US-12626103-B2 - Geologic learning framework

US12626103B2US 12626103 B2US12626103 B2US 12626103B2US-12626103-B2

Abstract

A method can include receiving data files, where the data files include different types of content; training an encoder using the data files to generate a trained encoder; compressing each of the data files using the trained encoder to generate a compressed representation of each of the data files; and processing the compressed representations of the data files to generate groups, where each of the groups represents one of the different types of content, where each of the groups includes members, and where each of the members is associated with a corresponding one of the data files.

Inventors

Jagrit SHEORAN
Preetika SHEDDE
Sunil Manikani

Assignees

SCHLUMBERGER TECHNOLOGY CORPORATION

Dates

Publication Date: 20260512
Application Date: 20220216
Priority Date: 20210218

Claims (13)

1 . A method comprising: receiving data files, wherein the data files comprise different types of content collected from a workspace framework tailored to a geologic environment and processed using a client layer, an application layer, a source or source of site information including offset well information, a storage layer, and an artificial intelligence layer, wherein the different types of content comprise text and images, wherein the images comprise at least one downhole log image, and/or wherein the images comprise at least one micrograph of a geologic sample; training an encoder using the data files to generate a trained encoder; compressing each of the data files using the trained encoder to generate a compressed representation of each of the data files; and processing the compressed representations of the data files to generate groups, wherein each of the groups represents one of the different types of content, wherein each of the groups comprises members, and wherein each of the members is associated with a corresponding one of the data files.
2 . The method of claim 1 , wherein the data files comprise digitized document files.
3 . The method of claim 1 , wherein at least a portion of the data files comprise corresponding metadata.
4 . The method of claim 1 , comprising rendering a visual representation of the groups to a graphical user interface on a display.
5 . The method of claim 4 , comprising receiving a search command that instructs a computing system to perform a search on the members of the groups.
6 . The method of claim 1 , comprising storing a data structure of the groups to a storage medium, wherein the data structure associates each of the members of the groups with a corresponding one of the data files.
7 . The method of claim 1 , wherein the processing comprises performing an orthogonal linear transformation of the compressed representations of the data files to generate a transformed representation of the compressed representations of the data files.
8 . The method of claim 7 , wherein the performing the orthogonal linear transformation comprises performing a principal component analysis (PCA).
9 . The method of claim 7 , wherein the processing further comprises performing a nonlinear dimensionality reduction process on the transformed representation of the compressed representations of the data files to generate a dimensionality reduced representation of the transformed representation.
10 . The method of claim 9 , wherein the performing the nonlinear dimensionality reduction process comprises performing a t-distributed stochastic neighbor embedding (t-SNE) process.
11 . The method of claim 9 , wherein the processing further comprises performing a clustering process on the dimensionality reduced representation to generate clusters, optionally wherein the performing a clustering process comprises performing a k-means clustering process, and optionally comprising automatically determining a value for a k parameter of the k-means clustering process.
12 . A system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive data files, wherein the data files comprise different types of content collected from a workspace framework tailored to a geologic environment and processed using a client layer, an application layer, a source or source of site information including offset well information, a storage layer, and an artificial intelligence layer, wherein the different types of content comprise text and images, wherein the images comprise at least one downhole log image, and/or wherein the images comprise at least one micrograph of a geologic sample; train an encoder using the data files to generate a trained encoder; compress each of the data files using the trained encoder to generate a compressed representation of each of the data files; and process the compressed representations of the data files to generate groups, wherein each of the groups represents one of the different types of content, wherein each of the groups comprises members, and wherein each of the members is associated with a corresponding one of the data files.
13 . A non-transitory computer readable medium storing computer-executable instructions to instruct a computing system: receive data files, wherein the data files comprise different types of content collected from a workspace framework tailored to a geologic environment and processed using a client layer, an application layer, a source or source of site information including offset well information, a storage layer, and an artificial intelligence layer, wherein the different types of content comprise text and images, wherein the images comprise at least one downhole log image, and/or wherein the images comprise at least one micrograph of a geologic sample; train an encoder using the data files to generate a trained encoder; compress each of the data files using the trained encoder to generate a compressed representation of each of the data files; and process the compressed representations of the data files to generate groups, wherein each of the groups represents one of the different types of content, wherein each of the groups comprises members, and wherein each of the members is associated with a corresponding one of the data files.

Description

CROSS REFERENCE PARAGRAPH This application is a National Stage Entry of International Application No. PCT/US2022/070674, filed Feb. 16, 2022, which claims the benefit of India Non-Provisional Application No. 202121006902, entitled “Geologic Learning Framework,” filed Feb. 18, 2021, the disclosure of which is hereby incorporated herein by reference. BACKGROUND A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.). In oil and gas exploration, geoscientists and engineers may acquire and analyze data to identify and locate various subsurface structures (e.g., horizons, faults, geobodies, etc.) in a geologic environment. Various types of structures (e.g., stratigraphic formations) may be indicative of hydrocarbon traps or flow channels, as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation can allow for construction of a more accurate model of a subsurface region, which, in turn, may improve characterization of the subsurface region for purposes of resource extraction. Characterization of one or more subsurface regions in a geologic environment can guide, for example, performance of one or more operations (e.g., field operations, etc.). As an example, a more accurate model of a subsurface region may make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc. SUMMARY A method can include receiving data files, where the data files include different types of content; training an encoder using the data files to generate a trained encoder; compressing each of the data files using the trained encoder to generate a compressed representation of each of the data files; and processing the compressed representations of the data files to generate groups, where each of the groups represents one of the different types of content, where each of the groups includes members, and where each of the members is associated with a corresponding one of the data files. A system can include one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive data files, where the data files include different types of content; train an encoder using the data files to generate a trained encoder; compress each of the data files using the trained encoder to generate a compressed representation of each of the data files; and process the compressed representations of the data files to generate groups, where each of the groups represents one of the different types of content, where each of the groups includes members, and where each of the members is associated with a corresponding one of the data files. One or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive data files, where the data files include different types of content; train an encoder using the data files to generate a trained encoder; compress each of the data files using the trained encoder to generate a compressed representation of each of the data files; and process the compressed representations of the data files to generate groups, where each of the groups represents one of the different types of content, where each of the groups includes members, and where each of the members is associated with a corresponding one of the data files. Various other apparatuses, systems, methods, etc., are also disclosed. This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings. FIG. 1 illustrates an example system that includes various framework components associated with one or more geologic environments; FIG. 2 illustrates examples of a basin, a convention and a system; FIG. 3 illustrates an example of a system; FIG. 4 illustrates an example of a system; FIG. 5 illustrates some examples of inputs; FIG. 6 illustrates some examples of output spaces; FIG. 7 il