Patent Description:
While modern multichannel data have increased the quantity and quality of interpretable data, interpretation generally calls for the interpreter to draw upon his or her geological understanding to pick an interpretation from the many nominally "valid" interpretations that the data allow. As such, seismic interpretation involves substantial manual effort and human educated guess work based on acquired experience. That is, two different human interpreters are unlikely to independently produce identical interpretations. In some situations, the differences in interpretations may be trivial, but in other situations, the differences may result in the use of different guidance methods or out-of-preference or experience. Interpretation differences may be more likely to occur in cases where there is little data to confirm one interpretation or another. The publication "<NPL>, discloses a classification method based on supervised clustering. The publication "<NPL> discloses a method for detecting melanoma. The publication "Ensemble learning: A survey", by<NPL>, discloses general methods for improving the predictive performance of a model by training multiple models while dividing the original dataset into smaller subsets and then using each subset to train a different inducer without any reference to seismic data.

The present disclosure provides a method as defined in claim <NUM>, a computing system as defined in claim <NUM>, and a non-transitory computer-readable medium according to claim <NUM>.

Embodiments of the present disclosure include systems and/or methods that provide end users (e.g., seismic interpreters) with multiple interpretation options of seismic data. More specifically, machine learning techniques are employed to produce different interpretation options in which each interpretation option is developed based on different machine learning models that have been generated with consideration to varying interpretation techniques, interpretation preferences, interpreter experiences, geological considerations, etc. In areas in which different seismic data interpretations may differ significantly, aspects of the present invention provide multiple computer-generated, non-subjective, rules-based, interpretation options. A seismic data interpreter may then independently analyze each interpretation option, and determine mitigating decisions based on a selected interpretation option. In this way, more effective and intelligent options are made as a result of the seismic interpreter having more options at their disposal.

In some implementations, aspects of the present invention receive, as input, seismic cubes and subject matter expert's (SME's) seismic interpretations (such as fault sticks, horizon interpretations, salt body boundaries, geobodies, etc.) on those cubes. Aspects of the present disclosure train a set of machine learning algorithms on the input training data. In some implementations, the set of machine learning algorithms are used to produce predictions on input seismic data.

As further described herein, aspects of the present disclosure receive seismic training input datasets in which each dataset includes a training seismic dataset (e.g., an image) and multiple different interpretations of that dataset (e.g., interpretations by set of seismic interpreters). The training input datasets is sorted and separated into first and second groups in which the first group includes portions of the training datasets in which the different interpretations are in agreement, and the second group includes portions of the training datasets in which the interpretations are in disagreement. The second group is further divided into multiple subgroups in which each subgroup includes different attributes. For example, one subgroup may include interpretations made by interpreters who specialize in seismic data interpretations from a particular geographic region, whereas another subgroup may include interpretations made using a particular type of interpretation technique. Aspects of the present disclosure further generate trained models based on the first group and each of the second subgroups. The trained models are then used (e.g., in runtime) to generate seismic interpretation options for an input seismic dataset. These seismic interpretation options are presented to a user (e.g., a subject-matter expert or seismic interpreter) to provide the user with additional interpretation information to make more accurate seismic interpretations. The seismic interpretations may be used as a data point or factor in oil/gas exploration planning activities, drilling equipment procedures, drilling operations, etc..

In some embodiments, aspects of the present disclosure produce trained models are using computer-based machine learning techniques. The processes described herein therefore transform a previously subjective process of interpreting data into a machine-based non-subjective process based on trained models.

The terminology used in the description 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 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.

<FIG> illustrate simplified, schematic views of oilfield <NUM> having subterranean formation <NUM> containing reservoir <NUM> therein in accordance with implementations of various technologies and techniques described herein. <FIG> illustrates a survey operation being performed by a survey tool, such as seismic truck <NUM>, to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In <FIG>, one such sound vibration, e.g., sound vibration <NUM> generated by source <NUM>, reflects off horizons <NUM> in earth formation <NUM>. A set of sound vibrations is received by sensors, such as geophone-receivers <NUM>, situated on the earth's surface. The data received <NUM> is provided as input data to a computer <NUM> of a seismic truck <NUM>, and responsive to the input data, computer <NUM> generates seismic data output <NUM>. This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction.

<FIG> illustrates a drilling operation being performed by drilling tools <NUM> suspended by rig <NUM> and advanced into subterranean formations <NUM> to form wellbore <NUM>. Mud pit <NUM> is used to draw drilling mud into the drilling tools via flow line <NUM> for circulating drilling mud down through the drilling tools, then up wellbore <NUM> and back to the surface. The drilling mud is typically filtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling mud. The drilling tools are advanced into subterranean formations <NUM> to reach reservoir <NUM>. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample <NUM> as shown.

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.

Surface unit <NUM> may include transceiver <NUM> to allow communications between surface unit <NUM> and various portions of the oilfield <NUM> or other locations. Surface unit <NUM> may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield <NUM>. Surface unit <NUM> may then send command signals to oilfield <NUM> in response to data received. Surface unit <NUM> may receive commands via transceiver <NUM> 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 <NUM> 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.

As shown, the sensor (S) may be positioned in production tool <NUM> or associated equipment, such as Christmas tree <NUM>, gathering network <NUM>, surface facility <NUM>, 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.

Data plots <NUM>-<NUM> are examples of static data plots that may be generated by data acquisition tools <NUM>-<NUM>, respectively; however, it should be understood that data plots <NUM> - <NUM> 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 <NUM> is a seismic two-way response over a period of time. Static plot <NUM> is core sample data measured from a core sample of the formation <NUM>. 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 <NUM> is a logging trace that typically provides a resistivity or other measurement of the formation at various depths.

A production decline curve or graph <NUM> 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..

While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield <NUM> 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 <NUM>, 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 <FIG>, may then be processed and/or evaluated. Typically, seismic data displayed in static data plot <NUM> from data acquisition tool <NUM> is used by a geophysicist to determine characteristics of the subterranean formations and features. The core data shown in static plot <NUM> and/or log data from well log <NUM> are typically used by a geologist to determine various characteristics of the subterranean formation. The production data from graph <NUM> is typically used by the reservoir engineer to determine fluid flow reservoir characteristics. The data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques.

<FIG> illustrates an oilfield <NUM> for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites <NUM> operatively connected to central processing facility <NUM>. The oilfield configuration of <FIG> is not intended to limit the scope of the oilfield application system. Part, or all, of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present.

Attention is now directed to <FIG>, which illustrates a side view of a marine-based survey <NUM> of a subterranean subsurface <NUM> in accordance with one or more implementations of various techniques described herein. Subsurface <NUM> includes seafloor surface <NUM>. Seismic sources <NUM> may include marine sources such as vibroseis or airguns, which may propagate seismic waves <NUM> (e.g., energy signals) into the Earth over an extended period of time or at a nearly instantaneous energy provided by impulsive sources. The seismic waves may be propagated by marine sources as a frequency sweep signal. For example, marine sources of the vibroseis type may initially emit a seismic wave at a low frequency (e.g., <NUM>) and increase the seismic wave to a high frequency (e.g., <NUM>-<NUM>) over time.

The component(s) of the seismic waves <NUM> may be reflected and converted by seafloor surface <NUM> (i.e., reflector), and seismic wave reflections <NUM> may be received by a plurality of seismic receivers <NUM>. Seismic receivers <NUM> may be disposed on a plurality of streamers (i.e., streamer array <NUM>). The seismic receivers <NUM> may generate electrical signals representative of the received seismic wave reflections <NUM>. The electrical signals may be embedded with information regarding the subsurface <NUM> and captured as a record of seismic data.

In one implementation, seismic wave reflections <NUM> may travel upward and reach the water/air interface at the water surface <NUM>, a portion of reflections <NUM> may then reflect downward again (i.e., sea-surface ghost waves <NUM>) and be received by the plurality of seismic receivers <NUM>. The sea-surface ghost waves <NUM> may be referred to as surface multiples. The point on the water surface <NUM> 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 <NUM> via transmission cables, wireless communication or the like. The vessel <NUM> may then transmit the electrical signals to a data processing center. Alternatively, the vessel <NUM> 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 <NUM>. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface <NUM>.

Marine seismic acquisition systems tow each streamer in streamer array <NUM> at the same depth (e.g., <NUM>-<NUM>). However, marine based survey <NUM> may tow each streamer in streamer array <NUM> 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 <NUM> of <FIG> illustrates eight streamers towed by vessel <NUM> at eight different depths. The depth of each streamer may be controlled and maintained using the birds disposed on each streamer.

As described herein, multiple different seismic interpretations may be made from a common seismic dataset. Such a situation may occur when insufficient data exists to provide a definitive interpretation and/or in cases where interpreters base their interpretations on different interpretation techniques. <FIG> illustrate different example interpretations of a common seismic dataset. As shown in in <FIG>, two interpretations may be made of the same geological/geophysical feature from the same set of seismic data <NUM>. For example, fault each interpretation may include the presence of a fault line. In some situations, a portion of the two different interpretations may be common. For example, referring to <FIG>, the common portion of both interpretations (e.g., the portion in which both interpretations match or are in agreement) are indicated as group A. The portion where the interpretations do not match or are in disagreement or differ are indicated as group D. More specifically, group A indicates the portion of a fault line/fault stick that is present in both interpretations, and group D indicates the portion of the fault line that is not present in both interpretations. Other types of interpretations may also be analyzed for commonalities (e.g., horizon interpretations, salt body boundaries, geobodies, etc.).

As further described herein, trained models are generated and used for interpreting seismic data. For example, the models are trained using multiple known or predetermined interpretations of input training seismic datasets. <FIG> illustrates an example of a training process performed by a data interpretation system <NUM> in which training models are generated based on an input training seismic dataset <NUM> received by the data interpretation system <NUM>. In the example of <FIG>, the input training seismic dataset <NUM> includes three different known or predetermined seismic interpretations (although it is noted that in practice, an input training seismic dataset may include any number of predetermined seismic interpretations). As described herein, the three different interpretations of the training seismic dataset <NUM> are used as "ground truths" as part of generating trained models in which the trained models are used to interpret a target seismic dataset (e.g., in run-time). That is, each interpretation is considered a ground truth interpretation in which each ground truth is based on a manual, predetermined, or known (e.g., "true") interpretation of the training seismic dataset <NUM>. As described herein, each interpretation is associated with a set of attributes (e.g., interpretations by interpreters who specialize in interpretations from a particular geographic region, interpretations made using a particular type of interpretation technique, experience level of the interpreter, etc.). The portion in which the interpretations of three seismic datasets agree are grouped (e.g., as group A), and the portion where the interpretations are in disagreement or differ are grouped (e.g., as group D)
As further shown in <FIG>, group D is further sub-divided (or "subgrouped") into three subgroups (e.g., one subgroup for each different interpretation, such as subgroups <NUM>, <NUM>, and <NUM>). In this way, three ground truths are established for a single training seismic dataset <NUM> in which each ground truth includes the portion in which the interpretations are in agreement (e.g., group A) and respective subgroups in which the interpretations are in disagreement (e.g., subgroups <NUM>, <NUM>, and <NUM>). Each ground truth is used to generate a trained model. Thus, in the example of <FIG>, three trained models are generated in which the first model is based on a ground truth including group A and subgroup <NUM>, the second model is based on a ground truth including group A and subgroup <NUM>, and the third model is based on a ground truth including group A and subgroup <NUM>. Each trained model is used to generate an interpretation option for an input set of seismic data (e.g., a target or runtime seismic dataset for which the interpretation is unknown). As described herein, multiple training seismic datasets are used to generate and store multiple sets of trained models. These trained models are then used to generate interoperation options for an input target seismic dataset.

Referring to <FIG>, a seismic dataset <NUM> (e.g., a target or runtime seismic dataset to be interpreted) is inputted into the data interpretation system <NUM>. Based on receiving the seismic dataset <NUM>, the data interpretation system <NUM> identifies interpretation options from the trained models matching the seismic dataset. For example, as previously discussed, the data interpretation system <NUM> generates and stores multiple sets of trained models based on multiple training seismic datasets. As such, the data interpretation system <NUM> interprets a target seismic dataset <NUM> by matching the seismic dataset <NUM> with a previously generated trained models that are based on previously inputted training seismic dataset. Based on the matching, the data interpretation system <NUM> provides interpretation options (e.g., interpretation options <NUM>) that are linked to the trained models. In the example shown in <FIG>, the seismic dataset <NUM> is matched with the training seismic dataset <NUM> of <FIG>. Accordingly, the data interpretation system <NUM> returns the interpretation options associated with the previously generated trained models generated in which each interpretation options includes group A and one of subgroups <NUM>, <NUM>, or <NUM>.

As described herein, the trained models are generated using a supervised machine learning technique and are used as part of building a machine-based neural network. As such, the trained models need not necessarily contain the entirety of the input training data, and interpretations for the seismic dataset <NUM> may be predicted based on a subset or portion of the input training data. Alternatively, in some embodiments, an entirety of the input training data may be stored as part of the trained models.

<FIG> illustrates an example block diagram <NUM> for providing multiple different seismic data interpretations, according to an embodiment. As shown in <FIG>, block diagram <NUM> includes a training process <NUM>, and an interpretation process <NUM>. As described herein, the training process <NUM> is used to generate multiple different trained models (e.g., in a similar manner as <FIG>), and the interpretation process <NUM> is used to predict or interpret a given input of seismic data (e.g., in a similar manner as <FIG>). More specifically, the interpretation process <NUM> produces multiple computer-generated interpretation options based on the trained models produced by the training process <NUM>. The processes in the block diagram <NUM> are performed by one or more computing devices, such as a data interpretation system <NUM>.

As shown in <FIG>, the training process <NUM> includes a process step of sorting a training input. As described herein, the training input includes the different interpretations for a given set of training seismic data from multiple different data interpreters. In some embodiments, the sorting function receives the training input and sorts the training input into two groups (e.g., group A and group D) in which group A includes the portions of the different interpretations that are in agreement, and group D includes the portions of the different interpretations that are in disagreement. As further shown in <FIG>, group D is sub-divided into multiple subgroups in which individual subgroups including a common attribute. For example, one subgroup includes interpretations made by interpreters having a common attribute (e.g., interpreters who specialize in seismic data interpretations from a particular geographic region, experience level of the interpreter). Another example subgroup includes interpretations made using a particular type of interpretation technique (e.g., a particular fault detection technique). In some implementations, group D may be sub-divided in any variety of other ways not mentioned herein.

A training function produces a training model based on each subgroup within group D and group A. For example, since group A includes the portion in which the interpretations are in agreement, each trained model is based on group A. The training function applies machine learning techniques to generate each trained model. For example, a first trained model is generated based on applying machine learning techniques with group A and subgroup one as inputs. A second trained model is generated based on applying machine learning techniques with group A and subgroup two as inputs, and so on and so forth. In this way, the trained models are produced using computer-based machine learning techniques and transforming a subjective process of interpreting data into a machine-based non-subjective process. In some embodiments, the training process <NUM> includes a supervised machine learning in which the input data (e.g., input seismic data <NUM>) is applied to a learning algorithm.

Once the trained models have been produced, the trained models are used to predict multiple interpretations of a set of seismic data. For example, as part of the interpretation process <NUM>, a prediction input (e.g., a target set of seismic data to interpreted) is processed using a prediction function. In some implementations, the prediction function applies the training models to the prediction input, and generates multiple outputs (e.g., output <NUM>, output <NUM>, output <NUM>, etc.). Individual outputs are different interpretations of the input seismic data set. In some implementations, the output interpretations may be scored and sorted based on any variety of criteria. As one illustrative example, the output interpretations based on subgroups associated with experts from a particular geographic location may be scored relatively higher. In some implementations, the output interpretations are be displayed and presented so that a data interpreter can view the output interpretations and determine mitigating actions based on a selected output interpretation.

<FIG> and <FIG> illustrate example flowcharts of a process for generating trained models and using the training models to produce multiple computer-generated data interpretation options. More specifically, <FIG> illustrates an example flowchart of a training process for generating trained models (e.g., the training process <NUM> of <FIG>). <FIG> illustrates an example flowchart of an interpretation process (e.g., the interpretation process <NUM> of <FIG>) The blocks of <FIG> and <FIG> may, for example, correspond to the any of the processes described above with respect to <FIG>, and <FIG>. The flowcharts illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. It will be appreciated that the worksteps illustrated in <FIG> and <FIG> may be conducted in an order other than as illustrated and/or two or more worksteps may be combined into a single step, one workstep may be separated into two worksteps, and/or worksteps may be executed in parallel.

As shown in <FIG>, process <NUM> includes receiving a training input (block <NUM>). For example, the data interpretation system <NUM> receives a training input. As described herein, the training input includes multiple different interpretations for a given set of training seismic data (e.g., different interpretations having different attributes, such as interpretation technique, interpreter experience level, interpreters who specialize in seismic data interpretations from a particular geographic region, etc.). In some embodiments, the training input may include any number of predetermined seismic interpretations.

Process <NUM> further includes sorting the training input into a first group and a second group (block <NUM>). For example, the data interpretation system <NUM> sorts the training input into two groups (e.g., group A and group D) in which group A includes the portions of the different interpretations that are in agreement, and group D includes the portions of the different interpretations that are in disagreement.

Process <NUM> also includes subgrouping the second group into multiple subgroups (block <NUM>). For example, the data interpretation system <NUM> subgroups the second group (e.g., group D) into multiple subgroups in which each subgroup includes a common attribute (e.g., interpretation technique, interpreter experience level, interpreters who specialize in seismic data interpretations from a particular geographic region, etc.).

Process <NUM> further includes generating trained models based on the first group and each of the subgroups (block <NUM>). For example, the data interpretation system <NUM> produces a training model based on each subgroup within group D (the second group) and group A (the first group). The data interpretation system <NUM> uses a supervised machine-based training technique to generate the trained models in which the trained models include group A and one of a subgroup within group D.

In some embodiments, blocks <NUM>-<NUM> are repeated for multiple sets of training inputs. In this way, trained models are generated for the individual training inputs in which the individual training inputs are used to generate a set of trained models for providing multiple interpretation options.

Referring to <FIG>, process <NUM> includes receiving a prediction input (block <NUM>). For example, the data interpretation system <NUM> receives a prediction input (e.g., a target set of seismic data to interpreted). In some embodiments, the data interpretation system <NUM> receives the prediction input from any variety of systems as part of an interpretation process or event in which the interpretation of the prediction input is desired.

Process <NUM> further includes applying the trained models to the prediction input (block <NUM>). For example, the data interpretation system <NUM> applies each of the trained models (e.g., generated using the training process of <FIG>) to the prediction input to generate multiple outputs. Applying each of the trained models to the prediction input involves matching the prediction input to the trained models and identifying which trained models were generated based on data matching the prediction input.

Process <NUM> also includes generating multiple interpretation outputs for the prediction input (block <NUM>). For example, the data interpretation system <NUM> generates multiple interpretation outputs for the prediction input. More specifically, the data interpretation system <NUM> identifies the training models that were generated based on data matching the prediction input, and generates interpretation options based on the identified training models. As described herein, each interpretation option includes Group A and one of subgroups within Group D.

Process <NUM> also includes outputting interpretation options for display (block <NUM>). For example, the data interpretation system <NUM> outputs the interpretation options (generated at block <NUM>) for display by a data interpreter. In this way, the data interpreter views the output interpretations and determines mitigating actions based on a selected output interpretation.

In some embodiments, the interpretation options may be scored, and the score may be presented in connection with their respective interpretation options. The interpretation options may be sorted based on their scores. In some embodiments, the interpretation options may be scored based on any variety of scoring rules, such as rules to score interpretations made by interpreters with greater experience levels higher, or rules to score interpretations interpreted using certain techniques higher. Any other variety of scoring rules may be implemented to score the interpretation options.

<FIG> and <FIG> illustrate a flowchart of a method <NUM>, according to an embodiment. It will be appreciated that the worksteps illustrated in <FIG> and <FIG> may be conducted in an order other than as illustrated and/or two or more worksteps may be combined into a single step, one workstep may be separated into two worksteps, and/or worksteps may be executed in parallel.

The method <NUM> includes receiving a training input, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, the training input includes (e.g., different) interpretations of seismic data, as at <NUM> (e.g., as shown at <FIG>).

The method <NUM> also includes sorting the training input into a first group and a second group, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, the first group includes a portion of the training inputs in which the interpretations match and the second group includes a portion of the training data in which the interpretations do not match, as at <NUM> (e.g., as shown at <FIG> and <FIG>).

The method <NUM> further includes subgrouping the second group into a plurality of subgroups, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, individual subgroups of the plurality of subgroups are associated with different interpretation attributes, as at <NUM> (e.g., <FIG>, block <NUM>, one example of individual subgroups of the plurality of subgroups being associated with different interpretation attributes).

The method <NUM> also includes generating a plurality of trained models based on the plurality of subgroups and the first group, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, the trained models are further generated based on a supervised machine learning technique, as at <NUM> (e.g., <FIG> and <FIG>, block <NUM>, one example of trained models further generated based on a supervised machine learning technique).

The method <NUM> further includes receiving a prediction input having a set of data to be interpreted, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, the prediction input includes target seismic data to be interpreted, as at <NUM> (e.g., as shown in <FIG>, and <FIG>, block <NUM>, one example of a prediction input having a set of data to be interpreted).

The method <NUM> also includes generating a plurality of interpretation options for the prediction input by applying the plurality of training models to the prediction input, as at <NUM> (e.g., <FIG>, block <NUM>). In an embodiment, the plurality of interpretation options may be scored, as at <NUM> (e.g., <FIG>, block <NUM>, one example of the scoring the plurality of interpretation options). In an embodiment, the plurality of interpretation options may be sorted based on the scoring, as at <NUM> (e.g., <FIG>, block <NUM>, one example of the sorting the interpretation options d based on the scoring).

The method <NUM> further includes outputting the plurality of interpretation options, as at <NUM> (e.g., <FIG>, block <NUM>).

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.

The methods of the present disclosure are executed by a computing system, such as the data interpretation system <NUM>. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> includes a computer or computer system 801A, which may be an individual computer system 801A or an arrangement of distributed computer systems. The computer system 801A includes one or more analysis module(s) <NUM> configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media <NUM>. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the computer system 801A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 801B, 801C, and/or 801D (note that computer systems 801B, 801C and/or 801D may or may not share the same architecture as computer system 801A, and may be located in different physical locations, e.g., computer systems 801A and 801B may be located in a processing facility, while in communication with one or more computer systems such as 801C and/or 801D that are located in one or more data centers, and/or located in varying countries on different continents).

The storage media <NUM> is implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of <FIG> storage media <NUM> is depicted as within computer system 801A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 801A and/or additional computing systems. Storage media <NUM> may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

In some embodiments, computing system <NUM> contains one or more training and data interpretation module(s) <NUM>. In the example of computing system <NUM>, computer system 801A includes the training and data interpretation module <NUM>. In some embodiments, a single training and data interpretation module <NUM> module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of training and data interpretation modules <NUM> may be used to perform some or all aspects of methods.

It should be appreciated that computing system <NUM> is only one example of a computing system, and that computing system <NUM> may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or computing system <NUM> may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

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 <NUM>, <FIG>), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

Claim 1:
A computer-implemented method (<NUM>) comprising:
receiving a training input (<NUM>; <NUM>), wherein the training input includes a training seismic dataset and multiple different interpretations of that training seismic dataset (<NUM>);
sorting the training input into a first group and a second group (<NUM>; <NUM>), wherein the first group comprises a portion of the training inputs in which the interpretations match and the second group comprises a portion of the training inputs in which the interpretations do not match (<NUM>), wherein the matching interpretations are different interpretations in agreement and the interpretations that do not match are interpretations in disagreement;
subgrouping the second group into a plurality of subgroups (<NUM>; <NUM>), wherein individual subgroups of the plurality of subgroups are associated with interpretation attributes (<NUM>);
generating a plurality of trained models based on the plurality of subgroups and the first group (<NUM>; <NUM>), wherein the trained models are further generated based on a supervised machine learning technique (<NUM>);
receiving a prediction input having a set of data to be interpreted (<NUM>; <NUM>), wherein the prediction input includes target seismic data (<NUM>) to be interpreted (<NUM>), wherein the target seismic data is received from seismic survey operation for a subterranean formation;
generating a plurality of interpretation options for the prediction input by applying the plurality of training models to the prediction input (<NUM>, <NUM>; <NUM>); and
outputting the plurality of interpretation options (<NUM>; <NUM>).