location of an LWD module is mentioned, as an example, it can refer to a module located at the location of LWD module 454, module 456, etc. The LWD module can include capabilities for measuring, processing, and storing information and for communicating with surface equipment. In the example shown, the LWD module 454 can include a seismic measurement device.

[0097] The MWD module 456 may be housed in a suitable type of drill collar and may include one or more devices for measuring characteristics of the drill string 425 and the drill bit 426. As an example, the MWD tool 454 may include a device for generating electrical power, for example, to power various components of the drill string 425. As an example, the MWD tool 454 may include a telemetry device 452, for example, where a turbine wheel may generate power through the flow of mud; it will be understood that other power sources and / or battery systems may be used to power the various components. As an example, the MWD module 456 may include one or more of the following types of measurement devices: a bit pressure measurement device, a torque measurement device, a vibration measurement device, an impact measurement device, a stick-slip measurement device, a direction measurement device, and an inclination measurement device.

[0098] Figure 4 Also shown are some examples of the types of holes that can be drilled. For example, consider an angled hole 472, an S-shaped hole 474, a deep angled hole 476, and a horizontal hole 478.

[0099] As an example, the drilling operation may include directional drilling, wherein, for example, at least a portion of the well includes a curved axis. For example, consider a radius defining a curvature, wherein the inclination relative to the vertical may vary until an angle between about 30 degrees and about 60 degrees is reached, or, for example, an angle of about 90 degrees or possibly greater than about 90 degrees is reached.

[0100] As an example, a directional well can include several shapes, each of which can be designed to meet specific operational requirements. As an example, the drilling process can be performed when information is transmitted to the drilling engineer and based on the information. As an example, the inclination and / or direction can be modified based on the information received during the drilling process.

[0101] As an example, the deflection of the hole can be achieved in part by using a downhole motor and / or turbine.With respect to the motor, for example, the drill string may include a positive displacement motor (PDM).

[0102] As an example, the system may be a steerable system and include equipment for performing methods such as geosteering. As an example, the steerable system may include a PDM or turbine in the lower portion of the drill string, which is just above the drill bit and a curved short section may be installed. As an example, above the PDM, MWD equipment and / or LWD equipment that provide real-time or near real-time data of interest (e.g., inclination, direction, pressure, temperature, actual weight on the drill bit, torque stress, etc.) may be installed. For the latter, the LWD equipment can send various data of interest to the surface, including, for example, geological data (e.g., gamma ray logs, resistivity, density, and sonic logs, etc.).

[0103] The coupling of sensors provides real-time or near real-time information about the progress of the well trajectory, for example, with one or more well logs that characterize the formation from a geological perspective, which can allow geosteering methods to be implemented. Such methods can include, for example, navigating the underground environment to follow a desired route to reach a desired one or more targets.

[0104] As an example, the drill string may include an azimuthal density neutron (ADN) tool for measuring density and porosity; an MWD tool for measuring inclination, azimuth, and shock; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma-ray related phenomena; one or more variable diameter stabilizers; one or more curved joints; and a geosteering tool, which may include a motor and, optionally, equipment for measuring and / or responding to one or more of inclination, resistivity, and gamma-ray related phenomena.

[0105] As an example, geosteering may include intentional directional control of a wellbore based on downhole geological logging measurements in a manner intended to keep the wellbore directional well within a desired zone, zone (zones) (e.g., a pay zone), etc. As an example, geosteering may include directing the wellbore to keep the wellbore in a particular portion of a reservoir, for example, to minimize breakthrough of gas and / or water, and, for example, to maximize the economic production of the well including the wellbore.

[0106] Reference again Figure 4 , the wellsite system 400 may include one or more sensors 464, which are operably coupled to the control and / or data acquisition system 462. As an example, the one or more sensors may be at a surface location. As an example, the one or more sensors may be located at a downhole location. As an example, the one or more sensors may be located at one or more remote locations within a distance of the order of about one hundred meters from the wellsite system 400. As an example, the one or more sensors may be located at an offset wellsite, where the wellsite system 400 and the offset wellsite are in a common field (e.g., an oil field and / or a gas field).

[0107] As an example, one or more sensors 464 may be provided for tracking a pipe, tracking movement of at least a portion of a drill string, etc.

[0108] As an example, the system 400 may include one or more sensors 466 that can sense signals and / or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 400, one or more sensors 466 may be operably coupled to a portion of the riser 408 through which mud flows. As an example, a downhole tool may generate pulses that may pass through the mud and be sensed by one or more of the one or more sensors 466. In such an example, the downhole tool may include associated circuitry, such as an encoding circuit, that may encode the signal, such as to reduce the need for transmission. As an example, the circuitry on the surface may include a decoding circuit to decode at least a portion of the encoded information transmitted via mud pulse telemetry. As an example, the circuitry on the surface may include an encoder circuit and / or a decoder circuit, and the circuitry downhole may include an encoder circuit and / or a decoder circuit. As an example, the system 400 may include a transmitter that may generate a signal that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

[0109] As an example, one or more portions of the drill string may be stuck. The term "stuck" may refer to one or more varying degrees of inability to move or remove the drill string from the hole. As an example, in a stuck condition, it may be possible to rotate the pipe or lower it back into the hole, e.g., in a stuck condition, it may be impossible to move the drill string axially within the hole, although a certain amount of rotation may be possible. As an example, in a stuck condition, it may be impossible to move at least a portion of the drill string axially and rotationally.

[0110] With respect to the term "stuck pipe," the term may refer to a portion of a drill string that cannot rotate or move axially. As an example, a condition known as "differential sticking" may be a condition in which the drill string cannot move (e.g., rotate or reciprocate) along the axis of the hole. Differential sticking may occur when high contact forces caused by low reservoir pressure, high wellbore pressure, or both are applied over a sufficiently large area of ​​the drill string. Differential sticking may cost time and money.

[0111] As an example, the sticking force can be the product of the pressure differential between the wellbore and the reservoir and the area over which the pressure differential acts. This means that a relatively low pressure differential (delta p) applied over a large working area can be just as effective in sticking the pipe as a higher pressure differential applied over a smaller area.

[0112] As an example, a condition referred to as "mechanical sticking" may be a situation in which drill string movement is restricted or prevented by a mechanism other than differential pressure sticking. Mechanical sticking may be caused by, for example, one or more debris in the hole, wellbore geometry anomalies, cement, keyways, or accumulation of cuttings in the annulus.

[0113] Figure 5An example of an environment 501 is shown, which includes an underground portion 503, wherein a drilling rig 510 is placed at a surface location above a borehole 520. Figure 5 In the example of FIG. 5 , various cable service devices may be operated to perform one or more cable services, including, for example, acquiring data from one or more locations within borehole 520 .

[0114] exist Figure 5 In the example of , the borehole 520 includes a drill pipe 522, a casing shoe, a cable side entry sub (CSES) 523, a wet connector adapter 526, and an open hole portion 528. As an example, the borehole 520 can be a vertical borehole or a deviated borehole, wherein one or more portions of the borehole can be vertical and one or more portions of the borehole can be deviated, including substantially horizontal.

[0115] exist Figure 5 In the example of , CSES 523 includes a cable clamp 525, an isolation seal assembly 527, and a check valve 529. These components can be used to insert a logging cable 530, which includes a portion 532 extending outside the drill pipe 522 to be inserted into the drill pipe 522, so that at least a portion 534 of the logging cable extends inside the drill pipe 522. Figure 5 In the example of FIG. 5 , the logging cable 530 passes through a wet connect adapter 526 and enters an open hole section 528 to a logging string 540 .

[0116] like Figure 5 As shown in the example of FIG. 5 , a logging truck 550 (e.g., a cable service vehicle) can deploy cable 530 under the control of system 560. Figure 5 As shown in the example of , the system 560 may include one or more processors 562, a memory 564 operatively coupled to at least one of the one or more processors 562, instructions 566 that may be stored, for example, in the memory 564, and one or more interfaces 568. As an example, the system 560 may include one or more processor-readable media including processor-executable instructions that may be executed by at least one of the one or more processors 562 to cause the system 560 to control one or more aspects of the equipment of the logging string 540 and / or the logging truck 550. In such an example, the memory 564 may be or include one or more processor-readable media, wherein the processor-executable instructions may be or include instructions. As an example, the processor-readable medium may be a computer-readable storage medium that is not a signal and is not a carrier wave.

[0117] Figure 5Also shown is a battery 570 that can be operably coupled to the system 560, e.g., to power the system 560. As an example, the battery 570 can be a backup battery that operates when another power source is not available to power the system 560 (e.g., via the generator of the cable truck 550, a separate generator, a power line, etc.). As an example, the battery 570 can be operably coupled to a network, which can be a cloud network. As an example, the battery 570 can include smart battery circuitry and can be operably coupled to one or more devices via an SMBus or other type of bus.

[0118] As an example, system 560 can be operably coupled to client layer 580. Figure 5 In the example of , the client layer 580 may include features that allow access and interaction via one or more private networks 582, one or more mobile platforms and / or mobile networks 584, and via a "cloud" 586, which can be considered to include distributed devices forming a network such as a network of networks. As an example, the system 560 may include circuits for establishing multiple connections (e.g., sessions). As an example, the connection can be made via one or more types of networks. As an example, the connection can be a client-server type connection, where the system 560 operates as a server in a client-server architecture. For example, a client can log in to the system 560, in which multiple clients can optionally be processed simultaneously.

[0119] Figure 1 , 2 , 3, 4 and 5 show various examples of devices in various examples of environments. As an example, one or more workflows may be implemented to perform operations using devices in one or more environments. As an example, a workflow may be intended to understand an environment. As an example, a workflow may be intended to drill into an environment, such as to form a borehole defined by the surrounding earth (e.g., rock, fluid, etc.). As an example, a workflow may be intended to obtain data from a downhole tool set in a borehole, wherein such data may be obtained via a drilling tool (e.g., as part of a bottom hole assembly) and / or a cable tool. As an example, a workflow may be intended to support a borehole, such as via a casing. As an example, a workflow may be intended to fracture an environment, such as by injecting a fluid. As an example, a workflow may be intended to produce a fluid from an environment via a borehole. As an example, a workflow may utilize one or more frameworks that are operated at least in part via a computer (e.g., a computing device, a computing system, etc.).

[0120] Figure 6An example of forward modeling 610 and an example of inversion 630 (e.g., inversion) are shown. As shown, forward modeling 610 progresses from acoustic impedance and an earth model of an input wavelet to synthetic seismic traces, while inversion 630 progresses from recorded seismic traces to an estimated wavelet of acoustic impedance and an earth model. As an example, forward modeling can take a model of formation properties (e.g., acoustic impedance that can be obtained from a well log) and combine such information with a seismic wavelength (e.g., a pulse) to output one or more synthetic seismic traces, while inversion can start from recorded seismic traces and take into account the effects of an estimated wavelet (e.g., a pulse) to generate an acoustic impedance at a series of time points (e.g., depths).

[0121] As an example, one method may employ amplitude inversion. For example, an amplitude inversion method may receive the arrival times and amplitudes of reflected seismic waves at multiple reflection points to resolve the relative impedance of the formation defined by the imaged reflectors. Such a method may be a form of seismic inversion for reservoir characterization, which may help generate a model of rock properties.

[0122] As an example, the inversion process can begin with forward modeling, for example, to provide a model of a layer with estimated formation depth, thickness, density, and velocity, which can, for example, be based at least in part on information such as well log information. The model can take into account compressional wave velocity and density, which can be used for inversion of P waves or acoustic impedance. As an example, the model can take into account shear velocity, for example, solving for S waves or elastic impedance. As an example, the model can be combined with a seismic wavelet (e.g., a pulse) to generate a synthetic seismic trace.

[0123] Inversion may aim to generate a "best fit" model by, for example, iterating between forward modeling and inversion while seeking to minimize the difference between one or more synthetic traces and the actual seismic data.

[0124] As an example, a framework such as the ISIS inversion framework (Schlumberger Limited, Houston, Texas) may be implemented to perform the inversion. As an example, a framework such as the Linearized Orthotropic Inversion Framework (Schlumberger Limited, Houston, Texas) may be implemented to perform the inversion.

[0125] As described above, with respect to seismic data, forward modeling may include receiving an earth model of acoustic impedance and inputting wavelets to synthetic seismic traces, while inversion may include developing from recorded seismic traces to estimated wavelets and an earth model of acoustic impedance.

[0126] As an example, another method of forward modeling and inversion can be for measurements acquired at least in part via a downhole tool, where such measurements can include one or more different types of measurements, which can be referred to as multi-physics measurements. As an example, the multi-physics measurements can include logging while drilling (LWD) measurements and / or wireline measurements. As an example, a method can include joint rock physics inversion (e.g., inversion) for interpreting multi-physics logging while drilling (LWD) measurements and / or wireline (WL) measurements.

[0127] As an example, a method may include estimating static and / or dynamic formation properties from various logging while drilling (LWD) measurements (e.g., including pressure, resistivity, sonic, and nuclear data) and / or wireline (WL) measurements, which may, for example, provide at least a formation parameter that characterizes the formation. As an example, one of the methods is performed during drilling, and the LWD measurements may be utilized in a joint inversion to output a formation parameter (e.g., a formation parameter value), which may be used to guide drilling (e.g., to avoid sticking, reduce one or more types of formation damage, etc.).

[0128] In petroleum exploration and development, formation evaluation is performed to interpret data obtained from a drilled borehole to provide information about the geological formations and / or in situ fluids that can be used to evaluate the producibility of the reservoir rock penetrated by the borehole.

[0129] As an example, data used for formation evaluation may include one or more of core data, mud log data, wireline log data (e.g., wireline data), and LWD data, the latter of which may be a source of one or more types of formation evaluation (e.g., particularly when wireline acquisition is operationally difficult and / or economically infeasible).

[0130] Regarding measurement types, these may include, for example, one or more of resistivity, gamma ray, density, neutron porosity, spectroscopy, sigma, magnetic resonance, elastic wave, pressure, and sampled data (e.g., as acquired during drilling for timely quantitative formation evaluation).

[0131] Figure 7An exemplary graphical user interface (GUI) 700 is shown, which includes example graphs of acquired data 712, 714, and 716, actual lithology 720, and computational framework lithology cluster data 742 and 744, which are based at least in part on at least one or more of the acquired data 712, 714, and 716. The process of going from acquired data to computational framework lithology can be referred to as an inversion process. In such a process, the acquired data includes information responsive to energy interactions with materials (e.g., rocks, fluids, etc.) that constitute a subsurface environment. Various types of acquired data can be generated via one or more downhole tools that receive one or more types of energy via one or more types of sensors, where such one or more types of energy depend on one or more types of physical properties of the material. The downhole tool can be a data acquisition tool that acquires data via one or more sensors, where such data can be analyzed to determine one or more types of physical properties of the material in the subsurface environment. The downhole tool can be a "camera" that exists to generate an "image" or "model" of one or more areas in the subsurface environment. For GUI 700, the GUI can be presented via execution of process executable instructions for accessing data, which can include sensor data and / or computer-generated data. Such GUI 700 can be presented to a display and include a graphical control that allows interaction therewith via one or more input devices (e.g., mouse, voice, keystrokes, touch screen, etc.).

[0132] Table 1 below shows some examples of data that may be referred to as "well log" data associated with the calculation and analysis of petrophysics and petrophysical properties.

[0133] Table 1. Some examples of well log data.

[0134]

[0135] In Table 1, regarding rock properties, AI is acoustic impedance, SI is shear impedance, σ is Poisson's ratio; and regarding spontaneous potential (SP), Rw is formation water resistivity, and Rwe is formation water resistivity (without thermal effect).

[0136] Reference again Figure 7 , the acquired data 712 includes density (RHOB) data and neutron porosity (NPHI, etc.) data, the acquired data 714 includes gamma ray (GR) data, and the acquired data 716 includes photoelectric (PEF) data. As shown in Table 1, such data can be used for lithological interpretation. Where TNPH or NPHI is mentioned separately, NPHI or TNPH can be used respectively, or, for example, BPHI can be used.

[0137] With respect to RHOB, it represents the density of the bulk material, including the void spaces or other materials in the volume of the object. In logging, RHOB can be the density of the rock, including the pore spaces filled with fluid, such as measured with a "gamma-gamma" type logging device. Another characteristic term is called electron density, such as from gamma-gamma logging technology that responds to electron density, which for various formation types may be close to the bulk density. Yet another characteristic term is called rock density, for example, as measured by a rock density or gamma-bama type logging tool. As an example, the rock density parameter can be abbreviated as RHOB. Various tools include associated parameters, which can be specified according to various mnemonics. For example, the ECOSCOPE tool can include an RHOB parameter as a bulk density parameter.

[0138] exist Figure 7 In , actual lithology 720 indicates the presence of quartz, smectite, and water in a manner that depends on a dimension such as depth, which can be total depth, measured depth, etc. Figure 7 In the example of , the depth is in feet. According to the computational framework lithology cluster data 742 and 744, the composition of the subsurface material is shown relative to the depth. Such data is generated in order to "replicate" the actual composition of the subsurface material which is usually "unknown". Figure 7 In the example of , for comparison with the computational framework lithology clustering data 742 and 744 , the composition is “known” as indicated by the graph of actual lithology 720 .

[0139] As an example, a method may include acquiring data using one or more downhole tools in a subsurface environment and performing an inversion to generate one or more clusters intended to represent the actual lithology of at least a portion of the subsurface environment. Figure 7 In the example of FIG. 7 , computational framework lithology cluster data 744 more closely matches actual lithology 720 than computational framework lithology cluster data 722. In such an example, cluster data 722 “explains” the acquired data because it estimates the composition of the subsurface environment used by one or more downhole tools to acquire the data (e.g., see acquired data 712, 714, and / or 716).

[0140] As described above, in rock physics, inversion methods can be used to characterize lithology based on one or more well logs. For example, consider a method that includes defining zones in a borehole (e.g., a well), selecting mineral compositions and fluids for each defined zone, and appropriately adjusting physical parameters of the composition (e.g., endpoints).

[0141] In the interpretation workflow, the petrophysicist can iterate through selection and adjustment until the band model fits the data and / or an acceptable match is achieved with the core data. For example, consider that adjustments to one or more endpoints may be applicable to one or more shale components. In a framework such as the TECHLOG framework, techniques such as QUANTI.ELAN (Schlumberger Limited, Houston, Texas), which implements the ELAN solver, can be utilized. Such techniques can be utilized to strive to verify the mineralogy of a layer by examining the reconstruction errors of one or more models. As an example, mineral selection can be made by examining a chart, for example, consider a cross-plot of two well logs. In such an approach, in a bulk density-neutron porosity cross-plot, an expert can identify the presence of gas, limestone, or any anomalous mineral. As an example, such a process can be automated, for example, on a geological layer or electrical phase.

[0142] Regarding the QUANTI.ELAN technology (see, for example, the TECHLOG framework), it makes it possible to implement multi-component inversion models incorporating the ELAN solver, which allows the model to be reapplied to various data and the ability to combine the outputs from several models to improve the accuracy of the results, transfer the model to other projects, use curves, use constants as endpoints, etc.

[0143] Technologies such as QUANTI.ELAN technology can allow initialization of various parameters (e.g., temperature, pressure, salinity, and porosity related parameters), utilization of various resistivity models (e.g., Archie, Dual Water, Juhasz, Waxman-Smits, Simandoux, Indonesia, etc.), utilization of various acoustic models (e.g., Wyllie, Raymer-Hunt-Gardner, Raiga-Clemenceau, field, velocity equations, etc.), utilization of neutron equations (e.g., linear and nonlinear response functions of wireline and / or LWD neutron tools), support for geochemical well logs (e.g., in the form of dry weight, etc.), embedded post-processing (e.g., for calculation of formation properties such as porosity, water saturation (Sw), permeability, etc.), model definition, etc.

[0144] With respect to model definition, for example, a model may be defined using a single mineral group per zone or multiple mineral groups per zone, wherein the groups are automatically switched based on a partition curve that may change with well log phase changes. As an example, the framework may provide interactive parameter management (e.g., for wet clays, etc.). As an example, a model-based solution may be constrained, for example, taking into account one or more constraints based on a priori known information (e.g., taking into account X-ray diffraction (XRD) data, cation exchange capacity (CEC) data, etc.). As an example, volume constraints for single and multiple components may be utilized.

[0145] With respect to output from the framework, array histograms can be generated and presented to a display, for example, to help illustrate data relationships by plotting components against input well log data or well log data residuals. As an example, the framework can provide output Juhasz plots, m* plots, result plots (e.g., mineral volume, Sw, and Φ), computational uncertainties associated with model components and parameter selection, sensitivity analysis with, for example, tornado plots to study the contribution of different parameters in the model, etc.

[0146] Figure 8 Exemplary graphs 814 and 834 and exemplary graphs 818 and 838 are shown for the arrangement of tools in a borehole in a geological formation, as well as a graphical representation of a numerical model 850 of a downhole environment with tools in a borehole. As shown, the data may vary depending on factors such as inclination, borehole trajectory, etc. In particular, data acquired in high-angle boreholes may exhibit "smearing" (e.g., consider high-angle horizontal wells, HaHz). For example, due to the depth of investigation of the measurement, volume measurements obtained at a low angle of incidence relative to the formation boundary will detect adjacent layers before the wellbore crosses the boundary, which may cause the measured depth log curve to have a significant tail, such as compared to a vertical well boundary crossing. If the radial depth of the investigation is less than the axial resolution window, then plotting the log curve with the true formation thickness index will show a steeper transition at the boundary. This is because the measurement is averaged over the radial depth of the smaller investigation length rather than over the longer axial resolution window.

[0147] As shown in figure 814, the longitudinal axis of the logging tool is aligned perpendicular to the boundary between the layers of the formation. In this way, the graph 818 of the logging curve response to depth tends to provide approximately acceptable values ​​for the actual formation property transition at the boundary. As shown in figure 814, the surveyed measurement volume enters the deeper layer before the measured depth point, which is used to construct the plotted graph 818. Therefore, the tool begins to measure the deeper formation before the measured depth point reaches the boundary, which causes the logging curve response to shift before reaching the boundary. As shown in figure 834, where the longitudinal axis of the logging tool is no longer perpendicular to the boundary, that is, relative to the drilling angle, it is called "high angle", and the influence of deeper layers can be seen even at shallower depths. Graph 838 shows both the high angle logging curve response and the vertical logging curve response relative to the steep transition at the boundary to show how tailing occurs and how to determine the boundary with respect to depth is more challenging.

[0148] For the abrupt transition shown, it may be referred to as a "square". As an example, a method may include generating one or more "square logs" using one or more types of well log responses (e.g., well log measurements, etc.). A square log may be defined as a well log in which the readings change abruptly with depth. A square log may be an approximation of a true well log in which a varying input well log has been approximated by constant values ​​and mutations. A square log may contain less data than a true well log but may be useful for further processing, analysis, etc.

[0149] As an example, a method can utilize a model, which can be a forward model. Such a model can be a numerical model of a grid or grid including an underground environment, which can take into account the offset between the longitudinal axis of the tool and the longitudinal axis of the borehole. In such an example, the tool can be eccentric to the borehole, as shown in the graphical representation of model 850. Such a model can be a finite element, finite difference or other type of model. As shown, at least a portion of the area outside the borehole can be discretized, where physics-based equations can be expressed, for example, to take into account the energy emitted and the energy received and the characteristics of the area. As an example, a forward model can be used to predict the well logging curve response. As an example, a forward model can be utilized in a cycle, for example, where one or more parameters, values, etc. change, and where the results of the forward model are compared with the actual well logging curve response.

[0150] As an example, a method may include: acquiring well logs in a HaHz well to identify locations where the well path intersects layer boundaries; using image well logs, if available and / or as needed, to calculate true and relative dips at each boundary intersection; constructing a structural model using boundary locations and dips; if necessary, adding boundaries located below the wellbore (e.g., unintersected boundaries) to the model (e.g., the spatial location of one or more such boundaries may be estimated based on one or more offset well logs); populating each layer in the structural model with well log property values ​​(e.g., gamma ray, vertical resistivity, horizontal resistivity, etc.), wherein, for layers that intersect the wellbore, using measured well logs ... curves to provide an initial estimate of property values, and for layers not intersected by the wellbore, initial property values ​​are entered (e.g., manually by a user); lateral property boundaries are appropriately inserted into the model, for example, when the same layer is intersected multiple times, property changes may be observed along the well path; forward model (FM) log curves are calculated, where the FM log curves are a function of the structural model and layer properties; the FM log curves are compared to the measured log curves; the details of the structural model and / or layer properties are adjusted until an acceptable agreement is achieved between the measured log curves and the FM log curves; and the petrophysical evaluation is performed using the layer properties determined from the workflow (e.g., optionally instead of the measured log curves).

[0151] In such a workflow, one or more graphical user interfaces may be implemented that present information to one or more displays, which may be touch screens or other types of displays. As an example, a user may utilize an input device (e.g., a mouse, a stylus, a finger, etc.) to make one or more adjustments in a process that is intended to output layer characteristics based at least in part on measured information, such as, for example, measured via one or more downhole tools (e.g., wireline, LWD, etc.).

[0152] Fig. 9An example of a method 900 is shown, which includes an initialization block 910, a calculation block 920 for calculating a forward model well log, a comparison block 930 for comparing the forward model well log with a measured well log, a decision block 940 for determining whether an acceptable agreement exists (e.g., via one or more metrics, etc.), and an output block 950 for outputting layer properties for reservoir evaluation (e.g., one or more reservoir operations, etc.). As shown, where the decision block 940 determines that the agreement is not acceptable, the method 900 can continue at an adjustment block 960 to adjust one or more layer positions and / or adjust one or more layer properties. As shown, the method 900 can then continue at the calculation block 920, for example, in an iterative loop, until the decision block 940 determines that the agreement is acceptable. In such an example, one or more criteria can be used to determine whether the agreement is acceptable. For example, consider an error criterion that depends on the mismatch error between the forward model well log and the measured well log. As an example, a loop may include an iteration criterion, such as where the loop terminates after a number of iterations where the error is not incrementally reduced (eg, below a desired amount, etc.).

[0153] The foregoing method may be part of an interpretation workflow, such as a multidimensional rock physics interpretation workflow, which determines the location and properties of various layers surrounding a borehole (e.g., a well or borehole, etc.). As explained, a user may begin by modeling the subsurface geometry to generate a structural model (e.g., see Figure 8 The structural model is then populated with well log property values ​​(e.g., one or more of gamma ray, neutron porosity, density, etc.). As an example, well logs recorded in the borehole may first be used to provide an initial estimate of the property values. Such well logs may be or include, for example, LWD logs (e.g., acquired while drilling the borehole).

[0154] As described above, a forward model (FM) log can be calculated and compared to a measured log, in which the structural model can be adjusted until an acceptable agreement is reached between the measured log and the FM log (e.g., a "model, compare, update" workflow). The determined layer properties can be used for one or more purposes, such as petrophysical evaluation. Such layer properties can be utilized instead of using measured logs.

[0155] Fig.10An example of a graphical user interface (GUI) 1000 is shown, which includes example graphs 1001, 1010, 1020, 1030 and 1040 of a HaHz well interpretation interface after several iterations of a modeling-comparison-update workflow. Graph 1001 is synthetic image data generated from synthetic well log responses via forward modeling, where the forward modeling achieves an acceptable match between synthetic well log responses and real well log responses. Graph 1010 includes image data, graph 1020 includes vertical resistivity (RV or Rv) and horizontal resistivity (RH or Rh) data, and graph 1030 includes GR data. Graph 1040 includes a borehole trajectory in true vertical depth (TVD or TVDSS, for example, as measured according to mean sea level (MSL)) relative to measured depth (MD) space, and various resistivity zones with different values, where reservoirs and some property boundaries are delineated.

[0156] GUI 1000 indicates that the borehole can be characterized at least in part as "high angle", for example, as tailing can be seen in various data plotted by log curve response versus measured depth (MD). The image data of graph 1010 indicates tailing, as does the data of RV and RH graphs 1020 and the data of GR graph 1030. In graphs 1020 and 1030, a square log curve is shown, for which a corresponding vertical line is shown in graph 1040. In GUI 1000, a logging tool graphical control can be navigated along the trajectory, thereby providing one or more indicators for the corresponding data. For example, consider moving a cursor along the borehole trajectory while viewing the log curve response and / or the square log curve of one or more of graphs 1010, 1020 and 1030.

[0157] exist Fig.10In the example of , agreement has been reached between the forward model and the measured well log curves, thereby determining the characteristics of the structural model and the assigned layers. Graph 1001 is synthetic image data and appears at the top of GUI 1000, where the synthetic image data is generated by a gamma ray forward model (e.g., a forward model for gamma ray well log curve response). Note that the features shown on the forward model gamma ray image graph 1001 are very consistent with the features shown on the measured density image graph 1010. The article entitled "Formation Evaluation in High Angle and Horizontal Wells–A New and Practical Workflow" by Griffiths et al., Society of Petrophysicists and Well Log Analysts (SPWLA) 53rd Annual Logging Symposium, June 16-20, 2012, is incorporated herein by reference.

[0158] exist Fig.10 In FIG. 1 , as mentioned, the reservoir and the trajectory of the borehole are shown. As an example, Fig.10 At least a portion of the results shown in can be used to control the drilling process. For example, such a method may include controlling the direction of a drill bit of a drill string via a drilling rig. In such an example, the control may be intended to drill into or into a reservoir.

[0159] When modeling neutron porosity, structural model property values ​​initialized with measured well logs are often not suitable for direct use in the forward modeling process, which may make neutron modeling complicated and time-consuming for the interpreter (e.g., for the interpretation process).

[0160] As an example, a method can process neutron data in a manner that facilitates interpretation. As an example, such a process can be "hidden" so that a user cannot see how the data is processed. In such an example, a user can be exempted from performing tasks associated with data preparation of neutron data. Such a method can make neutron data more like one or more other types of data, thereby helping to simplify workflows. For example, a user can run a "model, compare, update" workflow by modeling neutron measurements like one or more other measurements.

[0161] To forward model neutron porosity, one approach may include utilizing a migration length (Lm) as an input. However, various sensors (e.g., downhole tool sensors) do not provide Lm data; instead, they measure the flux of neutrons. As an example, the neutron flux may be measured by a sensor of a neutron tool, where, after processing, the measurement may be expressed as a count rate. As an example, a detector parameter may be the natural logarithm of the neutron count rate (Ln). As an example, a measured well log that may be used to initialize the neutron porosity property may be the best thermal neutron porosity (BPHI) or the thermal neutron porosity (TNPH or NPHI), each of which cannot be directly converted to Lm.

[0162] A neutron porosity tool may be a tool that can make measurements that are sensitive to the amount of hydrogen (e.g., and to a lesser extent, other elements) in a formation. It may be assumed that elements other than hydrogen contribute negligibly to the measurement and that the hydrogen measurement is from the fluid occupying the pore spaces of the formation. However, in the formation, elements other than hydrogen may be present in the material matrix and contribute to the measurement (e.g., chlorine in formation water, chlorine in mud filtrate, and certain evaporative formations, etc.); note that hydrogen may be present in the material matrix (e.g., bound water in shale). As an example, the tool and / or method may be calibrated so that porosity is given in particular units (e.g., limestone units, etc.). Pure limestone saturated with fresh water may be used as the basis for the units because it tends to provide a measurement signal of hydrogen that is indicative of hydrogen in the pores of the limestone. As an example, one or more "corrections" or adjustments may be made for other lithologies or other fluids. As an example, the measurements of a neutron porosity tool may be affected by various effects that may originate from hydrocarbons, shales, and / or chlorides. As discussed with respect to Figure 7 As explained, neutron well logs may be used to determine lithology. Neutron well log responses may vary relative to lithology and / or one or more of the effects described above. As an example, a method may include modeling one or more subsurface neutron porosity characteristics of one or more subsurface layers, wherein the modeling provides accurate corrections (e.g., accounting for adjustments to one or more factors). Some tool manufacturers use calibration curves, calibration charts, etc., however, such methods often require users to access such curves, charts, etc., and require an iterative manual process (e.g., layer by layer), which typically results in a rough approximation. For example, a chart for obtaining porosity values ​​for some lithologies other than limestone may include values ​​for adjusting apparent limestone porosity units to porosity units of silica and dolomite.

[0163] Calibration may include adjustments to the well log measurements to return them to standard conditions for the characterized tool. Neutron porosity measurements may involve corrections for, for example, temperature, pressure, and a number of borehole and formation parameters.

[0164] As an example, a method can include utilizing a computing framework in which a modeling method can be initiated automatically and / or through user interaction (e.g., mouse, touch screen, voice command, etc.) to generate neutron porosity property values ​​for a subsurface formation (e.g., as a well log, etc.), wherein the modeling provides accurate corrections (e.g., adjustments) to neutron tool measurements.

[0165] For neutron tools, the tool can measure the size of the neutron cloud, expressed as a function of a neutron macro parameter, migration length (Lm). For porosity tools, macro parameters can include, for example, slowing length (e.g., when considering epithermal neutrons) and migration length (e.g., when considering thermal neutrons). Composition, geometry, and energy can be averaged. As described above, neutron "porosity" tools cannot directly measure porosity; instead, such tools can measure the neutron migration length (Lm), which can then be used to determine porosity (e.g., or can be used directly as a proxy for estimating porosity). As an example, a macro parameter model can be used to determine the migration length from various types of information (e.g., average porosity, matrix, and fluid type). The SUNPAR program (Schlumberger Limited, Houston, Texas) is an example of a program that can be executed using computing resources to generate macro parameters for various neutron and gamma ray transmission tools. Once the macro parameters are understood, they can be mapped to the count rate ratio response for a specific tool design. As an example, the mapping may utilize a regression analysis of laboratory data (eg, performed using a specific tool, etc.).

[0166] As an example, to provide Lm, a method may include performing a conversion from a BPHI or TNPH and a density property to Lm. Such a method may be referred to as a reverse process, where Lm is iteratively obtained by adjusting Lm to give an acceptable match on a BPHI or TNPH target value. Given such a method, a user may directly model using measured well logs (e.g., BPHI or TNPH), where the complexity of processing Lm may be hidden (e.g., without presenting corresponding execution details to a display, etc.). For the user, the modeling of neutron porosity may be simplified, e.g., similar to the modeling of another property. As an example, such a method may be implemented in a framework such as a TECHLOG framework (e.g., 3DP, etc.). For example, the framework may include one or more graphical controls that cause a dialog window to be presented that includes various options that may be selected by a user and / or optionally pre-populated (e.g., as defaults, for predetermined or pre-known tool types, etc.). As an example, consider GUI 1000 to include one or more functions that may be initiated via input to a computing system (e.g., via a touch screen, mouse, stylus, voice command, etc.) to present a dialog window to provide a conversion from BPHI or TNPH and density characteristics to Lm.

[0167] As an example, measurements acquired in a borehole can be used to define the stratigraphic geometry surrounding the borehole and fill in various layer properties. External sources of geometric information such as geological models or remote bed boundary inversion maps can be used to enhance the understanding of the stratigraphic geometry surrounding the borehole.

[0168] Interpretation aims to determine the location and properties of various layers around the borehole. The interpretation workflow may include the following: First, the well logs recorded in the borehole are used to identify the locations where the borehole path intersects layer boundaries; if available, the image well logs are used to calculate the true and relative dips at each boundary intersection; the boundary locations and dips are then used to construct a structural model; the well log property values ​​(for example, one or more of gamma ray, vertical resistivity, horizontal resistivity, density, neutron porosity, and sigma) are then populated in the various layers of the structural model; for layers intersected by the borehole, the measured well logs are used to provide an initial estimate of the property values; for layers not traversed by the borehole, the user can manually enter initial property values, where offset well logs (from one or more offset well logs) are used to calculate the true and relative dips at each boundary intersection; the boundary locations and dips are then used to construct a structural model; the various layers in the structural model are then populated with well log property values ​​(for example, one or more of gamma ray, vertical resistivity, horizontal resistivity, density, neutron porosity, and sigma); for layers intersected by the borehole, the measured well logs are used to provide an initial estimate of the property values; for layers not traversed by the borehole, the user can manually enter initial property values, where offset well logs (from one or more offset well logs) are used to calculate the true and relative dips at each boundary intersection; the boundary locations and dips are then used to construct a structural model ... the structural model; the boundary locations and dips are then used to construct the structural model; the boundary locations and dips are then used to construct the structural model; the boundary locations and dip well movement, etc.) can provide an indication of the properties of non-intersecting layers; then, a forward model (FM) log is calculated, where the FM log is a function of the actual borehole trajectory, the structural model, and the layer properties; the FM log is then compared to the measured log; the details of the structural model and the layer properties are then adjusted (e.g., manually and / or automatically by a user) until a good agreement is reached between the measured log and the FM log, where a method can be used to build a model, which may be the simplest model, that is consistent with the log; in such an example, the layer properties can be determined from the aforementioned workflow and then used for rock physical evaluation (e.g., instead of the measured log).

[0169] With respect to neutron porosity modeling, the neutron forward model in 3DP can work in terms of neutron migration length (Lm) and count rate. That is, a migration length (Lm) property can be assigned to a layer of a formation. The output of the forward model can be the near and far count rates expected from the neutron tool when the neutron tool passes through the layer in a given rotation trajectory. Since the neutron porosity measurement may depend on lithology, density, and fluid hydrogen index (HI), these factors can be considered when forward modeling the neutron porosity response. Users may be accustomed to using neutron porosity (TNPH or BPHI), which is a standard measurement used in rock physics analysis, and as described above, a method can enable 3DP users to use more familiar neutron porosity instead of migration length or count rate. For example, the GUI may include a graphical control that allows one or more of those more familiar neutron porosities to be generated. As an example, such a GUI can be set to automatically perform operations so that the GUI presents one or more neutron porosities (e.g., as opposed to presenting a migration length or count rate).

[0170] When the neutron porosity properties are initialized, a neutron porosity square log curve can be generated for each desired layer to be analyzed; and it can be equal to the average or minimum / maximum value (e.g., selected by the user or by default) of the input measured log curves located between the entry and exit points of the trajectory of the layer.

[0171] Fig.11 An example of a method 1100 is shown, which includes receiving boxes 1114 and 1118 for receiving an average density per depth and a neutron square log curve, respectively, a migration length box 1120 for calculating a migration length (Lm) based at least in part on an output of the neutron square log curve receiving box 1118, a forward modeling module 1130 for performing forward modeling based at least in part on the output of the migration length box 1120 and the average density per depth of the receiving box 114, and an output box 1140 for outputting forward modeling information (e.g., BPHI or TNPH).

[0172] Fig.11 Method 1100 may include receiving neutron data and density data for a borehole in a geological formation for each box 1114 and 1118; determining a migration length value for a layer of the geological formation based at least in part on the neutron data for each box 1120; forward modeling at least the layer based at least in part on the migration length value and density data for each box 1130; and outputting modeled neutron data for the layer for each box 1140 based at least in part on the forward modeling.

[0173] Fig.11 Various computer readable media (CRM) blocks 1115, 1119, 1121, 1131, and 1141 are also shown. Such blocks may include instructions that may be executed by one or more processors, which may be one or more processors of a computing framework, system, computer, etc. A computer readable medium may be a computer readable storage medium that is not a signal, is not a carrier wave, and is non-transitory. For example, a computer readable medium may be a physical memory component that can store information in a digital format. As an example, Figure 2 The instructions 270 of the system 250 may include instructions of one or more of the CRM blocks 1115, 1119, 1121, 1131, and 1141 so that the system 250 may perform Fig.11 As an example, one or more application programming interfaces (APIs) may be used to make API calls to perform one or more actions of method 1100. For example, a client device may make an API call to a server via a network to perform Fig.11 As an example, a graphical user interface (GUI) may be used to call Fig.11Execution of one or more actions of method 1100 (see, e.g. Fig.18 , Fig.19 etc.), which may optionally be via one or more API calls or one or more other types of calls, commands, instructions, etc.

[0174] like Fig.11 As shown, the migration length (Lm) can be calculated via an iterative algorithm using layer density and neutron properties and one or more assumptions about conditions. As shown, the forward model can provide layers of formations with Lm properties, a sensitivity function that calculates the convolution Lm for each depth, and the convolution Lm and average density used to calculate various detector parameters and then calculate the near count rate and far count rate. As shown, the output can be processed via a tool neutron algorithm that calculates the BPHI or TNPH for the forward modeling under one or more assumptions about conditions.

[0175] As an example, the neutron flux may be measured by a sensor of a neutron tool, wherein, after processing, the measurement may be expressed as a count rate. As an example, the detector parameter may be the natural logarithm of the neutron count rate (Ln).

[0176] As mentioned, a method may include utilizing a computing framework wherein a modeling method may be initiated automatically and / or through user interaction (e.g., mouse, touch screen, voice command, etc.) to generate subsurface formation neutron porosity property values ​​(e.g., as well logs, etc.), wherein the modeling provides accurate corrections (e.g., adjustments) to neutron tool measurements. Fig.11 In the example of FIG. 1100 , method 1100 may provide neutron porosity property values, such as BPHI and / or TNPH (see also, e.g., Fig.18 Workflow 1800). Such a method can provide Lm-based neutron porosity property values ​​(e.g., via determining Lm values). For example, a method can convert measured neutron property data into Lm-based neutron porosity values. As an example, the output can be in the form of a square log curve, such as a BPHI square log curve or a TNPH square log curve. As explained, the layer-based method can take into account geometric effects, such as those that can affect HaHz borehole neutron tool measurements (e.g., taking into account tailing). As an example, the computing framework can include a graphical user interface that can be used to process neutron tool measurements for multiple layers and output one or more types of neutron porosity property log curves (e.g., BPHI, TNPH, BPHI square, TNPH square, etc.).

[0177] Fig.12An example of a method 1200 is shown, which can be referred to as a binary search workflow. As an example, the binary search can include selecting between alternatives (binary search). The method 1200 includes an input 1212 for receiving a layer density and an input 1214 for receiving a layer neutron porosity (e.g., BPHI or TNPH). The method 1200 includes a core process 1240, which can utilize the inputs 1212 and 1214 to generate an output 1262 for an output of a layer Lm, which can be, for example, in Fig.11 is used in method 1100 (e.g., see migration length (Lm) block 1120). Fig.12 As shown, the core process 1240 can receive the Lm layer search range 1242, select the Lm layer candidate value (candidate) 1244, perform the porosity square calculation (PoroSqCmp) 1246, generate a simulated layer neutron porosity (e.g., BPHI and / or TNPH) 1248, and compare between the simulated layer neutron porosity 1248 and the received layer neutron porosity at the input terminal 1214, so that the decision box 1254 can determine whether there is an acceptable match (e.g., see the "yes" branch), for example, whether to update the search range (e.g., see the "no" branch). As shown, in the event that there is an acceptable match, the core process 1240 of the method 1200 can output the layer Lm via the output 1262. As an example, the output 1262 of the core process 1240 can be operatively coupled to Fig.11 Block 1120 of method 1100 (see, e.g., Fig.18 Workflow 1800).

[0178] like Fig.12 As shown, the inputs may include output layer density and layer neutron porosity (e.g., BPHI or TNPH) for layer Lm. The output may be based on various calculations, including porosity square calculation (PoroSqCmp), simulated layer neutron porosity calculation, and comparison of simulated and input BPHI or TNPH. In the event that the comparison is acceptable (e.g., the error is less than a predetermined amount, etc.), method 1200 may output layer Lm, which may be from a search range for layers and candidate values ​​selected within the search range, as shown. In the event that the comparison is unacceptable, method 1200 may, for example, update the search range for Lm.

[0179] Fig.13 An example of method 1300 is shown, which includes Fig.12The method 1300 includes receiving blocks 1314 and 1318 for receiving layer density characteristics and layer neutron characteristics, respectively, a layer Lm estimation block 1342 for estimating layer Lm, a separate detector parameter block 1344 for determining individual detector parameters based at least in part on the layer Lm estimate and the layer density characteristics, a near and far detector count rate block 1346 for calculating near and far detector count rates, and a BPHI or TNPH (e.g., BPHI and TNPH) for each layer (e.g., based on the count rate and layer density characteristics). The method 1300 may further include a forward modeling module 1348 for comparing the forward modeling results with the received layer neutron characteristics, a comparison box 1350 for comparing the forward modeling results with the received layer neutron characteristics, a decision box 1352 for determining whether the comparison is acceptable (e.g., regarding one or more differences), and an output box 1360 for outputting the layer Lm estimate for each layer if the comparison is acceptable; otherwise, the method 1300 may continue to an adjustment box 1354 for adjusting the layer Lm estimate, which may be performed in one or more ways to provide the layer Lm estimate to the respective detector parameter box 1344. Fig.13 As shown, layer density and neutron porosity properties are used for neutron forward modeling. Methods 1200 and 1300 may be referred to as inversion methods (eg, inversion of information for determining Lm properties).

[0180] As an example, the workflow may include initialization of a layer migration length (Lm) characteristic, where for each layer, the layer Lm characteristic may be determined using the initialized layer neutron porosity characteristic and the layer density, which may be accomplished via inversion (e.g., see methods 1200 and 1300). The inversion method may use a neutron forward modeling algorithm. The inversion may minimize the difference between the layer neutron porosity characteristic (e.g., as based on TNPH or BPHI) and the forward modeled layer TNPH / BPHI. Fig.12 and 13 As shown, the Lm value corresponding to the lowest difference can be identified as the input layer Lm for the next step, in which a loop iteration can be performed until the comparison is acceptable (e.g., according to one or more criteria). As an example, the inversion algorithm can assume that the thickness of a single layer is infinite.

[0181] As an example, the layer Lm can be provided to the 3D sensitivity function along with the well trajectory to determine the convolved Lm per depth. The convolved Lm can represent the sum of the contributions of the individual grid cells of the grid cell model surrounding the tool (e.g., as defined in the sensitivity function) to the measured signal. Regarding the grid cell model, consider Figure 8 A graphical representation of the model 850 is shown as an inset at the lower right of the grid cell model, with grid cells that can be used to model the area surrounding the tool.

[0182] The convolved Lm along with the mean density can be used to calculate detector parameters. The detector parameters can be the natural logarithm of the neutron count rate (Ln), which can be calculated for near and far detectors. Forward modeled neutron count rates can be calculated for near and far detectors accordingly. These can be repeats of the measured detector count rates under standard conditions for characterizing the tool (e.g., fresh water filled limestone, 8 inch borehole, 60F temperature, zero mud and formation salinity for the ECOSCOPE 675 tool).

[0183] The forward modeled count rates may be provided to a neutron tool processing algorithm, which may ultimately provide a forward modeled TNPH and / or BPHI, wherein the algorithm may employ standard conditions.

[0184] As an example, BPHI may be a type of hydrogen index (HI) related parameter such as "Best Thermal Neutron Porosity (Ratio Method)" associated with a tool such as the ECOSCOPE tool (Schlumberger Limited, Houston, Texas). The BPHI measurement may include a full correction of the remote count rate for density effects. TNPH may be "Thermal Neutron Porosity (Ratio Method)" associated with a tool such as the ECOSCOPE tool. As described above, NPHI may be utilized (e.g., see the mnemonics of Schlumberger Limited of Houston, Texas, where the NPHI parameter may be a mnemonic for "Thermal Neutron Porosity in Selected Lithology (Raw Ratio Method)"). The aforementioned parameters BPHI, TNPH, and NPHI are neutron porosity property parameters (e.g., neutron porosity properties).

[0185] The migration length (Lm) represents the combination of the path the neutron travels during the deceleration phase (Ls) and the distance it travels during the thermal phase before being captured (Ld):

[0186] Lm=Ls 2 +Ld 2

[0187] The slowdown length Ls is proportional to the root mean square distance covered by a neutron between the time it is emitted from the source at high energy and the time it reaches a much lower energy, such as the lower edge of the epithermal neutron energy region.

[0188] The diffusion length Ld can be thought of as the modified distance a thermal neutron travels between the point where it becomes hot and its eventual capture.

[0189] The migration length (Lm) is a parameter characteristic of the formation lithology and fluid content given a known source-to-detector spacing. It can be used internally by the algorithm as a stratigraphic property of the layered earth model during forward modeling.

[0190] The Lm-based neutron porosity forward model can convert the migration length (Lm) layer properties and the density layer properties into TNPH or BPHI properties. As an example, the model used in the forward modeling can include one or more layers. As an example, a multi-layer model of the formation can be used. Fig.10 An example of a multi-layer model is shown, which includes various features, feature boundaries, and trajectories of drill holes that may cross one or more layer boundaries. As shown, the trajectory passes through one or more layers of the reservoir (e.g., oil-producing zones, etc.). Various types of data are shown in Fig.10 Also shown are well logs, such as those acquired by one or more sensors of one or more downhole tools.

[0191] Fig.14 A schematic diagram 1400 of an example of a conversion process for calculating BPHI or TNPH from Lm is shown. As described above, a method can provide a neutron porosity property value based on Lm (e.g., via determining an Lm value). For example, a method can convert measured neutron property data to a neutron porosity value based on Lm. Fig.14 In the example of , Lm is populated in each layer and the borehole in the layered earth model can be convolved using a 3D flux sensitivity function (FSF) map. A technique called diffusion adjustment can be used, where a separate set of maps becomes a DFD, and smaller adjustments are made to the convolved Lm. As shown, a tool parameter ROBB can be utilized, which corresponds to a tool such as the ECOSCOPE tool (e.g., ROBB is an acronym for Volume Density, Lower Quadrant; and ROBU is an acronym for Tool Face Volume Density, Upper Quadrant). As an example, a tool can output values ​​for parameters such as LQC.RHOB and LQC.ROBB, where LQC is an acronym for log quality control (e.g., consider the parameters of the ECOSCOPE tool).

[0192] As an example, detector specific count rates can be calculated for near and far detectors from the convolved Lm and density logs (e.g., RHOB). For example, consider the following example pseudocode:

[0193] Effective Lm = E1*Lm*Lm+E2*Lm+E3;

[0194] Fp_far=pow(effectiveLm,EF1)+EF2*density;

[0195] Dp_near=DF1*Fp_far*Fp_far+DF2*Fp_far+DF3;

[0196] Count rate_far = exp(Dp_far);

[0197] Fp_near=pow(effectiveLm,FN1)+FN2*density*effectiveLm;

[0198] Dp_near=DN1*Fp_near*Fp_near+DN2*Fp_near+DN3;

[0199] Count rate_near = exp(Dp_near);

[0200] In the above, the terms E1, E2, E3, EF1, EF2, DF1, DF2, DF3, DN1, DN2 and DN3 are constants. Near and far count rates can be converted and density corrected to neutron porosity output using the EC (Environmental Correction) algorithm defined in the ECOSCOPE EcoScope.dll (Dynamic Link Library).

[0201] Fig.15 Example graphs 1510, 1520, and 1530 of Lm versus porosity are shown, each for a given density (e.g., 1.75 g / cc, 2.5 g / cc, and 2.71 g / cc). Specifically, each of the graphs 1510, 1520, and 1530 shows the values ​​of BPHI and TNPH relative to Lm. The graphs 1510, 1520, and 1530 show the relationship between Lm and density.

[0202] Regarding conversion from measured neutron properties to Lm-based neutron porosity, when modeling neutron porosity for 3DP, measured neutron porosity logs (e.g., BPHI and TNPH) can be used to make an initial estimate of the layer properties. Neutron porosity estimates based on TNPH or BPHI logs cannot be used directly by Lm-based forward models because the Lm-based forward modeling methods accept Lm as input. Again, refer to Fig.12 , PoroSqrCmp can be a forward model that can provide a conversion of Lm to neutron properties. As an example, to calculate Lm, the reverse process of PoroSqrCmp (from Lm to BPHI or TNPH) can be performed. For example, for each layer, Lm can be obtained by iteratively adjusting Lm to achieve a good match with the target value of BPHI or TNPH.

[0203] Consider f Lm Defined as a function that performs layer feature transformation in PoroSqrCmp:

[0204] f Lm (Lm,Rho)→(BPHI,TNPH)

[0205] As shown, f Lm The function converts from layer Lm and density to layer BPHI / TNPH; note that f LmCan be defined in the following range: Lm: [12, 30] defined between 12cm and 30cm.

[0206] As mentioned before, the goal of one approach is to find Lm, so that f Lm (Lm) = layer neutron porosity (BPHI or TNPH). By initializing the layer neutron properties from BPHI or TNPH, the layer properties can be treated as constants for each layer in the model. For a single layer, f Lm Can be simplified to:

[0207] f Lm (Lm)→(BPHI,TNPH)

[0208] To find (f Lm (Lm) -1 , the condition may be f Lm (Lm) is monotonic. For example, we know that Lm is defined on the interval [12, 30], and f Lm (Lm) is monotonic.

[0209] In the monotonic range, one approach can be to use a custom binary search. The custom binary search algorithm can use the following workflow to iteratively reduce the initial range of Lm:

[0210] Counting Neutrons 候选值 =f Lm (Lm 候选值 ): Lm 候选值 =(Range min + Scope max ) / 2,

[0211] Neutron 候选值 Compared with the measured characteristic BPHI or TNPH,

[0212] Update the search range until the neutron candidate matches the measured property estimate,

[0213] When the match is good, use Lm 候选值 as a good candidate value.

[0214] Fig.16 An example listing 1600 of pseudocode for performing a custom binary search method is shown. As shown, the method can return an Lm porosity value from a measured property value (e.g., BPHI or TNPH) and a layer density value. As an example, a method can utilize a polynomial method. For example, the function can be a polynomial function (e.g., see the example pseudocode above).

[0215] As explained, forward modeling of neutron porosity requires migration length (Lm) as an input, while sensors measure and provide neutron flux data, not Lm data. Measured well logs that can be used to initialize neutron porosity properties can be BPHI and TNPH, which are not easily converted to Lm. As an example, a method can include performing a conversion from BPHI or TNPH and density properties to Lm. Such a method can be a "reverse" process (e.g., inversion) in which Lm is iteratively obtained by adjusting Lm to achieve an acceptable match at a BPHI or TNPH target value. In such a method, the user can model directly using the measured well logs and the complexity of handling Lm can be "hidden". From a user's perspective, such a method can make neutron porosity modeling readily available, which can speed up the workflow.

[0216] Fig.17 Exemplary graphical user interfaces (GUIs) 1710 and 1750 are shown, including GUIs 1715 and 1755, for processing acquired data to characterize geological formations (eg, via interpretation, etc.). Fig.17 The GUI may be part of the framework, or may be one or more components operably coupled to the framework. Fig.17 In the example of , GUI 1755 implements the detector parameter concept using Lm. As shown, the GUI can allow the selection of LWD tools, the selection of density and density correction sources for detector parameter calculations. Such selection can be utilized in forward modeling. GUI 1755 can be implemented in a manner that can improve the determination of hydrocarbon saturation in geological formations (e.g., with respect to time, difficulty, etc.). Such a system can be implemented in such a manner that various actions are hidden from users utilizing the system via one or more interactive GUIs. As explained, Lm can be utilized in a hidden process that facilitates a workflow, which can be used to determine hydrocarbon saturation in one or more layers through which a borehole can pass. As explained, a workflow can include drilling, which can be directional drilling, which can be in a high-angle horizontal well (HaHz). As an example, a workflow can include one or more of installing a completion assembly, drilling holes in a pipe, fracturing a formation, etc.

[0217] exist Fig.17In the example of , GUIs 1750 and 1755 demonstrate a method that can speed up analysis, such as through a three-to-one reduction of information, where the simplified information is in a more understandable form. As shown, GUI 1710 includes a thick line frame around TNPH_SQR, BPHI_SQR, and Neutron(AWLNP)_SQR, which can be reduced to Neutron_SQR as shown in GUI 1750. This method can be referred to as a detector parameter concept method, where neutron porosity can be presented instead of or in addition to migration length (Lm) and / or count rate. Such an approach can simplify user review and execution of workflows.

[0218] In the foregoing example, "Neutron_SQR" may be a square log of a neutron porosity property. A neutron porosity square log is easier to understand than an instrument measurement (e.g., count rate). In various cases, workflow may be facilitated by an operation that may easily present a neutron porosity (e.g., TNPH or BPHI) type value to a display (e.g., instead of presenting a migration length value and / or a count rate value).

[0219] Fig.17 Also shown are informational graphics that can provide information to assist the user with one or more workflows. One graphic relates to supported variables (e.g., "There is a variable named "RHOB" (with "Bulk Density" family) in the THL window. It is recommended to use the measured RHOB") and the other graphic relates to density (e.g., "Bottom Density" (ROBB) is used to process the measured neutron porosity (BPHI_FILT). It is recommended to use this variable for forward modeling. Supported variables (from dataset 'DVM_XYY_60B'): There are variables with the Bulk Density family (except ROBB_shifted) in the THL window."). In such an example, for the ECOSCOPE tool, BPHI_FILT is an acronym for "Filtered Optimum Thermal Neutron Porosity (Ratio Method) in Selected Lithology."

[0220] Fig.18 An example of a workflow 1800 for a framework such as a rock physics framework (e.g., consider a TECHLOG framework, etc.) is shown. Fig.17 The GUI 1755 is presented to a display where various graphical controls can drive Fig.111755, which is shown as including a density adjustment input box 1118 operably coupled to a forward modeling box 1140. The GUI 1755 includes a column for the density of the detector parameters (e.g., LQC.RHOB) and a column for the density adjustment source (e.g., LQC.ROBB). These two columns can be used to inform the method 1100 to use, for example, Fig.12 The forward modeling is performed in a manner determined by the method 1200 of FIG. 1800. As shown, the workflow 1800 may include an output box 1860 for outputting one or more types of neutron porosity square logs (e.g., BPHI and / or TNPH or NPHI). As an example, the workflow 1800 may be implemented in a global layer mode (e.g., see FIG. 1800 ). Fig. 9 Method 900).

[0221] As described above, a method can provide a Lm-based neutron porosity property value (e.g., via a function that determines an Lm value and an appropriate Lm-based neutron porosity property value). For example, a method can convert measured neutron property data (e.g., detector parameter values, etc.) into an Lm-based neutron porosity value. As described above, the neutron flux can be measured by a sensor of a neutron tool, where, after processing, the measurement can be expressed as a count rate. As an example, the detector parameter can be the natural logarithm of the neutron count rate (Ln).

[0222] Fig.19 1900 and GUI 1955, which may be dialog boxes or dialog windows providing information about the forward modeling method used for neutron porosity. For example, consider that GUI 1955 may be in Fig.18 18. As shown, a user may be viewing a well log response for a particular region including a borehole with an associated well log response, wherein the borehole has a trajectory that can be presented relative to total vertical depth (TVD) and measured depth (MD), or alternatively, for example, an appropriate lateral or horizontal dimension (e.g., for a lateral well, the MD and lateral dimensions may provide a display of relatively similar trajectories).

[0223] like Fig.19 As shown in the example of , GUI 1955 includes a column for the concentration of the detector parameter (e.g., the concentration used for the detector parameter calculation) and a column for the density correction source (e.g., the concentration adjustment source). Fig.18 These fields are utilized as described in workflow 1800 of FIG. 18 (eg, see input box 1114 for average density per depth and input box 1118 for density adjustment).

[0224] GUI 1900 can be accessed as part of a workflow. For example, consider a workflow involving three-dimensional petroleum physics (3DP). As an example, consider a workflow involving the use of a TECHLOG framework, in which various tools of the 3DP function can be presented on a display (e.g., as a GUI, etc.). In such an example, well log data can be used for a borehole including a portion of a boundary between one or more layers that are not perpendicular to the formation. In such an example, the user may wish to operate without count rate or migration length, but rather wish to operate with neutron porosity that can be presented in a more directly understandable manner. The user can use an input device (e.g., a touch screen, a mouse, a voice command, etc.) to cause the framework to execute one or more programs to present neutron porosity information to the display. Then, the user can evaluate various boundaries, layers, etc. about the trajectory. Then, the user can determine whether the trajectory is in an appropriate reservoir area (e.g., an oil-producing zone). As explained, the workflow can operate to simplify the presentation of neutron information, particularly when compared to methods that present migration lengths and / or count rates.

[0225] As an example, because the workflow may involve a borehole trajectory that includes one or more lateral portions (e.g., deviated portions), the workflow may utilize a simpler method to present neutron information, which may facilitate workflows regarding one or more of perforating, completing, fracturing, etc. For example, consider utilizing a GUI (e.g., GUI 1900) to determine where to form perforations, where to install completion components, where to fracture surrounding formation materials, etc. As an example, the workflow may include making one or more such determinations and then issuing a command to perforate casing, issuing a command to install completion components, issuing a command to fracture, etc. In such an example, perforating, installing, issuing, etc. may be performed, for example, and then fluids are produced from a target reservoir that intersects at least a portion of the borehole trajectory.

[0226] As an example, the process can utilize a migration length that represents various interactions of neutrons with materials (e.g., liquids, gases, oils, water, salts, lithologies, etc.). Published U.S. Patent Application No. US20160130916A1 to Abadie et al., entitled “Local layer geometry engine with work zone generated from buffer defined relative to a wellboretrajectory,” is incorporated herein by reference (Serial No. 14 / 933,768, filed November 5, 2015).

[0227] A method may include receiving neutron data and density data for a borehole in a geological formation; determining a migration length value for a layer of the geological formation based at least in part on the neutron data; forward modeling at least the layer based at least in part on the migration length value and the density data; and outputting modeled neutron data for the layer based at least in part on the forward modeling. In such an example, the borehole may be a high-angle borehole, wherein, for example, the received neutron data includes a tail due to the high angle of the borehole relative to the layer (e.g., see Figure 8 , Fig.19 wait).

[0228] As an example, a method may include determining a migration length value that takes into account the geometry of the borehole. As an example, a method may include determining the migration length value by implementing an iterative loop and one or more stopping criteria. As an example, a method may include determining the migration length value by implementing a binary search.

[0229] As an example, determining the migration length value may include forward modeling of at least one of the BPHI and the TNPH of the layer.

[0230] As an example, the neutron data may be or include BPHI data and / or TNPH data. As an example, a downhole tool may output information for use in calculating a neutron square log. As mentioned, a method may include utilizing migration length in a workflow that operates on neutron and density data. The neutron square log may be a neutron layer characteristic as defined from input measurements from a tool (e.g., an ECOSCOPE tool, etc.); noting that an average density per depth may be obtained via such a tool (e.g., measurements of the tool). As an example, a system may be operably coupled to a tool such as a downhole LWD tool.

[0231] As an example, a method may include simulating a physical phenomenon in a geological formation based at least in part on output modeling neutron data of a layer.Such a method may include sending a signal to at least one piece of equipment that interacts with the geological formation.

[0232] As an example, the neutron data may be logging while drilling (LWD) data acquired by a downhole tool during drilling operations in a geological formation.

[0233] As an example, a method may include receiving neutron data from a LWD tool during a drilling operation, and may include controlling the drilling operation based at least in part on the modeled neutron data. For example, consider orienting a drill bit relative to a reservoir in a geological formation, wherein the modeled neutron data represents a porosity characteristic of the reservoir.

[0234] As an example, a method may include presenting a graphical user interface to a display, wherein the graphical user interface includes a representation of a model of at least a portion of a geological formation and a representation of at least a portion of a borehole. In such an example, the graphical user interface may include one or more graphical tools for adjusting one or more boundaries of one or more layers.

[0235] As an example, a method may include determining hydrocarbon saturation (eg, and / or water saturation) based at least in part on modeled neutron data for the layer. In such an example, drilling may be intended to drill a well for production of a fluid including at least hydrocarbons.

[0236] As an example, a system may include a processor; a memory operably coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive neutron data and density data of a borehole in a geological formation; and determine a migration length value for a layer of the geological formation based at least in part on the neutron data; forward model at least the layer based at least in part on the migration length value and the density data; and output modeled neutron data for the layer based at least in part on the forward modeling.

[0237] As an example, one or more computer-readable storage media may include computer-executable instructions that are executable to instruct a computing system to: receive neutron data and density data for a borehole in a geological formation; and determine, based at least in part on the neutron data, a migration length value for a layer of the geological formation; forward model at least the layer based at least in part on the migration length value and the density data; and output modeled neutron data for the layer based at least in part on the forward modeling.

[0238] As an example, a workflow may be associated with various computer readable media (CRM) boxes. Such boxes typically include instructions suitable for execution by one or more processors (or cores) to instruct a computing device or system to perform one or more actions. As an example, a single medium may be configured with instructions to at least partially allow the execution of various actions of a workflow. As an example, a computer readable medium (CRM) may be a non-transitory, non-carrier, non-signal computer readable storage medium. As an example, a box may be provided as one or more sets of instructions, such as, for example, Figure 2 One or more sets of instructions 270 for system 250.

[0239] Fig. 20Components of an example of a computing system 2000 and an example of a networked system 2010 are shown. System 2000 includes one or more processors 2002, a memory and / or storage component 2004, one or more input and / or output devices 2006, and a bus 2008. In an example embodiment, instructions may be stored in one or more computer-readable media (e.g., memory / storage component 2004). Such instructions may be read by one or more processors (e.g., one or more processors 2002) via a communication bus (e.g., bus 2008), which may be wired or wireless. One or more processors may execute such instructions to (in whole or in part) implement one or more properties (e.g., as part of a method). A user may view output from a processor and interact with the processor via an I / O device (e.g., device 2006). In an example embodiment, a computer-readable medium may be a storage component such as a physical memory storage device, such as a chip, a chip on a package, a memory card, etc. (e.g., a computer-readable storage medium).

[0240] In an example embodiment, the components may be distributed, for example, in a network system 2010. The network system 2010 includes components 2022-1, 2022-2, 2022-3, ... 2022-N. For example, component 2022-1 may include processor 2002, and component 2022-3 may include a memory accessible by processor 2002. In addition, component 2002-2 may include an I / O device for displaying the method and optionally interacting with the method. The network may be or include the Internet, an intranet, a cellular network, a satellite network, etc.

[0241] As an example, the device can be a mobile device including one or more network interfaces for information communication. For example, the mobile device can include a wireless network interface (e.g., it can be operated via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, the mobile device can include components such as a main processor, a memory, a display, a display graphics circuit (e.g., optionally including a touch and gesture circuit), a SIM slot, an audio / video circuit, a motion processing circuit (e.g., an accelerometer, a gyroscope), a wireless local area network circuit, a smart card circuit, a transmitter circuit, a GPS circuit, and a battery. As an example, the mobile device can be configured as a mobile phone, a tablet computer, etc. As an example, the method can be implemented (e.g., in whole or in part) using a mobile device. As an example, the system can include one or more mobile devices.

[0242] As an example, the system may be a distributed environment, such as a so-called "cloud" environment, in which various devices, components, etc. interact for the purpose of data storage, communication, computing, etc. The device or system may include one or more components for communicating information via one or more Internets (e.g., when the communication occurs via one or more Internets), cellular networks, satellite networks, etc. As an example, the method may be implemented in a distributed environment (e.g., in whole or in part as a cloud-based service).

[0243] As an example, information can be input from a display (e.g., consider a touch screen), output to a display, or both. As an example, information can be output to a projector, a laser device, a printer, etc. so that the information can be viewed. As an example, information can be output stereoscopically or holographically. For a printer, consider using a 2D or 3D printer. As an example, a 3D printer can include one or more substances that can be output to construct a 3D object. For example, data can be provided to a 3D printer to construct a 3D representation of an underground stratum. As an example, layers can be constructed in 3D (e.g., horizons, etc.), geological bodies can be constructed in 3D, etc. As an example, holes, cracks, etc. can be constructed in 3D (e.g., as positive structures, as negative structures), etc.

[0244] Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. Therefore, all such modifications are intended to be included within the scope of the present disclosure as defined by the appended claims. In the claims, means-plus-function clauses are intended to cover structures described herein that perform the recited functions, not only structural equivalents, but also equivalent structures. Thus, while nails and screws may not be structural equivalents because nails employ cylindrical surfaces to secure wooden parts together while nails employ helical surfaces, nails and screws may be equivalent structures in the context of fastening wooden parts. It is the express intent of the applicant not to invoke 35 U.S.C. § 112, paragraph 6, to impose any limitations on any of the claims herein, except for limitations expressly using "means for..." and related functions in the claims.

Claims

1. A method (1100) for determining neutron porosity in a geological formation, comprising: receiving neutron data and density data for a high-angle borehole in a geological formation, wherein the neutron data includes a tail due to a high angle of the borehole relative to a layer of the geological formation; determining a migration length value for a layer of the geological formation based at least in part on the neutron data; forward modeling at least a layer of the geological formation based at least in part on the migration length values ​​and the density data; and Based at least in part on the forward modeling, modeled neutron data for a layer of the geological formation is output.

2. The method according to claim 1, wherein: Determining the migration length value takes into account the geometry of the high-angle drilled hole.

3. The method according to claim 1, wherein: Determining the migration length value includes implementing an iterative loop and one or more stopping criteria.

4. The method according to claim 1, wherein: Determining the migration length value involves performing a binary search.

5. The method according to claim 1, wherein: Determining the migration length value includes forward modeling of at least one of an optimal thermal neutron porosity and a thermal neutron porosity for at least a layer of the geological formation.

6. The method according to claim 1, wherein: The neutron data is optimal thermal neutron porosity data or thermal neutron porosity data.

7. The method of claim 1, comprising simulating physical phenomena in the geological formation based at least in part on outputting modeled neutron data of layers of the geological formation.

8. The method according to claim 1, wherein: Neutron data is logging-while-drilling data acquired by a downhole tool during drilling operations in the geological formation.

9. The method according to claim 1, wherein: The receiving includes receiving neutron data from a logging while drilling tool during drilling operations.

10. The method of claim 9, comprising controlling drilling operations based at least in part on the modeled neutron data, wherein: Controlling includes directing a drill bit relative to a reservoir in the geological formation, wherein the modeled neutron data represents a porosity characteristic of the reservoir.

11. The method of claim 1 , comprising presenting a graphical user interface to a display, wherein the graphical user interface includes a representation of a model of at least a portion of a geological formation and a representation of at least a portion of a high-angle borehole, wherein the graphical user interface includes graphical tools for adjusting one or more boundaries of one or more layers of the geological formation.

12. The method of claim 1, comprising determining hydrocarbon saturation based at least in part on modeled neutron data for layers of the geological formation.

13. A system (250) for determining neutron porosity in a geological formation, comprising: processor(256); a memory (258) operatively coupled to the processor; and Processor executable instructions (270), stored in the memory, to instruct the system to: receiving neutron data and density data for a high-angle borehole in a geological formation, wherein the neutron data includes a tail due to a high angle of the borehole relative to a layer of the geological formation; determining a migration length value for a layer of the geological formation based at least in part on the neutron data; forward modeling at least a layer of the geological formation based at least in part on the migration length values ​​and the density data; and Based at least in part on the forward modeling, modeled neutron data for a layer of the geological formation is output.

14. A computer-readable storage medium comprising computer-executable instructions executable to instruct a computing system to perform the method according to any one of claims 1 to 12.

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