TECHNICAL FIELD

The present disclosure relates generally to extracting natural resources from subterranean formations. More specifically, but not by way of limitation, this disclosure relates to machine-learning techniques for automatically identifying tops of geological layers to assist with extracting natural resources (e.g., oil, water, and gas) from subterranean formations.

BACKGROUND

A subterranean formation can include a succession of geological layers that are stacked on top of on another along a depth scale. Each geological layer can contain a relatively homogeneous set of geological properties (e.g., lithology, chemical composition, and physical properties) throughout its expanse. The depth location at which two adjacently stacked geological layers interface can be referred to as a “geological top.” Natural resources can be located in reservoirs embedded within or between the geological layers.

Well systems can be used to extract natural resources from a subterranean formation. A well system can include one or more wellbores drilled through the subterranean formation. To help detect the presence of a target natural resource, well logging tools can be lowered into each wellbore to analyze or otherwise study the geological properties of the formation region surrounding the wellbore. The well logging tools may include nuclear, gamma ray, electromagnetic, sonic, magnetic, or other measurement equipment for detecting the geological properties of the formation region. The measurement equipment can generate well log records that can include measurements collected over time as a function of depth. The well log records from one or more wellbores can be provided to a geologist, who can manually review the well log records to identify geological layers (e.g., strata) or other features of interest in the subterranean formation.

SUMMARY

One example of the present disclosure is a system that includes one or more processors and one or more memory devices. The one or more memory devices can include instructions that are executable by the one or more processors for causing the one or more processors to perform operations. The operations can include receiving a plurality of well log records associated with a plurality of wellbores drilled through geological layers. The operations can include generating a plurality of well clusters by applying a clustering process. The clustering process can involve determining statistical distances between pairs of well log records in the plurality of well log records, the statistical distances including a respective statistical distance between each pair of well log records in the plurality of well log records, wherein the respective statistical distance between a respective pair of well log records is determined based on a respective pair of matrices generated from the respective pair of well log records. The clustering process can involve determining geographical distances between pairs of wellbores in the plurality of wellbores, the geographical distances including a respective geographical distance between each pair of wellbores in the plurality of wellbores. The clustering process can involve generating a similarity matrix based on the statistical distances and the geographical distances, the similarity matrix including similarity values indicating a respective similarity between each pair of wellbores in the plurality of wellbores. The clustering process can involve determining the plurality of well clusters using the similarity matrix. The operations can include, for each well cluster in the plurality of well clusters: obtaining a respective set of training data associated with the well cluster, wherein the respective set of training data includes a set of well log records labeled to indicate geological tops of the geological layers and to indicate types of the geological layers; training a machine-learning model based on the respective set of training data to generate a trained machine-learning model; selecting a target well-log record associated with a target wellbore of the well cluster, wherein the respective set of training data excludes the target well-log record; providing the target well-log record as input to the trained machine-learning model, the trained machine-learning model being configured to generate an output usable to identify the geological tops of the geological layers in a region surrounding the target wellbore; determining, based on the output from the trained machine-learning model, the geological tops of the geological layers in the region surrounding the target wellbore; and transmitting an electronic signal indicating the geological tops of the geological layers in the region surrounding the target wellbore.

Another example of the present disclosure is a non-transitory computer-readable medium comprising program code that is executable by one or more processors for causing the one or more processors to perform operations. The operations can include receiving a plurality of well log records associated with a plurality of wellbores drilled through geological layers. The operations can include generating a plurality of well clusters by applying a clustering process. The clustering process can involve determining statistical distances between pairs of well log records in the plurality of well log records, the statistical distances including a respective statistical distance between each pair of well log records in the plurality of well log records, wherein the respective statistical distance between a respective pair of well log records is determined based on a respective pair of matrices generated from the respective pair of well log records. The clustering process can involve determining geographical distances between pairs of wellbores in the plurality of wellbores, the geographical distances including a respective geographical distance between each pair of wellbores in the plurality of wellbores. The clustering process can involve generating a similarity matrix based on the statistical distances and the geographical distances, the similarity matrix including similarity values indicating a respective similarity between each pair of wellbores in the plurality of wellbores. The clustering process can involve determining the plurality of well clusters using the similarity matrix. The operations can include, for each well cluster in the plurality of well clusters: obtaining a respective set of training data associated with the well cluster, wherein the respective set of training data includes a set of well log records labeled to indicate geological tops of the geological layers and to indicate types of the geological layers; training a machine-learning model based on the respective set of training data to generate a trained machine-learning model; selecting a target well-log record associated with a target wellbore of the well cluster, wherein the respective set of training data excludes the target well-log record; providing the target well-log record as input to the trained machine-learning model, the trained machine-learning model being configured to generate an output usable to identify the geological tops of the geological layers in a region surrounding the target wellbore; determining, based on the output from the trained machine-learning model, the geological tops of the geological layers in the region surrounding the target wellbore; and transmitting an electronic signal indicating the geological tops of the geological layers in the region surrounding the target wellbore.

Yet another example of the present disclosure is a method including computer-implemented operations. The operations can include receiving a plurality of well log records associated with a plurality of wellbores drilled through geological layers. The operations can include generating a plurality of well clusters by applying a clustering process. The clustering process can involve determining statistical distances between pairs of well log records in the plurality of well log records, the statistical distances including a respective statistical distance between each pair of well log records in the plurality of well log records, wherein the respective statistical distance between a respective pair of well log records is determined based on a respective pair of matrices generated from the respective pair of well log records. The clustering process can involve determining geographical distances between pairs of wellbores in the plurality of wellbores, the geographical distances including a respective geographical distance between each pair of wellbores in the plurality of wellbores. The clustering process can involve generating a similarity matrix based on the statistical distances and the geographical distances, the similarity matrix including similarity values indicating a respective similarity between each pair of wellbores in the plurality of wellbores. The clustering process can involve determining the plurality of well clusters using the similarity matrix. The operations can include, for each well cluster in the plurality of well clusters: obtaining a respective set of training data associated with the well cluster, wherein the respective set of training data includes a set of well log records labeled to indicate geological tops of the geological layers and to indicate types of the geological layers; training a machine-learning model based on the respective set of training data to generate a trained machine-learning model; selecting a target well-log record associated with a target wellbore of the well cluster, wherein the respective set of training data excludes the target well-log record; providing the target well-log record as input to the trained machine-learning model, the trained machine-learning model being configured to generate an output usable to identify the geological tops of the geological layers in a region surrounding the target wellbore; determining, based on the output from the trained machine-learning model, the geological tops of the geological layers in the region surrounding the target wellbore; and transmitting an electronic signal indicating the geological tops of the geological layers in the region surrounding the target wellbore, Some or all of the operations can be performed by one or more processors.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will become more apparent upon referring to the following specification, claims, and accompanying drawings.

