Patent Publication Number: US-2020292723-A1

Title: Method and Apparatus for Automatically Detecting Faults Using Deep Learning

Description:
This application claims priority to U.S. Provisional patent application No. 62/817,338, filed with the United States Patent and Trademark Office on Mar. 12, 2019 and entitled “Method and Apparatus for Automatically Detecting Faults,” the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The present disclosure relates generally to analyzing seismic data, and more specifically, to detecting faults in prediction of reservoir properties. 
     This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. 
     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. 
     Upon creation of an image or profile of a subsurface region, these images and/or profiles can be used to interpret characteristics of a formation (such as, the faults of a formation, for example). Identifying faults in seismic images is important for the oil and gas industry. Faults can be both seal zones which trap hydrocarbons and baffle zones that cause reservoir compartmentalization. Therefore, fault interpretation is an important process in both exploration and reservoir development. However, current fault interpretation techniques can be labor intensive, costly, and/or time consuming. 
     SUMMARY 
     A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. 
     In the course of interpreting images to identify hydrocarbon deposits, interpreters attempt to identify subsurface faults. Faults are generally understood as being a discontinuity in a portion of rock, where the discontinuity may be caused by subsurface movement, for example. 
     Interpreters seek to identify faults because identifying the location of faults can aid in identifying oil/gas traps. For example, direct hydrocarbon indicators can be located against/near faults in certain regions. Further, interpreters seek to identify faults because fault detection can be important for reservoir modelling and well planning. For example, certain faults can create drilling hazards. 
     Fault identifying/mapping can be a labor-intensive process. As such, it may be desirable to speed up the mapping of faults so that interpreters can look at each reservoir more quickly, and so that interpreters can look at more reservoirs. 
     In view of the above, one or more embodiments of the present invention are directed to performing automated fault detection. One or more embodiments can perform automated fault detection within a three-dimensional subsurface volume. One or more embodiments can implement fault detection by using deep learning. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which: 
         FIG. 1  illustrates a flow chart of various processes that may be performed based on analysis of seismic data acquired via a seismic survey system; 
         FIG. 2  illustrates a marine survey system in a marine environment; 
         FIG. 3  illustrates a land survey system in a land environment; 
         FIG. 4  illustrates a computing system that may perform operations described herein based on data acquired via the marine survey system of  FIG. 2  and/or the land survey system of  FIG. 3 ; 
         FIG. 5  illustrates a flow chart of a method that implements one or more embodiments; 
         FIG. 6  illustrates an example of the implementation of the method of  FIG. 5 ; 
         FIG. 7  illustrates an embodiment of a neural network of  FIG. 6 ; and 
         FIG. 8  illustrates an embodiment of a Convolutional Neural Network (CNN) architecture that can be used in conjunction with  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS 
     One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     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. 2  and  FIG. 3 . 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. 1  illustrates a flow chart of a method  10  that details various processes that may be undertaken based on the analysis of the acquired seismic data. Although the method  10  is described in a particular order, it should be noted that the method  10  may be performed in any suitable order. 
     Referring now to  FIG. 1 , at block  12 , 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  14 , 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  16 , the hydrocarbons that are stored in the hydrocarbon deposits may be produced via natural flowing wells, artificial lift wells, and the like. At block  18 , the produced hydrocarbons may be transported to refineries and the like via transport vehicles, pipelines, and the like. At block  20 , 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  10  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. 2  is a schematic diagram of a marine survey system  22  (e.g., for use in conjunction with block  12  of  FIG. 1 ) 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  22  may be conducted in an ocean  24  or other body of water over a subsurface region  26  of the Earth that lies beneath a seafloor  28 . 
     The marine survey system  22  may include a vessel  30 , one or more seismic sources  32 , a (seismic) streamer  34 , one or more (seismic) receivers  36 , and/or other equipment that may assist in acquiring seismic images representative of geological formations within a subsurface region  26  of the Earth. The vessel  30  may tow the seismic source(s)  32  (e.g., an air gun array) that may produce energy, such as sound waves (e.g., seismic waveforms), that is directed at a seafloor  28 . The vessel  30  may also tow the streamer  34  having a receiver  36  (e.g., hydrophones) that may acquire seismic waveforms that represent the energy output by the seismic source(s)  32  subsequent to being reflected off of various geological formations (e.g., salt domes, faults, folds, etc.) within the subsurface region  26 . Additionally, although the description of the marine survey system  22  is described with one seismic source  32  (represented in  FIG. 2  as an air gun array) and one receiver  36  (represented in  FIG. 2  as a set of hydrophones), it should be noted that the marine survey system  22  may include multiple seismic sources  32  and multiple receivers  36 . In the same manner, although the above descriptions of the marine survey system  22  is described with one seismic streamer  34 , it should be noted that the marine survey system  22  may include multiple streamers similar to streamer  34 . In addition, additional vessels  30  may include additional seismic source(s)  32 , streamer(s)  34 , and the like to perform the operations of the marine survey system  22 . 
       FIG. 3  is a block diagram of a land survey system  38  (e.g., for use in conjunction with block  12  of  FIG. 1 ) that may be employed to obtain information regarding the subsurface region  26  of the Earth in a non-marine environment. The land survey system  38  may include a land-based seismic source  40  and land-based receiver  44 . In some embodiments, the land survey system  38  may include multiple land-based seismic sources  40  and one or more land-based receivers  44  and  46 . Indeed, for discussion purposes, the land survey system  38  includes a land-based seismic source  40  and two land-based receivers  44  and  46 . The land-based seismic source  40  (e.g., seismic vibrator) that may be disposed on a surface  42  of the Earth above the subsurface region  26  of interest. The land-based seismic source  40  may produce energy (e.g., sound waves, seismic waveforms) that is directed at the subsurface region  26  of the Earth. Upon reaching various geological formations (e.g., salt domes, faults, folds) within the subsurface region  26  the energy output by the land-based seismic source  40  may be reflected off of the geological formations and acquired or recorded by one or more land-based receivers (e.g.,  44  and  46 ). 
     In some embodiments, the land-based receivers  44  and  46  may be dispersed across the surface  42  of the Earth to form a grid-like pattern. As such, each land-based receiver  44  or  46  may receive a reflected seismic waveform in response to energy being directed at the subsurface region  26  via the seismic source  40 . In some cases, one seismic waveform produced by the seismic source  40  may be reflected off of different geological formations and received by different receivers. For example, as shown in  FIG. 3 , the seismic source  40  may output energy that may be directed at the subsurface region  26  as seismic waveform  48 . A first receiver  44  may receive the reflection of the seismic waveform  48  off of one geological formation and a second receiver  46  may receive the reflection of the seismic waveform  48  off of a different geological formation. As such, the first receiver  44  may receive a reflected seismic waveform  50  and the second receiver  46  may receive a reflected seismic waveform  52 . 
     Regardless of how the seismic data is acquired, a computing system (e.g., for use in conjunction with block  12  of  FIG. 1 ) may analyze the seismic waveforms acquired by the receivers  36 ,  44 ,  46  to determine seismic information regarding the geological structure, the location and property of hydrocarbon deposits, and the like within the subsurface region  26 .  FIG. 4  is a block diagram of an example of such a computing system  60  that may perform various data analysis operations to analyze the seismic data acquired by the receivers  36 ,  44 ,  46  to determine the structure and/or predict seismic properties of the geological formations within the subsurface region  26 . 
     Referring now to  FIG. 4 , the computing system  60  may include a communication component  62 , a processor  64 , memory  66 , storage  68 , input/output (I/O) ports  70 , and a display  72 . In some embodiments, the computing system  60  may omit one or more of the display  72 , the communication component  62 , and/or the input/output (I/O) ports  70 . The communication component  62  may be a wireless or wired communication component that may facilitate communication between the receivers  36 ,  44 ,  46 , one or more databases  74 , other computing devices, and/or other communication capable devices. In one embodiment, the computing system  60  may receive receiver data  76  (e.g., seismic data, seismograms, etc.) via a network component, the database  74 , or the like. The processor  64  of the computing system  60  may analyze or process the receiver data  76  to ascertain various features regarding geological formations within the subsurface region  26  of the Earth. 
     The processor  64  may be any type of computer processor or microprocessor capable of executing computer-executable code or instructions to implement the methods described herein. The processor  64  may also include multiple processors that may perform the operations described below. The memory  66  and the storage  68  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  64  to perform the presently disclosed techniques. Generally, the processor  64  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  64  can instantiate or operate in conjunction with one or more 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 neural networks can provide responses to different inputs. The process by which a neural network learns and responds to different inputs may be generally referred to as a “training” process. 
     The memory  66  and the storage  68  may also be used to store the data, analysis of the data, the software applications, and the like. The memory  66  and the storage  68  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  64  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  70  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  70  may enable the computing system  60  to communicate with the other devices in the marine survey system  22 , the land survey system  38 , or the like via the I/O ports  70 . 
     The display  72  may depict visualizations associated with software or executable code being processed by the processor  64 . In one embodiment, the display  72  may be a touch display capable of receiving inputs from a user of the computing system  60 . The display  72  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  26 , the location and property of hydrocarbon deposits within the subsurface region  26 , predictions of seismic properties associated with one or more wells in the subsurface region  26 , and the like. The display  72  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  72 , it should be noted that the computing system  60  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  60 , a cloud-computing system, or the like to distribute processes to be performed across multiple computing systems  60 . In this case, each computing system  60  operating as part of a super computer may not include each component listed as part of the computing system  60 . For example, each computing system  60  may not include the display  72  since multiple displays  72  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  60  may store the results of the analysis in one or more databases  74 . The databases  74  may be communicatively coupled to a network that may transmit and receive data to and from the computing system  60  via the communication component  62 . In addition, the databases  74  may store information regarding the subsurface region  26 , such as previous seismograms, geological sample data, seismic images, and the like regarding the subsurface region  26 . 
     Although the components described above have been discussed with regard to the computing system  60 , it should be noted that similar components may make up the computing system  60 . Moreover, the computing system  60  may also be part of the marine survey system  22  or the land survey system  38 , and thus may monitor and control certain operations of the seismic sources  32  or  40 , the receivers  36 ,  44 ,  46 , 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. 4 . 
     In some embodiments, the computing system  60  may generate a two-dimensional representation or a three-dimensional representation of the subsurface region  26  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  26  that can extend for some distance. In a two-dimensional (2-D) seismic survey, the receiver locations may be placed along a single line, whereas in a three-dimensional (3-D) survey the receiver locations may be distributed across the surface in a grid pattern. As such, a 2-D seismic survey may provide a cross sectional picture (vertical slice) of the Earth layers as they exist directly beneath the recording locations. A 3-D seismic survey, on the other hand, may create a data “cube” or volume that may correspond to a 3-D picture of the subsurface region  26 . 
     In addition, a 4-D (or time-lapse) seismic survey may include seismic data acquired during a 3-D survey at multiple times. Using the different seismic images acquired at different times, the computing system  60  may compare the two images to identify changes in the subsurface region  26 . 
     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  60  may be employed to analyze the acquired seismic data to obtain an image representative of the subsurface region  26  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  60  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  10  of  FIG. 1  that details various processes that may be undertaken based on the analysis of the acquired seismic data. 
     In some embodiments, the results of the seismic data analysis may be generated in conjunction with a seismic processing scheme that includes seismic data collection, editing of the seismic data, initial processing of the seismic data, signal processing, conditioning, and imaging (which may, for example, include production of imaged sections or volumes) in prior to any interpretation of the seismic data, any further image enhancement consistent with the exploration objectives desired, generation of attributes from the processed seismic data, reinterpretation of the seismic data as needed, and determination and/or generation of a drilling prospect or other seismic survey applications. As a result, location of hydrocarbons within a subsurface region  26  may be identified. Techniques for detecting subsurface features (such as, for example, faults) from the seismic data/images will be described in greater detail below. 
       FIG. 5  illustrates a flow chart of a method  78  that implements a method of one or more embodiments. The method  78  of one or more embodiments can be performed by the computing system  60  of  FIG. 4 , for example by the processor  64  operating in conjunction with at least one of the memory  66  or the storage  68 , for example, by executing code or instructions to carry out the steps of method  78 . 
     The method  78 , at step  80 , illustrates reception of image data by the computing system  60  that is to be recognized by at least one neural network. The image data can be representative of a fault within a subsurface volume. Specifically, the image data can be representative of one fault, multiple faults, and/or no faults. The image data can, for example, include three-dimensional synthetic data. The method  78 , at step  82 , includes generating an output via the at least one neural network based on the received image data. The method  78 , at step  84 , can include comparing the output of the at least one neural network with a desired output. The method  78  can also include modifying the neural network so that the output of the neural network corresponds to the desired output in step  86 .  FIG. 6  illustrates an example of an implementation of at least a portion of method  78 . 
       FIG. 6  illustrates the use of deep learning in conjunction with fault identification in a seismic image and may be performed, for example, by the computing system  60  of  FIG. 4 . More particularly, the processor  64  operating in conjunction with at least one of the memory  66  or the storage  68 , for example, by executing code or instructions to carry out the techniques described below described in conjunction with  FIG. 6 . 
     Image  88  represents a two dimensional (2D) slice of a three dimensional (3D) image cube that will be processed as a portion of a seismic image being processed to determine fault locations therein. Thus, image  88  is presented as a 2D slice merely for ease of illustration; however, it should be understood that a 3D image cube can replace image  88  and that this 3D image cube (as well as the image  88 , as illustrated) can each respectively correspond to the image data received in step  80  of method  78  in  FIG. 5 . 
     Image  88  includes center point  90 . Fault prediction can be treated as an image classification problem, whereby the neural networks  92  and  94  classify only a particular location (e.g., the center point  90 ) of an image/cube (e.g., image  88 ) as indicative of a fault or not. When predicting faults using this technique, (e.g., a center point classifier), a sliding window is moved across a whole of the seismic image to be processed, typically voxel by voxel (or pixel by pixel). As illustrated, the image  88  is processed via (a first) neural network  92  and (a second) neural network  94 . These neural networks  92  and  94  may be separate neural networks each assigned to predict one unique aspect of a potential fault in the image  88 , for example, simultaneously (e.g., at the same time, nearly at the same time, or in parallel) or one after another (e.g., sequentially or in series). Alternatively, the neural networks  92  and  94  may be portions of a single neural network, whereby the portions corresponding to neural networks  92  and  94  are each assigned to predict one aspect of a potential fault in the image  88 , for example, simultaneously (e.g., at the same time, nearly at the same time, or in parallel) or one after another (e.g., sequentially or in series). 
     It is envisioned that, for example, neural network  92  can predict and generate as an output the dip (e.g., the angle of a fault relative to a horizontal plane) of a fault located at or about the center point  90  as a portion of step  82  of  FIG. 5 . Additionally, for example, neural network  94  can predict and generate as an output the azimuth (e.g., the angle characterizing direction of the fault with respect to a reference direction) of a fault located at or about the center point  90  as a portion of step  82  of  FIG. 5 . Furthermore, additional attributes of the fault located at or about the center point  90  can be generated by additional neural networks and/or alternative attributes can be generated by the neural networks  92  and  94  as a portion of step  82  of  FIG. 5 . With one or more embodiments, the dip and azimuth attributes can be attributes which are necessary for defining a planar orientation of a predicted fault. 
     As further illustrated in  FIG. 6 , the computing system  60 , in conjunction with step  84  of  FIG. 5 , determines whether a fault is present or is not present at or about the center point  90  in step  96 . If the output of the neural network  92  indicates the presence of a fault, at least one attribute (e.g., dip, and/or azimuth) of that fault is transmitted in conjunction with an indication of a fault in image  88  or as indicative of the presence of a fault in image  88 . If the output of the neural network  92  does not indicate the presence of a fault (i.e., if the neural network  92  does not determine that a fault is present in image  88 ), a negative indication thereof (e.g., a zero, a no, or another negative indicator) is transmitted. As discussed above, with one or more embodiments, the output of the neural network  92  can indicate whether or not a fault is present at a center point of image  88 . 
     If the output of the neural network  94  indicates the presence of a fault, at least one attribute (e.g., azimuth) of that fault is transmitted in conjunction with an indication of a fault in image  88  or as indicative of the presence of a fault in image  88 . If the output of the neural network  94  does not indicate the presence of a fault (i.e., if the neural network  94  does not determine that a fault is present in image  88  or at the center of image  88 ), a negative indication thereof (e.g., a zero, a no, or another negative indicator) is transmitted. In step  96 , if either or both of the indications received as outputs from the neural networks  92  and  94  are negative indications, in step  96 , the computing system  60  determines that no fault is present in image  88  (at the center point), as a portion of step  84  of  FIG. 5 , and the computing system  60  generates an output  98  indicating (e.g., classifying) image  88  as having no fault (at the center point). 
     However, if the output from both of the neural network  92  and the neural network  94  indicate the presence of a fault (at the center point), the computing system  60  determines that a fault is present in image  88 , as a portion of step  84  of  FIG. 5 , and the computing system  60  generates an output  98  indicating (e.g., classifying) image  88  as having a fault (with the respective aspects, such as dip and azimuth, corresponding to the fault). Thus, a center point  90  of an image  88  is determined to be a fault (or have a fault therein) when both the neural network  92  and the neural network  94  vote yes (i.e., each indicate the presence of a fault). Thereafter, in some embodiments, a probability can be assigned to the fault, for example, the average of the predicted dip and azimuth probabilities from those two neural networks  92  and  94 . One or more embodiments can output a dip and an azimuth at the same time. 
     This process is repeated for additional images  88  (e.g., additional voxels of the seismic image being processed) until the seismic image of interest is processed to reveal the faults present therein. Through the use of more than one neural network (e.g., neural network  92  and neural network  94 ) each designed to determine an distinct aspect of a fault as indicative of the presence of a fault, increased reliability of fault detection is established. 
       FIG. 7  illustrates an example of the neural network  92 . The neural network  92  operates as a deep learning network. Deep learning methods implemented via a deep learning network can directly map the relationship between an image (e.g., image  88 ) and its corresponding label, for example, a fault or not. Different from the attribute methods, the feature maps in deep learning are derived by machines automatically through iterations, instead of engineered by humans. With the “self-learning” capability, deep learning can easily contain and handle millions of parameters, allowing it to learn very complex mapping relationships. Particularly, as one of the major deep learning methods, Convolutional Neural Networks (CNNs) are proven to be state-of-art for computer vision problems, including image classification, localization and segmentation. Accordingly, in present embodiments, one or more CNNs are utilized as the deep learning network of neural network  92 . 
     The neural network  92  is illustrated as utilizing an ensemble of multiple CNN models  100 ,  102 , and  104 . While a single CNN model  100  may be used, the use of more than one CNN model  100  and  102 , or CNN models  100 ,  102 , and  104 , or more than three CNN models may result in increased stability of the prediction  106  (e.g., output) generated by the neural network  92 . With one or more embodiments, as reflected by experimental results, the number of CNN models within neural network  92  can be three models, in order to increase accuracy, while also keeping the computational cost from being too high. This, in turn, may operate to enhance fault predictions. Due to the diversification/independent nature of each individual CNN model  100 ,  102 , and  104 , an ensemble of multiple CNN models  100 ,  102 , and  104  often outperforms a single CNN model, as the individual CNN models  100 ,  102 , and  104  can complement each other. However, it should be noted that an ensemble can also add significant extra computation time. Accordingly, selection of the number of CNN models in an ensemble (or the use of an ensemble at all), may be altered based on the desire for rapid results, the desire for accuracy in the prediction  106  that is generated, cost and/or complexity considerations, among other factors. 
     The prediction  106  generated by the neural network  92  may have a set number of output categories. For example, the neural network  92  (e.g., calculating dip) has  26  output categories (a non-fault bin and 25 dip bins, each centered at 15°, 18°, 21°, . . . , and 87°, with a dip bin size 3°). With one or more embodiments, as reflected by experimental results, a dip bin size of 3° provided results which were practically accurate, while not needing computational costs that were too high. Thus the prediction  106  from neural network  92  will have a result indicative of no fault being present or a dip value centered at one of the above noted angles. The bin size and, thus, the total number of output categories of the neural network  92  may be chosen based on desired granularity of the result chosen; however, this choice may invoke cost/complexity considerations and/or other factors. 
     Furthermore, it should be noted that the structure of the neural network  92  (having individual CNN models  100 ,  102 , and  104 ) may be repeated for neural network  94 . However, as will be discussed in detail below, the training of the CNN models  100 ,  102 , and  104  of neural network  92  differ from the training of CNN models of neural network  94 . Additionally, since the neural network  94  has a different fault attribute output (e.g., azimuth) with respect to prediction  106  (dip) of neural network  92 , the neural network  94  also will have different output categories with respect to neural network  92  discussed above. 
     For example, the neural network  94  (e.g., calculating azimuth) has  37  output categories (a non-fault bin plus 36 azimuth bins centered at 5°, 15°, 25°, . . . , and 355°, with a azimuth bin size 10°). Thus the prediction from neural network  94  will have a result indicative of no fault being present or an azimuth value centered at one of the above noted angles. The bin size and, thus, the total number of output categories of the neural network  94  may be chosen based on desired granularity of the result chosen; however, this choice may invoke cost/complexity considerations or other factors. 
     The outputs generated by the CNN models  100 ,  102 , and  104  can be averaged in step  108  to generate the prediction  106  of the neural network  92 . This averaging in step  108  may be a simple average of the outputs of CNN models  100 ,  102 , and  104  or one or more of the outputs of the CNN models  100 ,  102 , and  104  can be weighted (e.g., with respect to one another or with respect to one or more default weighting values). Similar averaging can be applied in neural network  94 . 
     As noted above, the training of the CNN models  100 ,  102 , and  104  of neural network  92  differ from the training of CNN models of neural network  94 . One or more embodiments can use different training data when training different CNN models. Additionally, the training of the CNN models  100 ,  102 , and  104  of neural network  92  differ from one another and the training of CNN models of neural network  94  differ from one another. For example,  FIG. 7  illustrates training data  110 , training data  112 , and training data  114 . Each of the training data  110 , training data  112 , and training data  114  differs from one another, which causes the CNN models  100 ,  102 , and  104  of neural network  92  to process the image  88  and generate results that differ from one another slightly. In deep learning, it is key to carefully design and collect training data. A deep learning algorithm for fault detection demands a significant amount of training data to represent as many of the geologic scenarios as possible. CNN tends to perform poorly in the situations it has not seen in its training data pool. For example, CNN will not be able to predict steep dip faults if the training data only contains gentle to medium dip faults. 
     In present embodiments, the training data  110 , training data  112 , and training data  114  is 3D synthetic training data; however, actual recorded data, for example, from previous expeditions could be used in place of or in conjunction with the synthetic data. Benefits from the use of synthetic data for training include no human labeling required, reduction/elimination of manually labeled fault dips and azimuths in 3D field data, unlimited possibilities for the number of training data and labels, ease in populating all possible fault dips and azimuths, known ground truth labels, avoidance of existing manual selections that often following fault truncations inaccurately (rendering them inadequate for training). The training data  110 ,  112 , and  114  is selected to allow its corresponding CNN model  100 ,  102 , and  104  generalize better to field data. For example, the training data  110 ,  112 , and  114  includes low angle faults (although infrequent), and therefore, expands fault dips in training data  110 ,  112 , and/or  114  to values included in the range of, for example, approximately 13.5° to 88.5°. Additional filtering can be applied thereafter. For example, in the case where there is only interest in medium to high dip faults, the low dip faults can be selected and removed after inference. Similarly, the fault azimuth is another parameter, which is left to span the full range of approximately 0° to 360° for synthetic training data for neural network  94 . 
     An important consideration in training data generation is the shape or slope of the horizons adjacent to faults. Although horizons are usually flat or gently dipping, it has been found to be useful to include horizons with all possible dips. Therefore, steep and almost vertical horizons are included in the training data  110 ,  112 , and  114 . This can operate to reduce the misclassification of a steep dipping horizon as a fault plane as well as mitigate false fault predictions in noisy seismic sections where steep noise and migration swings mislead the classifier. By improving the variability of instances in the training data  110 ,  112 , and  114 , the training data  110 ,  112 , and  114  expands to frequencies inclusive of both low and high extremes for the seismic reflectors (produced from hundreds of thousands of randomly populated reflectivity models), and at the same time, includes almost all possible fault dips/azimuths and horizon dips. 
     In some embodiments, six steps are used to create a 3D synthetic image cube: 1) making a horizontal reflectivity model; 2) folding; 3) shearing; 4) faulting; 5) convolving with a wavelet; and 6) adding noise. The 3D training data cube may be set to be 32×32×32 samples. The center point of an image cube is labeled as a fault only if a fault plane passes through the center within a distance boundary of one sample and the fault slip is greater than one sample. In some embodiments, approximately 10,000, 25,000, 50,000, 100,000 or more 3D image cubes can be generated for each training data  110 ,  112 , and  114 . Likewise, in some embodiments, approximately 2,500, 5,000, 7,500, 10,000, or more 3D image cubes can be used for validation of the neural networks  92  and  94 . Additionally, the synthetic training data chosen for each of the neural networks  92  and  94  can be balanced for the neural network  92  (e.g., the dip CNN models  100 ,  102 , and  104  having 26 categories of outputs) and the neural network  94  (e.g., the azimuth CNN models having 37 categories of outputs). 
       FIG. 8  illustrates a CNN architecture  116  that can be used for each of the CNN model models  100 ,  102 , and  104  (as well as for the CNN models of the neural network  94 ). Alternatively, it is envisioned that two or more different CNN architectures can be used in a given ensemble, for example, to take advantage of their diversified hypotheses. However, for discussion purposes, the same CNN architecture  116  of  FIG. 8  is used for all three CNN models  100 ,  102 , and  104  in the ensemble in neural network  92  (and the same CNN architecture  116  is used in the ensemble of neural network  94 ). However, as discussed above with respect to  FIG. 7 , each of the three models CNN models  100 ,  102 , and  104  are trained with non-overlapping training data (datasets)  110 ,  112 , and  114 , respectively, that are generated separately. 
     The CNN architecture  116  of  FIG. 8  includes twelve 3D convolutional (CONV) layers  118  using a uniform kernel size 3×3×3 for a given input data  119 . The number of CONV channels starts at 16 and then doubles after every max pooling  120  (e.g., down sampling). A rectifier linear unit (ReLU) activation function  122  (e.g., a transfer function) is applied after every 2 CONV layers  118 , and max pooling  120  is applied after every 4 CONV layers  118 . A fully-connected (FC) layer  124  with 256 neurons connects the CONV layers  118  and the output layer  126  where a 50% dropout is applied after the FC layer  124  for regularization. In the output layer  126 , a softmax classifier is used to output the probability associated with each category, where the max probability indicates the predicted category. 
     One or more embodiments of the present invention are directed to performing automated fault detection. One or more embodiments can perform automated fault detection within a three-dimensional subsurface volume. One or more embodiments can implement fault detection by using deep learning. 
     With one or more embodiments, the process of deep learning can be performed by training the system with synthetic training data. One or more embodiments can use training data in the form of 2-dimensional patches of data. One or more embodiments can use training data in the form of 3-dimensional cubes of data. With one example embodiment, a plurality of 32×32×32 cubes (e.g., 3D training cubes) can be used as training data. 
     In view of the above, one or more embodiments can provide a useful product that can guide interpreters and that can speed up the process of mapping faults. One or more embodiments can perform automated fault mapping at short notice (e.g., such as performing fault mapping for time-sensitive exploration projects). One or more embodiments can assist horizon and direct-hydrocarbon-indicator mapping. 
     The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure. 
     The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function]. . . ” or “step for [perform]ing [a function]. . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. 112(f).