Patent Application
20250102693
Subsurface mapping is used in a variety of contexts to characterize the properties of a subterranean volume of interest. One way this is done is through seismic imaging. Seismic data is received in a seismic survey, and the seismic data may then be processed to generate two or three dimensional images of the subsurface. The images may then be interpreted, e.g., to identify geological features in the subsurface and, e.g., generate a facies model.
Advances have been made recently in computer-aided seismic interpretation, and a set of automated tools have been developed that greatly accelerate the process of seismic interpretation, including 3D visualization, horizon tracking, fault picking, facies analysis, and others. More recently, deep learning, particularly convolutional neural networks (CNN), has enabled techniques that include interpreting a seismic volume directly from its amplitude data with relatively little user interaction, using deterministic seismic attributes. This has been used in fault detection, salt body delineation, horizon tracking, and sequence analysis, and potentially other contexts.
These interpretation CNNs generally implement supervised learning, calling for a human user (“interpreter”) to annotate a set of seismic sections as training data. The human aspect of these processes can present a challenge, because the annotated sections that the training relies on may represent a small sampling of an entire seismic volume and thus may not accurately represent of the complexities in seismic patterns throughout the seismic survey.
In such a case, although a CNN effectively learns from the annotated sections, its prediction on the sections far away from these training inputs may have a relatively low accuracy. To improve the accuracy, one strategy is to expand the training data by sorting out and guiding an interpreter to annotating these challenging sections, re-training and evaluating the CNN, and repeating the process until the machine prediction becomes acceptable. For such iterative seismic interpretation, active learning (AL) may be employed, in which a CNN can interactively query an expert to annotate new seismic sections where its prediction is least accurate. However, without a volumetric annotation for quantitative analysis, such section-wise evaluation of CNN prediction depends on visual screening based on the interpreter's knowledge, which is both labor intensive and subjective.
Embodiments of the disclosure include a method that includes receiving seismic data and an attribute, the seismic data and the attribute representing a subsurface volume, receiving training labels for the seismic data, training a machine learning model to identify features in the subsurface volume and to reconstruct the attribute, based at least in part on the seismic data and the training labels, generating an interpretation by predicting features in the subsurface volume using the machine learning model, generating a reconstructed attribute using the machine learning model, comparing the reconstructed attribute with the attribute that was received to identify one or more sections of the subsurface volume, and generating a recommendation to acquire additional training labels in the one or more sections that were identified.
Embodiments of the disclosure include a computing system that includes one or more processors, and a memory system including one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving seismic data and an attribute, the seismic data and the attribute representing a subsurface volume, receiving training labels for the seismic data, training a machine learning model to identify features in the subsurface volume and to reconstruct the attribute, based at least in part on the seismic data and the training labels, generating an interpretation by predicting features in the subsurface volume using the machine learning model, generating a reconstructed attribute using the machine learning model, comparing the reconstructed attribute with the attribute that was received to identify one or more sections of the subsurface volume, and generating a recommendation to acquire additional training labels in the one or more sections that were identified.
Embodiments of the disclosure include a non-transitory, computer-readable media storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The operations include receiving seismic data and an attribute, the seismic data and the attribute representing a subsurface volume, receiving training labels for the seismic data, training a machine learning model to identify features in the subsurface volume and to reconstruct the attribute, based at least in part on the seismic data and the training labels, generating an interpretation by predicting features in the subsurface volume using the machine learning model, generating a reconstructed attribute using the machine learning model, comparing the reconstructed attribute with the attribute that was received to identify one or more sections of the subsurface volume, and generating a recommendation to acquire additional training labels in the one or more sections that were identified.
Thus, the computing systems and methods disclosed herein are more effective methods for processing collected data that may, for example, correspond to a surface and a subsurface region. These computing systems and methods increase data processing effectiveness, efficiency, and accuracy. Such methods and computing systems may complement or replace conventional methods for processing collected data. This summary is provided to introduce a selection of 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 as an aid in limiting the scope of the claimed subject matter.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object could be termed a second object, and, similarly, a second object could be termed a first object, without departing from the scope of the invention. The first object and the second object are both objects, respectively, but they are not to be considered the same object.
The terminology used in the description of an embodiment of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of embodiments of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.
Attention is now directed to processing procedures, methods, techniques and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques and workflows disclosed herein may be combined and/or the order of some operations may be changed.
Computer facilities may be positioned at various locations about the oilfield 100 (e.g., the surface unit 134) and/or at remote locations. Surface unit 134 may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors. Surface unit 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit 134 may also collect data generated during the drilling operation and produce data output 135, which may then be stored or transmitted.
Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. As shown, sensor (S) is positioned in one or more locations in the drilling tools and/or at rig 128 to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. Sensors (S) may also be positioned in one or more locations in the circulating system.
Drilling tools 106b may include a bottom hole assembly (BHA) (not shown), generally referenced, near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly includes capabilities for measuring, processing, and storing information, as well as communicating with surface unit 134. The bottom hole assembly further includes drill collars for performing various other measurement functions.
The bottom hole assembly may include a communication subassembly that communicates with surface unit 134. The communication subassembly is adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, or wired drill pipe communications. The communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other known telemetry systems.
Typically, the wellbore is drilled according to a drilling plan that is established prior to drilling. The drilling plan typically sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected.
The data gathered by sensors (S) may be collected by surface unit 134 and/or other data collection sources for analysis or other processing. The data collected by sensors (S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be stored in separate databases, or combined into a single database.
Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations. Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 may then send command signals to oilfield 100 in response to data received. Surface unit 134 may receive commands via transceiver 137 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.
Wireline tool 106c may be operatively connected to, for example, geophones 118 and a computer 122a of a seismic truck 106a of
Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, sensor S is positioned in wireline tool 106c to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the field operation.
Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, the sensor (S) may be positioned in production tool 106d or associated equipment, such as Christmas tree 129, gathering network 146, surface facility 142, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation.
Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).
While
The field configurations of
Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively; however, it should be understood that data plots 208a-208c may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.
Static data plot 208a is a seismic two-way response over a period of time. Static plot 208b is core sample data measured from a core sample of the formation 204. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot 208c is a logging trace that typically provides a resistivity or other measurement of the formation at various depths.
A production decline curve or graph 208d is a dynamic data plot of the fluid flow rate over time. The production decline curve typically provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc.
Other data may also be collected, such as historical data, user inputs, economic information, and/or other measurement data and other parameters of interest. As described below, the static and dynamic measurements may be analyzed and used to generate models of the subterranean formation to determine characteristics thereof. Similar measurements may also be used to measure changes in formation aspects over time.
The subterranean structure 204 has a plurality of geological formations 206a-206d. As shown, this structure has several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c and a sand layer 206d. A fault 207 extends through the shale layer 206a and the carbonate layer 206b. The static data acquisition tools are adapted to take measurements and detect characteristics of the formations.
While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield 200, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.
The data collected from various sources, such as the data acquisition tools of
Each wellsite 302 has equipment that forms wellbore 336 into the Earth. The wellbores extend through subterranean formations 306 including reservoirs 304. These reservoirs 304 contain fluids, such as hydrocarbons. The wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks 344. The surface networks 344 have tubing and control mechanisms for controlling the flow of fluids from the wellsite to processing facility 354.
Attention is now directed to
The component(s) of the seismic waves 368 may be reflected and converted by seafloor surface 364 (i.e., reflector), and seismic wave reflections 370 may be received by a plurality of seismic receivers 372. Seismic receivers 372 may be disposed on a plurality of streamers (i.e., streamer array 374). The seismic receivers 372 may generate electrical signals representative of the received seismic wave reflections 370. The electrical signals may be embedded with information regarding the subsurface 362 and captured as a record of seismic data.
In one implementation, each streamer may include streamer steering devices such as a bird, a deflector, a tail buoy and the like, which are not illustrated in this application. The streamer steering devices may be used to control the position of the streamers in accordance with the techniques described herein.
In one implementation, seismic wave reflections 370 may travel upward and reach the water/air interface at the water surface 376, a portion of reflections 370 may then reflect downward again (i.e., sea-surface ghost waves 378) and be received by the plurality of seismic receivers 372. The sea-surface ghost waves 378 may be referred to as surface multiples. The point on the water surface 376 at which the wave is reflected downward is generally referred to as the downward reflection point.
The electrical signals may be transmitted to a vessel 380 via transmission cables, wireless communication or the like. The vessel 380 may then transmit the electrical signals to a data processing center. Alternatively, the vessel 380 may include an onboard computer capable of processing the electrical signals (i.e., seismic data). Those skilled in the art having the benefit of this disclosure will appreciate that this illustration is highly idealized. For instance, surveys may be of formations deep beneath the surface. The formations may typically include multiple reflectors, some of which may include dipping events, and may generate multiple reflections (including wave conversion) for receipt by the seismic receivers 372. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface 362.
Marine seismic acquisition systems tow each streamer in streamer array 374 at the same depth (e.g., 5-10 m). However, marine-based survey 360 may tow each streamer in streamer array 374 at different depths such that seismic data may be acquired and processed in a manner that avoids the effects of destructive interference due to sea-surface ghost waves. For instance, marine-based survey 360 of
The workflow 400 includes data preparation or ingestion stage 402. In this stage 402, an initial training section 401 of a seismic data set may be selected, e.g., by a human operator or a machine learning model, as will be discussed in greater detail below. The workflow 400 may also include receiving seismic data (e.g., seismic amplitude) 404 and one or more attributes 406, such as relative geologic time (RGT), corresponding to the selected training sections 401 (e.g., representing the same volume of interest). It will be appreciated that one or more other attributes may be used, in addition to or instead of RGT, such as structural dips, horizon and/or fault interpretations. It will thus be appreciated that although RGT is used to describe the present workflow 400, other attributes may be employed. It will also be appreciated that although facies classification is used to describe an embodiment of the present workflow, embodiments of the workflow may be readily tailored for other seismic image interpretation tasks.
The seismic data 404 and attribute 406 (e.g., RGT) may be used for training a machine learning model 408, such as a seismic interpretation (SI) convolutional neural network (CNN), to name one specific example. The machine learning model 408 may quantify the prediction errors and recommend sections for next-iteration of SI-CNN training. In addition are a few sections, which could be selected by either manual screening or clustering-based recommendation, which will be annotated and used for initializing the iterative process, as shown.
The workflow 400 may also include a label annotation stage 410. In this stage 410, an interpreter applies his or her knowledge of the seismic interpretation (e.g., a previously constructed facies model) in the selected training sections 401 and annotates a portion of the seismic data therein. For example, the annotations may represent the locations of features of interest in the seismic data. The annotations are digitalized and provided to the machine learning model 408 for training purposes, as shown. The manual annotation in stage 410 may be comprehensive at individual sections and generally consistent across sections.
Next, the workflow 400 includes training the machine learning model 408. In an embodiment, the machine learning model 408 may be a “dual task” SI-CNN. An example architecture for the dual task SI-CNN is discussed below. As a dual task SI-CNN, the machine learning model 408 may be configured to receive two sources of input (e.g., seismic data 404 and an attribute 406 such as RGT). The seismic data 404 may be annotated by a human user, with the annotations indicating locations of features (e.g., faults, horizons, salt bodies, etc.). From this input, the machine learning model 408 may predict an interpretation 414 (e.g., facies model) identifying features in the seismic data, e.g., extending the annotations provided by the human user. The machine learning model 408 may also reconstruct the original attribute (RGT) based at least in part on the facies model (e.g., as a convolution with the facies model). This reconstructed RGT is represented as RGT* (reference number 412) in
The workflow 400 may also include a “quality control” stage 416. In this stage, the workflow 400 may include reviewing and evaluating cubes predicted by the machine learning model. For example, the reconstructed attribute 412 may be compared to the attribute 406 as it was received as input, in order to estimate the accuracy of the predicted interpretation 414 (e.g., facies model). This accuracy may be quantified in an RGT-reconstruction error (RRE) analysis 418 and used to identify areas in the seismic volume where additional training labels would be beneficial, thereby enhancing the efficiency of the iterative, adaptive learning process by potentially identifying areas where additional labels are impactful. If the accuracy is acceptable, as determined at 420, the predicted interpretation 414 (e.g., facies model) can then be exported for future interpretation modules and tasks. Otherwise, the workflow 400 may revert to adding more training sections 422 as recommended using, e.g., an automated scheme, as will be described in greater detail below, to expand the training data, enhance the capability of the machine learning model 408 (e.g., SI-CNN) in learning, and improve the accuracy of machine prediction.
More specifically, for automated 2D section recommendation, a reconstruction error may be calculated, as represented by the RRE analysis 418 in
where i, j, and k denote the inline, crossline and vertical dimensions of the seismic survey.
As will be discussed in greater detail below, because both the predicted interpretation 414 (e.g., facies model) and reconstructed attribute 412 (e.g., RGT*) originate from the same encoder-decoder block in the machine learning model 408, mis-predictions in the predicted interpretation 414 may be traceable in the reconstructed attribute 412. In some embodiments, the difficulty in reconstructing the attribute (e.g., matching the reconstructed attribute 412 with the input attribute 406) may be directly related to the accuracy of the predicted interpretation 414. Accordingly, a reconstruction error curve may be employed to sort the sections (e.g., areas in the subsurface) according to complexity for a machine to learn and capture, identify the sections that have been least learned by the machine, and add them into the library of training data for the next iteration of machine learning training and prediction.
The machine learning model 408 may further include two output branches 508, 509. The output branch 508 may generate a predicted facies model 510, e.g., to match the expert annotations on the provided training sections, and the output branch 509 may reconstruct the attribute 502, yielding a reconstructed attribute 512.
Using the attribute-constrained, machine learning model 408 (e.g., an RGT-constrained, SI-CNN) may enforce the lateral consistency of seismic patterns preserved in the attribute 502 (RGT) while building the mapping relationship between the seismic data 500 and the predicted facies model 510 and thus leading to improved machine prediction, as discussed above. Moreover, as also noted above, errors in the attribute reconstruction may be fed back to the same block of the encoder-decoder 506, and thus employed to identify corresponding areas in the interpretation prediction (e.g., the predicted facies model 510) that are poorly interpreted, so that areas for additional training can be identified quantitatively and automatically.
Referring again to
The method 600 may include receiving seismic data (e.g., seismic amplitude measured by one or more geophones in a seismic survey) and at least one other attribute, which both represent the same (or at least partially the same) subsurface volume, as at 602. The at least one other attribute may be or include RGT and/or other attributes/inputs, as noted above.
The method 600 may further include receiving labels identifying features in the seismic data from a human user, as at 604. The labels may be configured to be broadly representative of the seismic volume, but, because the seismic volume may have different levels of complexity in different areas, the labels may be unequally distributed in order to capture such complexity. In at least some embodiments, the labels are generated at multiple times, in response to feedback, as will be discussed in greater detail below.
The method 600 may also include training a machine learning model (e.g., an SI-CNN) to identify features based at least in part on the seismic data, the labels, and the at least one attribute, as at 606. The machine learning model may convolve the annotated seismic data and the attribute in an encoder-decoder block.
The method 600 may also include predicting locations of features in the seismic data using the trained machine learning model, as at 608. This may provide a “predicted interpretation”, e.g., a facies model, among other possibilities, providing locations and/or other characteristics of features, such as faults, salt domes, etc., in the subsurface volume.
The method 600 may also include reconstructing the at least one attribute using the machine learning model, as at 610. As noted above, the two tasks of the machine learning model (identifying features and reconstructing the attribute) may be performed by the same encoder-decoder. As such, errors in one output result may indicate errors in the other output. This may indicate particular sections in the subsurface volume where the machine learning model is not accurately predicting the presence of features, e.g., because the machine learning model is not well trained for the geological complexities in that particular section.
The method 600 may include comparing the reconstructed attribute with the attribute that was provided as input, as at 612. Based on the comparison, the method 600 may identify one or more sections in the seismic data in which the machine learning model is not sufficiently accurate in its interpretations (e.g., not sufficiently trained), and/or one or more sections in which the machine learning model is sufficiently accurate. The sufficiency of the accuracy, and thus the training in the related sections, may be determined at least in part based on the accuracy value calculated, e.g., using equations (1) and (2) above.
In some embodiments, sections with the largest inconsistency may be identified for labeling first, e.g., a ranking scheme may be implemented. In other embodiments, any sections that have an inconsistency that exceeds a certain threshold may be flagged for additional labelling. In other embodiments, any sort of identification technique that is based upon the quantitative consistency may be used. Additionally, sections in the seismic data for which the machine learning model does accurately interpret the data may be determined, and no further training labels may be called for in these sections, or additional labels may be considered a lower priority, in at least some embodiments.
The method 600 may proceed to generating a recommendation for additional training labels from the user (e.g., a human interpreter) based on the comparing, as at 614. In particular, the recommendation may be for additional training labels in the sections identified where the machine learning model's interpretations are not sufficiently accurate. The method 600 may then receive the labels, as at 616, in response to the recommendation, from an interpreter. The method 600 may return to training (in this case, retraining) the machine learning model (e.g., the SI-CNN) as discussed above, as at 606. The method 600 may then proceed again through the training, predicting, and reconstructing, and again determine the consistency. This process may repeat until an exit condition is met, such as the maximum inconsistency being below a certain threshold, a certain number of iterations being reached, or any statistical measure being satisfied. The method 600 may also, in some embodiments, including visualizing the predicted interpretation, the reconstructed attribute, or both, so as to facilitate operations in the field, e.g., well location, planning, drilling, completion, treatment, etc., and/or any other construction project, such as wind, solar, or geothermal facilities construction.
In one or more embodiments, the functions described can be implemented in hardware, software, firmware, or any combination thereof. For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, classes, and so on) that perform the functions described herein. A module can be coupled to another module or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, or the like can be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, and the like. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
In some embodiments, any of the methods of the present disclosure may be executed using a system, such as a computing system.
A processor can include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
The storage media 1006 can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of
In some embodiments, computing system 1000 contains one or more adaptive learning module(s) 1008. In the example of computing system 1000, computer system 1001a includes the adaptive learning module 1008. In some embodiments, a single adaptive learning module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of adaptive learning modules may be used to perform some or all aspects of methods.
It should be appreciated that computing system 1000 is only one example of a computing system, and that computing system 1000 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of
Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are all included within the scope of protection of the invention.
Geologic interpretations, models and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to embodiments of the present methods discussed herein. This can include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 1000,
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.
This application claims priority to U.S. Provisional Patent Application having Ser. No. 63/269,885, which was filed on Mar. 24, 2022, and is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2023/016249 | 3/24/2023 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63269885 | Mar 2022 | US |
