Patent Description:
The present disclosure relates generally to implementing a signature finder.

A seismic survey includes generating an image or map of a subsurface region of the Earth by sending sound energy down into the ground and recording the reflected sound energy that returns from the geological layers within the subsurface region. During a seismic survey, an energy source is placed at various locations on or above the surface region of the Earth, which may include hydrocarbon deposits. Each time the source is activated, the source generates a seismic (e.g., sound wave) signal that travels downward through the Earth, is reflected, and, upon its return, is recorded using one or more receivers disposed on or above the subsurface region of the Earth. The seismic data recorded by the receivers may then be used to create an image or profile of the corresponding subsurface region.

The invention provides a computer implemented method as set out in claim <NUM>, a tangible and non-transitory machine readable medium as set out in claim <NUM>, and a computer system as set out in claim <NUM>.

By way of introduction, seismic data may be acquired using a variety of seismic survey systems and techniques, two of which are discussed with respect to <FIG> and <FIG>. Regardless of the seismic data gathering technique utilized, after the seismic data is acquired, a computing system may analyze the acquired seismic data and may use the results of the seismic data analysis (e.g., seismogram, map of geological formations, etc.) to perform various operations within the hydrocarbon exploration and production industries. For instance, <FIG> illustrates a flow chart of a method <NUM> that details various processes that may be undertaken based on the analysis of the acquired seismic data. Although the method <NUM> is described in a particular order, it should be noted that the method <NUM> may be performed in any suitable order.

Referring now to <FIG>, at block <NUM>, locations and properties of hydrocarbon deposits within a subsurface region of the Earth associated with the respective seismic survey may be determined based on the analyzed seismic data. In one embodiment, the seismic data acquired may be analyzed to generate a map or profile that illustrates various geological formations within the subsurface region. Based on the identified locations and properties of the hydrocarbon deposits, at block <NUM>, certain positions or parts of the subsurface region may be explored. That is, hydrocarbon exploration organizations may use the locations of the hydrocarbon deposits to determine locations at the surface of the subsurface region to drill into the Earth. As such, the hydrocarbon exploration organizations may use the locations and properties of the hydrocarbon deposits and the associated overburdens to determine a path along which to drill into the Earth, how to drill into the Earth, and the like.

After exploration equipment has been placed within the subsurface region, at block <NUM>, the hydrocarbons that are stored in the hydrocarbon deposits may be produced via natural flowing wells, artificial lift wells, and the like. At block <NUM>, the produced hydrocarbons may be transported to refineries and the like via transport vehicles, pipelines, and the like. At block <NUM>, the produced hydrocarbons may be processed according to various refining procedures to develop different products using the hydrocarbons.

It should be noted that the processes discussed with regard to the method <NUM> may include other suitable processes that may be based on the locations and properties of hydrocarbon deposits as indicated in the seismic data acquired via one or more seismic survey. As such, it should be understood that the processes described above are not intended to depict an exhaustive list of processes that may be performed after determining the locations and properties of hydrocarbon deposits within the subsurface region.

With the foregoing in mind, <FIG> is a schematic diagram of a marine survey system <NUM> (e.g., for use in conjunction with block <NUM> of <FIG>) that may be employed to acquire seismic data (e.g., waveforms) regarding a subsurface region of the Earth in a marine environment. Generally, a marine seismic survey using the marine survey system <NUM> may be conducted in an ocean <NUM> or other body of water over a subsurface region <NUM> of the Earth that lies beneath a seafloor <NUM>.

The marine survey system <NUM> may include a vessel <NUM>, one or more seismic sources <NUM>, a (seismic) streamer <NUM>, one or more (seismic) receivers <NUM>, and/or other equipment that may assist in acquiring seismic images representative of geological formations within a subsurface region <NUM> of the Earth. The vessel <NUM> may tow the seismic source(s) <NUM> (e.g., an air gun array) that may produce energy, such as sound waves (e.g., seismic waveforms), that is directed at a seafloor <NUM>. The vessel <NUM> may also tow the streamer <NUM> having a receiver <NUM> (e.g., hydrophones) that may acquire seismic waveforms that represent the energy output by the seismic source(s) <NUM> subsequent to being reflected off of various geological formations (e.g., salt domes, faults, folds, etc.) within the subsurface region <NUM>. Additionally, although the description of the marine survey system <NUM> is described with one seismic source <NUM> (represented in <FIG> as an air gun array) and one receiver <NUM> (represented in <FIG> as a set of hydrophones), it should be noted that the marine survey system <NUM> may include multiple seismic sources <NUM> and multiple receivers <NUM>. In the same manner, although the above descriptions of the marine survey system <NUM> is described with one seismic streamer <NUM>, it should be noted that the marine survey system <NUM> may include multiple streamers similar to streamer <NUM>. In addition, additional vessels <NUM> may include additional seismic source(s) <NUM>, streamer(s) <NUM>, and the like to perform the operations of the marine survey system <NUM>.

<FIG> is a block diagram of a land survey system <NUM> (e.g., for use in conjunction with block <NUM> of <FIG>) that may be employed to obtain information regarding the subsurface region <NUM> of the Earth in a non-marine environment. The land survey system <NUM> may include a land-based seismic source <NUM> and land-based receiver <NUM>. In some embodiments, the land survey system <NUM> may include multiple land-based seismic sources <NUM> and one or more land-based receivers <NUM> and <NUM>. Indeed, for discussion purposes, the land survey system <NUM> includes a land-based seismic source <NUM> and two land-based receivers <NUM> and <NUM>. The land-based seismic source <NUM> (e.g., seismic vibrator) that may be disposed on a surface <NUM> of the Earth above the subsurface region <NUM> of interest. The land-based seismic source <NUM> may produce energy (e.g., sound waves, seismic waveforms) that is directed at the subsurface region <NUM> of the Earth. Upon reaching various geological formations (e.g., salt domes, faults, folds) within the subsurface region <NUM> the energy output by the land-based seismic source <NUM> may be reflected off of the geological formations and acquired or recorded by one or more land-based receivers (e.g., <NUM> and <NUM>).

In some embodiments, the land-based receivers <NUM> and <NUM> may be dispersed across the surface <NUM> of the Earth to form a grid-like pattern. As such, each land-based receiver <NUM> or <NUM> may receive a reflected seismic waveform in response to energy being directed at the subsurface region <NUM> via the seismic source <NUM>. In some cases, one seismic waveform produced by the seismic source <NUM> may be reflected off of different geological formations and received by different receivers. For example, as shown in <FIG>, the seismic source <NUM> may output energy that may be directed at the subsurface region <NUM> as seismic waveform <NUM>. A first receiver <NUM> may receive the reflection of the seismic waveform <NUM> off of one geological formation and a second receiver <NUM> may receive the reflection of the seismic waveform <NUM> off of a different geological formation. As such, the first receiver <NUM> may receive a reflected seismic waveform <NUM> and the second receiver <NUM> may receive a reflected seismic waveform <NUM>.

Regardless of how the seismic data is acquired, a computing system (e.g., for use in conjunction with block <NUM> of <FIG>) may analyze the seismic waveforms acquired by the receivers <NUM>, <NUM>, <NUM> to determine seismic information regarding the geological structure, the location and property of hydrocarbon deposits, and the like within the subsurface region <NUM>. <FIG> is a block diagram of an example of such a computing system <NUM> that may perform various data analysis operations to analyze the seismic data acquired by the receivers <NUM>, <NUM>, <NUM> to determine the structure and/or predict seismic properties of the geological formations within the subsurface region <NUM>.

Referring now to <FIG>, the computing system <NUM> may include a communication component <NUM>, a processor <NUM>, memory <NUM>, storage <NUM>, input/output (I/O) ports <NUM>, and a display <NUM>. In some embodiments, the computing system <NUM> may omit one or more of the display <NUM>, the communication component <NUM>, and/or the input/output (I/O) ports <NUM>. The communication component <NUM> may be a wireless or wired communication component that may facilitate communication between the receivers <NUM>, <NUM>, <NUM>, one or more databases <NUM>, other computing devices, and/or other communication capable devices. In one embodiment, the computing system <NUM> may receive receiver data <NUM> (e.g., seismic data, seismograms, etc.) via a network component, the database <NUM>, or the like. The processor <NUM> of the computing system <NUM> may analyze or process the receiver data <NUM> to ascertain various features regarding geological formations within the subsurface region <NUM> of the Earth.

The processor <NUM> may be any type of computer processor or microprocessor capable of executing computer-executable code. The processor <NUM> may also include multiple processors that may perform the operations described below. The memory <NUM> and the storage <NUM> may be any suitable articles of manufacture that can serve as media to store processor-executable code, data, or the like. These articles of manufacture may represent computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor <NUM> to perform the presently disclosed techniques. Generally, the processor <NUM> may execute software applications that include programs that process seismic data acquired via receivers of a seismic survey according to the embodiments described herein.

With one or more embodiments, processor <NUM> can instantiate or operate in conjunction with one or more classifiers. With one or more embodiments, the classifiers can be linear classifiers (such as, for example, Multi-Layer Perception classifiers), support vector classifiers, and/or quadratic classifiers, for example. With another embodiment, the classifier can be implemented by using neural networks. The one or more neural networks can be software-implemented or hardware-implemented. One or more of the neural networks can be a convolutional neural network.

With one or more embodiments, these classifiers can provide responses to different inputs. The process by which a classifier learns and responds to different inputs may be generally referred to as a "training" process.

The memory <NUM> and the storage <NUM> may also be used to store the data, analysis of the data, the software applications, and the like. The memory <NUM> and the storage <NUM> may represent non-transitory computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor <NUM> to perform various techniques described herein. It should be noted that non-transitory merely indicates that the media is tangible and not a signal.

The I/O ports <NUM> may be interfaces that may couple to other peripheral components such as input devices (e.g., keyboard, mouse), sensors, input/output (I/O) modules, and the like. I/O ports <NUM> may enable the computing system <NUM> to communicate with the other devices in the marine survey system <NUM>, the land survey system <NUM>, or the like via the I/O ports <NUM>.

The display <NUM> may depict visualizations associated with software or executable code being processed by the processor <NUM>. In one embodiment, the display <NUM> may be a touch display capable of receiving inputs from a user of the computing system <NUM>. The display <NUM> may also be used to view and analyze results of the analysis of the acquired seismic data to determine the geological formations within the subsurface region <NUM>, the location and property of hydrocarbon deposits within the subsurface region <NUM>, predictions of seismic properties associated with one or more wells in the subsurface region <NUM>, and the like. The display <NUM> may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. In addition to depicting the visualization described herein via the display <NUM>, it should be noted that the computing system <NUM> may also depict the visualization via other tangible elements, such as paper (e.g., via printing) and the like.

With the foregoing in mind, the present techniques described herein may also be performed using a supercomputer that employs multiple computing systems <NUM>, a cloud-computing system, or the like to distribute processes to be performed across multiple computing systems <NUM>. In this case, each computing system <NUM> operating as part of a super computer may not include each component listed as part of the computing system <NUM>. For example, each computing system <NUM> may not include the display <NUM> since multiple displays <NUM> may not be useful to for a supercomputer designed to continuously process seismic data.

After performing various types of seismic data processing, the computing system <NUM> may store the results of the analysis in one or more databases <NUM>. The databases <NUM> may be communicatively coupled to a network that may transmit and receive data to and from the computing system <NUM> via the communication component <NUM>. In addition, the databases <NUM> may store information regarding the subsurface region <NUM>, such as previous seismograms, geological sample data, seismic images, and the like regarding the subsurface region <NUM>.

Although the components described above have been discussed with regard to the computing system <NUM>, it should be noted that similar components may make up the computing system <NUM>. Moreover, the computing system <NUM> may also be part of the marine survey system <NUM> or the land survey system <NUM>, and thus may monitor and control certain operations of the seismic sources <NUM> or <NUM>, the receivers <NUM>, <NUM>, <NUM>, and the like. Further, it should be noted that the listed components are provided as example components and the embodiments described herein are not to be limited to the components described with reference to <FIG>.

In some embodiments, the computing system <NUM> may generate a two-dimensional representation or a three-dimensional representation of the subsurface region <NUM> based on the seismic data received via the receivers mentioned above. Additionally, seismic data associated with multiple source/receiver combinations may be combined to create a near continuous profile of the subsurface region <NUM> that can extend for some distance. In a two-dimensional (<NUM>-D) seismic survey, the receiver locations may be placed along a single line, whereas in a three-dimensional (<NUM>-D) survey the receiver locations may be distributed across the surface in a grid pattern. As such, a <NUM>-D seismic survey may provide a cross sectional picture (vertical slice) of the Earth layers as they exist directly beneath the recording locations. A <NUM>-D seismic survey, on the other hand, may create a data "cube" or volume that may correspond to a <NUM>-D picture of the subsurface region <NUM>.

In addition, a <NUM>-D (or time-lapse) seismic survey may include seismic data acquired during a <NUM>-D survey at multiple times. Using the different seismic images acquired at different times, the computing system <NUM> may compare the two images to identify changes in the subsurface region <NUM>.

In any case, a seismic survey may be composed of a very large number of individual seismic recordings or traces. As such, the computing system <NUM> may be employed to analyze the acquired seismic data to obtain an image representative of the subsurface region <NUM> and to determine locations and properties of hydrocarbon deposits. To that end, a variety of seismic data processing algorithms may be used to remove noise from the acquired seismic data, migrate the pre-processed seismic data, identify shifts between multiple seismic images, align multiple seismic images, and the like.

After the computing system <NUM> analyzes the acquired seismic data, the results of the seismic data analysis (e.g., seismogram, seismic images, map of geological formations, etc.) may be used to perform various operations within the hydrocarbon exploration and production industries. For instance, as described above, the acquired seismic data may be used to perform the method <NUM> of <FIG> that details various processes that may be undertaken based on the analysis of the acquired seismic data.

In one embodiment, the computing system <NUM> analyzes the acquired seismic data utilizing a signature finder operation (e.g., a program or code stored, for example, in the storage <NUM> and/or the memory <NUM> and executed by the processor <NUM>) that is based on a machine learning workflow is implemented to help recognize user-defined patterns in the seismic data. These user-defined patterns can be prospects like hydrocarbon sand, brine sand, any geologic features or seismic noise.

One technique for this is to manually look into different seismic attribute volumes and try to find prospects or de-risk prospects. However manual interpretation many times has user biased and interpretations would differ from experienced vs. non experienced geoscientists. Sometimes, in unknown areas (e.g., exploration prospects or where there is little knowledge of the reservoir), such user/interpreter for data selection bias can lead to wrong interpretations. Also, the manual process is cumbersome and takes time. A signature finder operation is an efficient way to include an increased number of seismic information for analysis and removes user bias in identifying patterns in the seismic data, thus de-risking prospects while increasing the accuracy of the interpretations.

With one or more embodiments, as a signature finder operation was being developed, case studies were run to find the presence of prospective sands in several surveys from North Sea, Mauritanian Senegal, Trinidad and Egypt. Each of the case studies was successful in determining prospective sands based on selected patterns/targets, considering the limitations from the seismic data.

With one or more embodiments, a signature finder operation is based on an unsupervised clustering-based technique followed by image match technique. <FIG> illustrates one example of the signature finder operation as a flow chart <NUM>, which may illustrate the signature finder operation as a program performed on the computing system <NUM> to analyze acquired seismic data (e.g., performed as code stored on a tangible and non-transitory machine readable medium, such as the memory <NUM> and/or the storage <NUM>, that when in operation causes the processor <NUM> to perform one or more of the steps of the flow chart <NUM> as performance of the signature finder operation). Generally, flow chart <NUM> includes step <NUM> as accessing and/or receiving input seismic data, step <NUM> as categorizing attributes of that data, step <NUM> as generating different combinations of the attributes, step <NUM> as performing run clustering for the combinations, step <NUM> as selecting signature boxes, step <NUM> as representing the signature boxes, step <NUM> as acceptance or elimination of attribute combinations, and step <NUM> as generation of output(s).

As illustrated, step <NUM> includes accessing or receiving input seismic data. This input seismic data can be, for example, a set (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or more) of amplitude volumes that may each have one or more seismic attributes, for example, it can include a full stack, a partial stack, a mid stack, a far stack (of processed seismic records), it may include spectral decomposition volumes, it may include structural attributes, and/or other seismic data. The data of step <NUM> thus may represent processed seismic data as in input to the signature finder operation.

In step <NUM>, the input data from step <NUM> are categorized. This categorization of the input data may include subgroups for each item of seismic data (e.g., each seismic attribute dataset from step <NUM>). In one embodiment, the subgroups include seismic amplitude data (e.g., full stack, near angle, mid angle, far angle, ultra far, blued seismic, etc. as attributes), intercept and gradient (e.g., acoustic impedance, gradient impedance, etc. as attributes), seismic attributes (e.g., envelope, quadrature, etc. as attributes), inverted seismic attributes as attributes, and/or other subgroups having attributes associated therewith. In some embodiments, the above categorized attributes can, accordingly, include seismic full stacks, seismic angle stacks, inverted stacks, rock physics volumes, spectral decomposed data, volumetric seismic attributes, etc. each organized into their respective subgroups. In addition to this, pre-stack seismic gathers can also be added to the set of input data.

In step <NUM>, the processor <NUM> (or the processor executing code to affect the signature finder operation) operates to select one or more attributes categorized in step <NUM> to generate a realization of attributes. It should be noted that in some embodiments, this selection may be randomized so that the attributes selected from each group are chosen at random. In one embodiment, a single attribute from each group is selected to generate the realization of attributes. In another embodiment, one or more attributes are selected from each group to generate the realization of attributes. In a further embodiment, one or one or more attributes are selected from each group or less than the total number of groups to generate the realization of attributes. This selection process can be performed a number of times, for example, approximately <NUM> times, <NUM> times, <NUM> times, a number of times more than <NUM> times, or another number of times to generate corresponding realization of attributes. Indeed, in some embodiments, all possible combinations of attributes allowed by the selection constraints (i.e., how many groups can be selected from and how many allowed attributes from each group can be selected) may be generated in step <NUM>. Likewise, a predetermined number of realization of attributes can be generated in step <NUM>.

Thus, the selection and generation of a plurality of realization of attributes is performed in step <NUM>, creating a plurality of different combinations of the input attributes. In some embodiments, a quality control step may be performed during step <NUM> in which the quality of the contributing attributes for a given realization of attributes are each checked against a given threshold for the respective contributing attribute and if one or more of the contributing attributes does not meet a required quality measure (e.g., threshold value or other reliability measure), the generated realization of attributes may be discarded or, for example, provided a lowered weighting value. The plurality of different combinations (as represented by the distinct realization of attributes) will be carried forward in the process for unsupervised cluster analysis and signature correlation step. As will be subsequently described, there will be options to accept or element various combinations. Accordingly, step <NUM> represents an automatic process that operates to improve the results of the signature finder operation by removing user bias from the selection of seismic attributes (i.e., a user does not determine specific attributes to form the realization of attributes and, thus, does not impart user bias into its formation).

In step <NUM>, cluster analysis is performed on the plurality of generated combinations (realization of attributes) from step <NUM> as multi-attribute seismic facies classification. In some embodiments, the cluster analysis is an unsupervised cluster analysis, for example, a robust unsupervised cluster analysis, that is performed on each of the different combinations of the attribute dataset (realization of attributes). In one embodiment, the robust unsupervised cluster analysis is a Gaussian Mixture Model (GMM) Classification. In another embodiment, other unsupervised clustering algorithms, such as Self-organizing Maps (SoM) can also be used instead of GMM. Regardless, the cluster analysis is performed and operates to generate a plurality of unsupervised seismic facies volumes. An example of a seismic facies volume <NUM> that is generated is illustrated in <FIG> and a second example of a seismic facies volume <NUM> that is generated is illustrated in <FIG>.

While two examples of seismic facies volumes <NUM> and <NUM> are illustrated respectively in <FIG>, during step <NUM>, more than two seismic facies volumes are typically generated in step <NUM> of <FIG>. For each of the <NUM>' of generated combinations (realization of attributes) from step <NUM> an unsupervised seismic facies volume will be created. In some embodiments, the number of clusters or seismic facies are specified as predetermined value(s) and in some embodiments, the number of clusters are over defined.

Thus step <NUM> operates to run unsupervised clustering over a plurality (for example, <NUM>) different attribute combination scenarios using automatically selected and compiled attributes. This additionally operates to generate a greater number and variety of seismic facies relative to user selected seismic attribute volumes that are then clustered, which leads to a greater dataset that can be used in conjunction with step <NUM> of the signature finder operation.

In step <NUM>, a region of interest 2D (<NUM>-dimensional) signature patch (or a plurality of patches) are identified, for example, in a seismic volume of interest (i.e., the seismic data to be interpreted and from which a pattern to be matched is recognized as the ROI). This may be performed automatically by the computing system <NUM> (e.g., by the processor <NUM> or the processor executing code). In some embodiments the automated selection process by the computing system <NUM> can be based on, for example, drilled wells present in the region. Alternatively, a user may provide one or more inputs to the computing system <NUM> to select the signature patch/patches. These defined patterns as the ROI, i.e., signatures, can be prospects like hydrocarbon sand, brine sand, any geologic features or seismic noise. With one or more embodiments, an interpreter provides small signatures of the prospects or the region of interest. The signatures are identified in the input seismic data/attribute data. In some embodiments, there is no size limitation of the signatures, however, a focused signature may be preferred.

After one (or more than one) signature is identified, the identified signature(s) are projected on each of the unsupervised clustered volumes, which have been generated in step <NUM> as discussed above. <FIG> illustrates an example of a portion <NUM> of the facies volume <NUM> onto which the identified signature is projected (i.e., to determine correspondence between the two) and <FIG> also illustrates a portion <NUM> of the facies volume <NUM> onto which the identified signature is projected (i.e., to determine correspondence between the two). This projection represents a set of data points and in some embodiments, the process can be repeated for each of the facies volume <NUM> and the facies volume <NUM> with respect to additional portions of the facies volume <NUM> and the facies volume <NUM>.

<FIG> illustrates an example of a 2D (<NUM>-dimensional) signature patch <NUM> illustrative of a seismic facies pattern when projected onto a respective unsupervised seismic facies volume (i.e., one of the signatures described above with respect to <FIG>). Likewise, <FIG> illustrates another example of a 2D signature patch <NUM> illustrative of a seismic facies pattern when projected onto a second respective unsupervised seismic facies volume (e.g., whereby the projection may include the seismic facies pattern overlaid or otherwise compared on a location by location basis with the second respective unsupervised seismic facies volume). As illustrated in <FIG> and <FIG>, the signature patch <NUM> shows a different seismic facies pattern than the signature patch <NUM>, which corresponds to the signature being projected on the different respective unsupervised seismic facies volumes. Furthermore, in some embodiments, instead of and/or in addition to generating 2D signature patches, 3D signature patches can also be defined.

Returning to <FIG>, in step <NUM> the 2D signatures for are then reduced to a 1D (<NUM>-dimensional) histogram. <FIG> illustrates an example of a 1D histogram <NUM> that shows the population of each of the seismic facies (clusters) present in the respective 2D signature patch <NUM>. Similarly, <FIG> illustrates an example of a 1D histogram <NUM> that shows the population of each of the seismic facies (clusters) present in the respective 2D signature patch <NUM>. The 1D histograms are then used to evaluate matches with respect to the whole dataset (i.e., evaluated in the clustering volumes of step <NUM>) in step <NUM>. This process operates to determine where the 1D histograms correlate most and where they least correlate with the data generated in step <NUM>. In some embodiments, the 1D histograms may also connote or otherwise represent a location in the 2D patch in which the each of the seismic facies (clusters) is present. It should be noted that the use of 1D histograms simplifies the spatial relation of the seismic facies/clusters in the patch so instead, in some embodiments, the whole 2D patch can also be cross-correlated (which results in better geological constraints) with the data generated in step <NUM>. Furthermore, the 1D histograms are merely a representation, however, alternative groupings of the data may be undertaken.

In this manner, for each of the box signatures, a histogram will be created for each of the clustered volumes which will then be matched with all over the dataset of the clustered outputs. Thus, steps <NUM> and <NUM> of <FIG> operate to compare a ROI or signature input into the signature finder operation program and the image match operation searches for similar seismic facies patterns across the dataset generated in step <NUM>. As discussed above, the user/interpreter identifies the ROIs (e.g., signature patches). These signatures are then correlated in all the locations of the selected survey area. The signature patches from each of dataset generated in step <NUM> are correlated with the data from which it was extracted (respective unsupervised seismic facies/clustered volume). Moreover, correlation can be based on a 1D histogram correlation, as described above. This process creates the correlation volumes for all the different unsupervised seismic facies volumes.

In step <NUM>, based on the type of signature patches, some of the correlation volumes are selected and some of the correlation volumes are eliminated. <FIG> illustrates an example of an accepted signature <NUM> while <FIG> illustrates an example of a declined signature <NUM>. Whether the correlation volume is accepted or declined (i.e., eliminated) in step <NUM> of <FIG> is based on, for example, a predetermined or otherwise pre-defined threshold value, which may be, for example, a cut off of the ratio between the un-correlated vs. correlated regions of the data. Moreover, in some embodiments, geological input is preferred to have more insight of the selection process. Additionally, in some embodiments, acceptance and declining of correlation data may be performed fully by the computing system <NUM>, by a user, or a combination thereof (e.g., the computing system can disregard all correlation data that does not meet one or more threshold values and a user can choose from the remaining correlation data which will be finally accepted).

The selection process of step <NUM> results in selection of few list of the attribute combinations which are relevant for a signature. In most of the cases only a handful of attribute combination lists are selected. That is, based on the interpretation and the cutoff applied in step <NUM>, unrealistic and/or undesirable cases are eliminated. Thus, step <NUM> reduces the seismic attribute combinations to few handful/relevant list of attributes and this process can be performed, for example using user interpretation and/or prior provided (predetermined) information.

In this manner, step <NUM> results in a list of accepted correlation volumes for a given signature. Similarly, step <NUM> can be repeated for other signatures to generate respective lists of correlation volumes. As previously noted, attribute selection is one of the major aspects of unsupervised cluster analysis. The techniques of the signature finder operation described herein operates to reduce the large set of seismic attributes to only the relevant ones and finding the relevant seismic attributes for a given signature.

Step <NUM> represents generation of outputs based on the reduced set of correlation volumes for a given signature. In one embodiment a weighted mean of the valid (e.g., selected) cases is performed in conjunction with step <NUM> to create the final most likely signature volume corresponding to one defined signature box (i.e., each ROI). This process can be repeated for each ROI (i.e., each defined signature box). This results in final most likely signature volumes being generates corresponding to each individual ROI Box or signature patch. Thus, in one or more embodiments, a weighted average of the "good" or reasonable cases can again, based on user interpretation and/or prior information, be applied to create a weighted mean final most likely signature volume. Furthermore, in some embodiments, a predetermined number of such individual most likely signature volumes can be combined to create one single output merge volume that highlights the likelihood of different pattern boxes as a portion of step <NUM>.

That is, at the end of the signature finding process of step <NUM>, one, two, or more of these mean most likely signature volumes are combined to create one single output volume that highlights the likelihood of different patterns in step <NUM>. In one embodiment, the accepted correlation volumes for a given signature from step <NUM> are merged in step <NUM> using simple mean. In another embodiment, the accepted correlation volumes for a given signature from step <NUM> are combined by using weighted mean using the weights from the ratio of un-correlated sections vs. highly correlated sections. Two such final correlated volumes are illustrated in <FIG>, which shows volume <NUM> as representative of an individual most likely signature volume from a first signature and shows volume <NUM> as representative of an individual most likely signature volume from a second signature. In some embodiments, in step <NUM>, these values may be particularly generated and/or transmitted or presented to a user.

Likewise, in some embodiments, two or more final correlated volumes from the respective signatures can be merged to create one seismic volume which will highlight most likely regions for each of the signature. <FIG> illustrates volume <NUM> illustrative of the most likely signature volume as a merged volume of volume <NUM> and <NUM>. As illustrated, regions <NUM> and <NUM> generally correspond to the most likely signature from volume <NUM> while regions <NUM> and <NUM> generally correspond to the most likely signature from volume <NUM> in the volume <NUM>. In some embodiments, in step <NUM>, volume <NUM> may be particularly generated and/or transmitted or presented to a user in conjunction with and/or separate from volumes <NUM> and <NUM>.

Returning to step <NUM>, in some embodiments, it is also possible to identify the most contributing seismic attributes for the accepted/relevant unsupervised seismic facies/cluster volumes. The dataset used for this process is the unsupervised seismic facies results as labels to the corresponding input set of seismic attributes. In one embodiment, SHAP (SHapley Additive exPlanations) is utilized to identify the most contributing seismic attributes for the accepted/relevant unsupervised seismic facies/cluster volumes (i.e., which features are the most useful and which features are less useful). The goal of SHAP is to explain the unsupervised facies results. Use of SHAP values to explain the results generated and output in step <NUM> can thus be achieved, by generating results in step <NUM> that illustrate which features or seismic attributes contributed more and which features or seismic attributes contribute less to the unsupervised facies results.

<FIG> illustrates an example of a volume <NUM> representative of one of the determined relevant classification volumes generated in step <NUM> as well as a focused signature region <NUM> thereof. <FIG> illustrates a bar plot <NUM> of seismic attribute importance for the data generated in step <NUM> broken down into seismic facies classes. In this manner, the bar plot <NUM> can illustrate the mean SHAP Value for all the seismic attributes in a representative manner (e.g., color-coded by the contribution from each seismic facies or clusters). Sorting of the importance in the bar plot <NUM> can be performed in a manner whereby the most contributing seismic attributes will be at the top, as illustrated, or in other manners.

<FIG> illustrates bar plot <NUM> illustrating the mean SHAP value for all the seismic attributes in a representative manner (e.g., color-coded by the contribution from each seismic facies or clusters) coming from the Selected Region of Interest (Signature box), i.e., the focused signature region <NUM> of <FIG>. In this manner, bar plot <NUM> illustrates attribute importance for focused signatures broken down into classes.

<FIG> illustrates a graph representing how each seismic facies class can be further studied on how much each seismic attribute contributed to it in positive and negative manner. That is, <FIG> illustrates the attribute importance for a relevant seismic class from the focused signature region <NUM> of <FIG>. Taken together, the examples represented in <FIG> illustrate an example of the use of SHAP to identify the most contributing seismic attributes for the accepted/relevant unsupervised seismic facies/cluster volumes as well as the results that can be generated and/or transmitted or presented to a user in step <NUM>. The use of SHAP also helps to create an insightful result where the users can back-track the validity of the results and makes the whole signature finder workflow much more transparent and explainable workflow rather than being a black-box.

In other embodiments, increases in speed may be desired. Accordingly, the signature finder operation discussed above can me modified, for example, provide approximate insight to the attributes relevant for the unsupervised seismic facies/cluster volume. For example, permutation feature importance can be generated from running a gradient booster classification on the unsupervised seismic facies results in step <NUM>. An example of seismic attribute/feature importance using permutation techniques is illustrated in chart <NUM> of <FIG>. In another embodiment, utilization of random forest feature importance can be applied, for example, in step <NUM>.

Additionally, in some embodiments, a final product of the signature finder operation in step <NUM> may also provide a list of seismic attributes that have the largest contribution to a given signature. <FIG> illustrates a plot <NUM> in which the seismic attributes that have the largest contribution to a given signature are sorted and presented with the most relevant attribute at the top. This illustrates an example of an output from step <NUM> that can be generated and/or transmitted or presented to a user. The outputs generated in step <NUM> can be useful in helping a geoscientist to go back to the seismic attribute volumes, which has physical/rock properties meaning, to find the cause of a given signature or interpret the final signature volume.

The signature finder operation represents a technique based on machine learning and pattern finding which searches for a given signature in large seismic survey areas simultaneously in a large seismic attribute space, which is otherwise that is not possible by manual analysis/interpretation. The above described operation helps is increasing accuracy in finding a pattern all over the dataset, since in operation is can simultaneously explore approximately <NUM>, <NUM>, <NUM> or more input seismic attribute volumes (or mathematically - dimensional space). With manual interpretation, there is a possibility to overlook patterns/prospects in a huge dataset and there is typically only a handful (e.g., approximately <NUM>) simultaneously investigated seismic attributes or dimensional space. The signature finder operation is not similarly constrained in terms of data dimensionality space or overlooking areas of similar patterns, which might be hydrocarbon prospects. That is, through use of a greater number of input seismic attribute volumes, results can be generated that otherwise are not possible to generate.

The signature finder operation additionally minimize the initial interpretation bias by automatically producing the best set of seismic attributes for a given signature (i.e., from step <NUM> of <FIG>). A user is thus freed to focus more on post analysis of the volumes out from the signature finder operation. Also, the signature finder operation generates as results (e.g., in step <NUM> of <FIG>) the contributions or importance of each of the seismic attributes in generating the results, as an option to provide to a user, which makes the whole process much more explainable.

Claim 1:
A computer-implemented method, comprising:
automatically selecting (<NUM>) a first plurality of seismic attributes corresponding to seismic data as first selected seismic attributes;
combining (<NUM>) the first selected seismic attributes into a first realization of attributes;
automatically selecting (<NUM>) a second plurality of seismic attributes corresponding to the seismic data as second selected seismic attributes;
combining (<NUM>) the second selected seismic attributes into a second realization of attributes;
performing (<NUM>) a first cluster analysis on the first realization of attributes to generate a first clustered volume;
performing (<NUM>) a second cluster analysis on the second realization of attributes to generate a second clustered volume, the method being characterized in comprising:
selecting (<NUM>) a region of interest, ROI, <NUM>-dimensional signature patch in the seismic data;
projecting (<NUM>) the ROI <NUM>-dimensional signature patch onto the first clustered volume to generate a first signature;
projecting (<NUM>) the ROI <NUM>-dimensional signature patch onto the second clustered volume to generate a second signature;
determining (<NUM>) a first level of correlation between the ROI <NUM>-dimensional signature patch and the first signature;
determining (<NUM>) a second level of correlation between the ROI <NUM>-dimensional signature patch and the second signature;
determining (<NUM>) whether the first level of correlation between the ROI <NUM>-dimensional signature patch and the first signature exceeds a predetermined threshold and outputting a first correlation volume corresponding to the first signature when the first level of correlation between the ROI <NUM>-dimensional signature patch and the first signature exceeds the predetermined threshold; and
determining (<NUM>) whether the second level of correlation between the ROI <NUM>-dimensional signature patch and the second signature exceeds the predetermined threshold and outputting a second correlation volume corresponding to the second signature when the second level of correlation between the ROI <NUM>-dimensional signature patch and the second signature exceeds the predetermined threshold, wherein the first and second correlation volumes corresponding respectively to the first and second signatures are used to locate and produce hydrocarbons from a subsurface region corresponding to the ROI <NUM>-dimensional signature patch.