Patent Description:
<CIT> describes a method including receiving, via an network interface of a cloud-based infrastructure, a request for analysis of rock material properties based at least in part on a digital, image-based model of the rock material; responsive to the request, executing the analysis via provisioning of one or more resources of the cloud-based infrastructure to generate analysis results; and transmitting information based at least in part on the analysis results. <NPL>, describes a platform to facilitate interpretation and analysis of large volumes of seismic data in identifying faults by combining CNN and traditional machine learning models with a variety of seismic attributes.

The present invention resides in a computer-implemented method as defined in claim <NUM>, a computer system as defined in claim <NUM> and a computer program as defined in claim <NUM>.

The computing systems and methods disclosed herein are more effective methods for processing collected data that may, for example, correspond to a surface and a subsurface region. These computing systems and methods increase data processing effectiveness, efficiency, and accuracy. Such methods and computing systems may complement or replace conventional methods for processing collected data.

Systems and/or methods, described herein, train a machine learning system to identify geological features included in seismic data images. The systems and/or methods implement a search system in which one or more geological features are searched in the seismic data images as identified by the trained machine learning system. In this way, a "play," representing a group of geological features, is searched and identified in a group of seismic images. Examples of geological features that may be searched may include anticlines, toe-thrusts, horsts, terrain types, reservoirs, seals, source rocks, rock types, or the like. Data features that may be searched may include areas of noise in the data, (e.g. residual multiple, diffractions etc.), areas of poor imaging due to fault shadow, salt etc. and incorrect parameterization of seismic processing.

From play identification, geological areas having a group of geological features and attributes may be identified for further exploration. In other words, embodiments of the present disclosure provide a system to rapidly search and screen through large volumes of seismic data, finding features of interest, and/or finding geographical areas where multiple features coexist. Accordingly, exploration decisions may be focused on those geographical areas having the features of interest. Further, aspects of the present disclosure may be used to identify a group of features representing hazardous areas for which exploration may be avoided, thereby improving safety of workers and equipment.

In some embodiments, the systems and/or methods implements a ranking and/or filtering system to provide more relevant search results to a search query. In some embodiments, a "search result" may include to a "play" and includes a seismic data image having geological features of interest as defined by the search query. In this regard, any suitable ranking system is used to rank and/or filter multiple search results. A value representing the degree to which the seismic data image matches a search query (e.g., the degree to which the seismic data image includes the geological features of interest defined in the search query) is used to rank search results. Additionally, a user profile may be accessed to determine a user's search preferences, job roles, and/or other information that may indicate the relevancy of search results to a target user. In some embodiments, collaborative filtering may be employed to filter and rank search results based on the search activity and search history of other similar user's indicative of search results that may be relevant to a target user. Additionally, or alternatively, certain geological features may be weighted more heavily than others for the purposes of ranking and/or filtering search results.

Aspects of the present disclosure may transform a subjective process into a computer-based decision processes through the use of rules. For example, search parameters included in a search query may serve as rules for identifying areas with particular seismic features. Further, machine learning algorithms and rules are used to consistently and accurately identify features in seismic images and geological areas. In this way, feature identification is made consistent and objective through computer-based decisions rather than through subjective human interpretation. Aspects of the present disclosure accesses and searches a substantially large volume of seismic data images and datasets (e.g., thousands of images or more), which could not be practically performed without the use of the systems described herein. Further, aspects of the present disclosure accurately identify plays in a matter of moments, thereby reducing the level of human labor and time inputs in the identification of plays. In some embodiments, aspects of the present disclosure may allow explorers to identify hazardous areas to avoid, thus improving worker and/or equipment safety.

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.

One or more gathering facilities may be operatively connected to one or more of the wellsite's for selectively collecting downhole fluids from the wellsite(s).

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.

Embodiments of the present disclosure provide systems and methods for accessing information contained in large seismic data sets and delivering concise results that enhance the user experience. The user will also be able to leverage and access geoscience information to continue their analysis and trigger further workflows.

In some embodiments, the present disclosure provides a search engine to quickly screen through large amounts of seismic data. Using such a search engine, the user can quickly find geological features that are similar to a particular seismic feature, and can receive a visualization of the distribution of the features spatially.

Embodiments of the present disclosure may employ any number and combination of image recognition technologies to identify the geological features in many images of seismic data. With the help of deep-learning models, a database of geo-feature images can be built and displayed on a map or in a 3D visualization window.

However, some searches might provide an overwhelming number of results. Accordingly, embodiments of the present disclosure curate the results. The present disclosure includes ranking the results, by which the system predicts the user's most desired result at the top. This can be done by employing any suitable combination of ranking algorithms. Additionally, recommendations from either item-based or user-based collaborative filtering can be provided. Further, a variety of above-ground factors can be used, such as client behavior analysis, news insight retrieval, government regulation, fiscal terms, license rounds, or others. Geoscience factors can also be used, and may include depth below mudline, size/scale of objects, proximity to discoveries, or other geoscience factors.

Further, embodiments of the present disclosure provide limits on the results. The user adds limits/constraints to the attributes of the geological features including depth below mudline. Embodiments of the disclosure may also combine results. For example, the user can then employ spatial searches to the sets of geological features, and can analyze their combined spatial distribution and to find potential play types.

In some embodiments, the present disclosure can rapidly find geological features and/or play elements such as reservoirs, seals, and source rock in relatively large data sets. Further, quick assessments of the value of seismic data, an area with <NUM> anticlines might have more potential than an area with five anticlines as input to pricing analytics tools may be provided. Finding rock types and their prevalence in large seismic processing may also be provided, e.g., to prioritize noise removal approaches. Finding geohazards/drilling hazards within in a seismic volume may further be provided. Embodiments of the disclosure may be used in combining data volumes/maps to find geological features, e.g., combining seismic and gravity, seismic and magnetics, seismic and pore pressure, seismic and any attribute volumes/basin models.

Embodiments allows for leveraging different image recognition algorithms, and/or leveraging different ranking algorithms. Further, the present disclosure may facilitate finding features in shot gathers. Thus, embodiments of the present disclosure may enable rapid analysis of large volumes of data, and may be less sensitive to data quality than traditional automatic pickers, which often perform worse as data quality diminishes. This may accelerate and enhance accuracy in user's decisions on where to focus exploration efforts going forward.

<FIG> illustrates a diagram of a machine learning training process for identifying geological features in input seismic data images, labeling the input seismic data images with geological features, and storing the labeled input seismic data images. As shown in <FIG>, a geological feature search system <NUM> trains a neural network (or any other type of machine learning algorithm) having multiple layers using training images. Individual training images are labeled with the type of geological features present, such as anticlines, toe-thrusts, horsts, terrain types, reservoirs, seals, source rocks, rock types, or the like. The training images are used to train a multi-layer neural network implemented by the geological feature search system <NUM>, and to identify the geological features present in seismic images of real-life seismic data.

In some embodiments, the geological feature search system <NUM> receives an input or operational seismic image representing operational or real-life seismic data. The geological feature search system <NUM> identifies geological features in the seismic image using the multi-layer neural network (e.g., in which higher layers identify shape edges, and each subsequent layer identify more specific and complex structures and portions of image objects representing geological features). The seismic image with identified and labeled geological features is stored in a database, such as the geological features information storage <NUM> and may be represented graphically in the form shown, or in a different form. In some embodiments, the geological features information may identify the types of geological features present in the seismic image (e.g., as determined using the multi-layer neural network). Additionally, the geological features information include geological attributes associated with the features comprising a depth below mudline. Additionally, the geological features information may be used to identify non-geological attributes associated with the features, such as client behavior analysis information, news insights, government regulation information, fiscal terms of exploration, license requirements, etc. Such attributes may be received from an external source and may be linked with the geological attributes. In this way, the geological features information storage <NUM> includes a database that may store, for a given seismic data image, information identifying geological features, and that attributes of those geological features.

In some embodiments, multiple input seismic data images, representing real-life seismic data, are received and analyzed using the trained multi-layer neural network to identify the geological features and attributes included in each input seismic data image. In this way, the geological features information storage <NUM> stores multiple seismic data images that are tagged and/or labeled with information identifying geological features (and attributes of those geological features within the seismic data images).

In some embodiments, the geological feature search system <NUM> receives multiple input seismic data images, and for each seismic data image, the geological feature search system <NUM> identifies the geological features present in the seismic data images (e.g., using the trained, multi-level neural network), identify the attributes of the geological features, and store information (e.g., in the geological features information storage <NUM>) linking the seismic image data with geological features and attributes of the features. As described in greater detail herein, the geological feature search system <NUM> implements a search function to receive a search query. The search query identifies one or more features and also identifies one or more constraints (e.g., attributes associated with the features). The search query may correspond to a "play" having features of interests and/or features with attributes of interest. The geological feature search system <NUM> identifies search results, which may include seismic data images having the searched features and matching the constraints. As described herein, a "search result" includes a seismic data image and the degree to which the seismic data image matches the search query (e.g., the degree to which the seismic data image includes the geological features and the attributes/constraints defined in the search query).

In some embodiments, the geological feature search system <NUM> may be trained based on other input data, in addition to seismic data images. For example, the geological feature search system <NUM> may be trained based on other types of images and datasets, such as inversion and attribute volumes.

<FIG> shows an example flowchart of a process <NUM> for identifying geological areas having features of interest using a machine learning-based search system. The steps of <FIG> is implemented by the geological feature search system <NUM>. The flowchart of <FIG> illustrates the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure.

As shown in <FIG>, the process <NUM> includes receiving a geological feature search query (e.g., at block <NUM>). The geological feature search system <NUM> receiveS a search query containing one or more geological features. In some embodiments, the geological feature search system <NUM> may receive the search query from a target user via a user interface associated with an application, a webpage, or the like. The search query may include text entered by the target user, a selection of features in a seismic image, an image file of an image containing features, or other type of input identifying one or more geological features of interest. As described herein, examples of geological features that may be searched may include anticlines, toe-thrusts, horsts, terrain types, reservoirs, seals, source rocks, rock types, or the like. Additionally, a combination of seismic and volumes/basin models is searched, such as seismic and gravity, seismic and magnetics, seismic and pore pressure, and/or seismic and any other volumes/basin model. Additionally, or alternatively, features that may be searched may include areas of noise in the data, (e.g. residual multiple, diffractions etc.), areas of poor imaging due to fault shadow, salt etc. and incorrect parameterization of seismic processing. The search query further includes information identifying one or more constraints including geological attributes associated with the image comprising depth below mudline. Additionally, the constraints may include non-geological attributes, such as client behavior analysis information, news insights, government regulation information, fiscal terms of exploration, license requirements, etc..

The process <NUM> also includes executing a search based on the search query (e.g., at block <NUM>). The geological feature search system <NUM> executes a search by accessing the tagged and labeled seismic data images stored by the geological features information storage <NUM> and finding seismic data images having geological features and attributes that match the geological features and attributes from the search query. As previously discussed, the information stored by the geological features information storage <NUM> is generated through machine learning techniques. The information stored by the geological features information storage <NUM> includes seismic data images that are tagged and/or labeled with information identifying geological features (and attributes of those geological features) within the seismic data images.

The process <NUM> further includes determining search results (e.g., at block <NUM>). The geological feature search system <NUM> determines search results, which include seismic data images having geological features matching the constraints from the search query. In some embodiments, a "search result" may include a seismic data image which matches the search query to a threshold degree.

The process <NUM> also includes ranking the search results (e.g., at block <NUM>). For example, the geological feature search system <NUM> may rank the search results using any combination of suitable ranking and/or filtering techniques. The geological feature search system <NUM> ranks the search results based on value representing the degree to which the seismic data image matches a search query (e.g., the degree to which the seismic data image includes the geological features of interest defined in the search query). Additionally, the geological feature search system <NUM> may rank the search results based on a user profile may indicating the target user's search preferences, job roles, and/or other information that may indicate the relevancy of search results to the target user. In some embodiments, collaborative filtering may be employed to filter and rank search results based on the search activity and search history of other similar users indicative of search results that may be relevant to the target user. Additionally, or alternatively, certain geological features and/or attributes may be weighted more heavily than others for the purposes of ranking and/or filtering search results. In some embodiments, the geological feature search system <NUM> may determine a relevancy value or score based on one or more of the aforementioned ranking and/or filtering techniques, and may rank the search results based on the relevancy score.

The process <NUM> further includes outputting the search results (e.g., at block <NUM>). For example, the geological feature search system <NUM> may output the search results in any combination of forms. In some embodiments, the search results may be presented as a list in which each search result identifies the seismic data image and related information (e.g., the geographic location associated with the seismic data image, the geological features and attributes present in the seismic data image, a relevancy value or score, etc.). In some embodiments, the search results may be presented graphically, such as on a geographic map in which the spatial distribution of the searched geographical features of interest are plotted and presented graphically with various colors and/or patterns. Additionally, or alternatively, the search results may be presented in other ways.

From the search results, locations having specific groups of geographic features of interest (e.g., corresponding to "plays") may be quickly identified for further exploration. For example, certain plays may be more suitable for certain types of explorations. By using the machine-learning based search system, described herein, explorers may quickly and accurately identify areas to focus for exploration. In a similar regard, hazardous areas with a group of hazardous geographic features and/or drilling hazards may be identified and avoided.

In some embodiments, a computer-based instruction may be executed based on the search results and/or based on its content. For example, a computer-based instruction may be executed to generate and send a report that presents the search results. Additionally, or alternatively, a computer-based instruction may be executed to generate an alert based on the search results identifying a new area of exploration and/or a hazardous area. Additionally, or alternatively, a computer-based instruction may be executed to modify a workflow or modify an exploration planning system.

<FIG> illustrates a geological search interface for receiving search queries and presenting corresponding search results. As shown in <FIG>, the geological search interface <NUM> includes a search query field <NUM>, a search results list area <NUM>, a search results map area <NUM>, and related results area <NUM>. A user enters a search query in the search query field <NUM> (e.g., the search query "anticline). The geological feature search system <NUM> executes a search based on the search query to produce search results of seismic data images matching the search query (e.g., in a similar manner as described above with respect to the process <NUM> in <FIG>). In some embodiments, the search results may be presented as a list (e.g., in the search results list area <NUM>). The search results are listed in a ranked order along with a rating of relevancy (e.g., in the form of a relevancy percentage, or a value on a scale of <NUM>-<NUM> or other scale, etc.). Additionally the search results may be presented in a map view in the search results map area <NUM> in which the search results are mapped to corresponding geographic locations. As further shown in the example of <FIG>, the relevancy of each search result may be presented with a particular color, pattern, and/or shading. For example, different colors, patterns, shadings, etc. may represent varying levels of relevancy. In some embodiments, additional related seismic data images (e.g., related to the search results) may be presented in the related results area <NUM>. For example, the related search results may include seismic data images viewed by others who viewed a target or selected seismic data image within the search results. Using the geological search interface <NUM>, a user may quickly and accurately identify "plays" and areas having geological features of interest (or identify hazardous areas to avoid).

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 are stored in memory units and executed by processors. The memory unit is implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

In some embodiments, any of the methods of the present disclosure are executed by a computing system. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> include a computer or computer system 701A, which may be an individual computer system 701A or an arrangement of distributed computer systems. The computer system 701A 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 701A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 701B, 701C, and/or 701D (note that computer systems 701B, 701C and/or 701D may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and/or 701D that are located in one or more data centers, and/or located in varying countries on different continents).

The storage media <NUM> are 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 701A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 701A 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 geological feature searching module(s) <NUM>. In the example of computing system <NUM>, computer system 701A includes the geological feature searching module <NUM>. In some embodiments, a single geological feature searching module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of geological feature modules 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.

These modules, combinations of these modules, and/or their combination with general hardware are all included within the scope of protection of the invention as defined in the appended claims.

Claim 1:
A computer-implemented method comprising:
receiving (<NUM>) a geological feature search query identifying one or more geological features and specifying one or more constraints associated with one or more geological features, wherein the one or more constraints include geological attributes comprising a depth below a mudline;
executing (<NUM>), based on receiving the geological feature search query, a search of a database (<NUM>) storing a plurality of seismic data images, wherein executing the search comprises searching the seismic data images and images from a volume model or a basin model, and wherein the seismic data images are labeled with the one or more geological features present in each of the plurality of seismic data images as part of a machine learning process;
determining (<NUM>), based on executing the search, search results, wherein the search results identify one more of the plurality of seismic data images having the one or more geological features identified in the geological feature search query; and
ranking the search results based on a value representing the degree to which the seismic data image includes the geological features defined in the search query; and
outputting (<NUM>) the search results for use in oil and gas exploration.