Determining boundaries for subsurface features through pixel-wise inferencing of inversion images

Abstract

A method of identifying subterranean features includes receiving an inversion image indicating a portion of a subsurface feature. Boundary information is determined for the inversion images using a subsurface boundary machine learning model that is generated to process individual pixels of input inversion images through a decision-based architecture to identify boundaries of subsurface features. Based on the boundary information, a boundary mask is generated for the inversion image. The method further includes providing the boundary mask for adjusting one or more downhole parameters based on the boundary mask.

What is claimed is: 
     
         1 . A method of identifying subterranean features, comprising:
 receiving an inversion image indicating a portion of a subsurface feature;   determining boundary information using a subsurface boundary machine learning model that is generated to process pixels of input inversion images through a decision-based architecture to identify boundaries of subsurface features;   based on the boundary information, generating a boundary mask for the inversion image; and   providing the boundary mask for adjusting one or more downhole parameters based on the boundary mask.   
     
     
         2 . The method of  claim 1 , wherein the subsurface boundary machine learning model is generated to process individual pixels of the input inversion images through the decision-based architecture to identify boundaries of subsurface features. 
     
     
         3 . The method of  claim 1 , wherein the boundary mask indicates at least one boundary of the subsurface feature of the inversion image, and further comprising updating the inversion image to indicate the at least one boundary of the subsurface features based on the boundary mask. 
     
     
         4 . The method of  claim 1 , wherein adjusting the one or more downhole parameters includes adjusting, automatically and without user input, one or more steering parameters for maintaining a downhole tool within identified boundaries of the subsurface feature indicated by the boundary mask. 
     
     
         5 . The method of  claim 1 , wherein the inversion image is a two-dimensional inversion result from downhole resistivity data. 
     
     
         6 . The method of  claim 1 , further comprising automatically determining the boundary information and generating the boundary mask based on real-time inversion results. 
     
     
         7 . The method of  claim 1 , wherein determining the boundary information includes determining an indication of boundary uncertainty for each pixel of the inversion image, and generating the boundary mask is based on a boundary uncertainty threshold for each pixel of the inversion image. 
     
     
         8 . The method of  claim 1 , further comprising determining a pixel data set for each pixel of the inversion image and providing the pixel data set for each pixel to the subsurface boundary machine learning model to process the pixel data set through the decision-based architecture and determine an indication of boundary uncertainty for each pixel of the inversion image. 
     
     
         9 . The method of  claim 8 , wherein determining the pixel data set for each pixel includes determining values that indicate, for each pixel, one or more of a measurement value of an associated downhole measurement, a downhole tool type, a measurement depth, pixel coordinates of the pixel within the inversion image, or latitude and longitude of the inversion image. 
     
     
         10 . The method of  claim 1 , wherein the inversion image indicates a top boundary and a bottom boundary of the subsurface feature, and determining the boundary information includes identifying each of the top boundary and the bottom boundary of the subsurface feature in the inversion image. 
     
     
         11 . The method of  claim 1 , wherein the inversion image indicates a portion of the subsurface feature and additionally indicates a portion of an additional subsurface feature, and determining the boundary information includes identifying a boundary of each of the subsurface feature and the additional subsurface feature in the inversion image. 
     
     
         12 . The method of  claim 1 , further comprising:
 receiving a plurality of additional inversion images indicating portions of the subsurface feature;   determining additional boundary information for each of the plurality of additional inversion images using the subsurface boundary machine learning model;   based on the additional boundary information, determining an additional boundary mask for each of the plurality of additional inversion images;   generating a three-dimensional subsurface object of the subsurface feature based on assembling the inversion image and the plurality of additional inversion images and based on the boundary mask and the plurality of additional boundary masks; and   providing the three-dimensional subsurface object for adjusting the one or more downhole parameters based on identified boundaries of the subsurface feature in the three-dimensional subsurface object.   
     
     
         13 . The method of  claim 12 , wherein generating the three-dimensional subsurface object includes interpolating boundary information between consecutive inversion images of the assembled inversion images. 
     
     
         14 . A system, comprising:
 at least one processor;   memory in electronic communication with the at least one processor; and   instructions stored in the memory, the instructions being executable by the at least one processor to:
 identify inversion training images indicating a portion of a subsurface feature; 
 obtain simulated inversion images that identify the subsurface feature from a subsurface model corresponding to a wellbore position of the inversion training images; 
 generate simulated boundary masks for the simulated inversion images based on detected boundaries of the subsurface feature in the simulated inversion images; and 
 generate a subsurface boundary machine learning model based on the inversion training images and the simulated boundary masks to process individual pixels of the inversion training images through a decision-based architecture and generate predicted boundary masks indicating boundaries of the subsurface feature within the inversion training images. 
   
     
     
         15 . The system of  claim 14 , wherein:
 the inversion training images are two-dimensional inversion results;   the subsurface model is a three-dimensional formation model spanning at least a portion of the wellbore corresponding to the inversion training images; and   generating the simulated inversion images includes creating two-dimensional transverse sections of the three-dimensional formation model corresponding to the inversion training images.   
     
     
         16 . The system of  claim 14 , wherein generating the subsurface boundary machine learning model includes providing feedback to the subsurface boundary machine learning model based on comparing the predicted boundary masks to the simulated boundary masks to further tune the decision-based architecture of the subsurface boundary machine learning model. 
     
     
         17 . The system of  claim 14 , wherein generating the subsurface boundary machine learning model is performed automatically based on receiving the inversion training images in real time. 
     
     
         18 . The system of  claim 14 , wherein the subsurface boundary machine learning model is generated based on a small sample of 50 inversion training images or less. 
     
     
         19 . A computer-readable storage medium including instruction that, when executed by at least one processor, cause the processor to:
 receive an inversion image indicating a portion of a subsurface feature;   determine boundary information using a subsurface boundary machine learning model that is generated to process individual pixels of input inversion images through a decision-based architecture to identify boundaries of subsurface features;   based on the boundary information, generating a boundary mask for the inversion image; and   update the inversion image to indicate a boundary of the subsurface feature based on the boundary mask.   
     
     
         20 . The computer-readable storage medium of  claim 19 , wherein the computer-readable storage medium and the processor are located in a downhole tool positioned downhole in a wellbore.