Borehole data filtering techniques

Abstract

A method includes receiving, via a processing system, borehole data comprising a sinusoid-like pattern. The method also includes receiving, via the processing system, one or more reference features from a storage component. Further, the method includes filtering, via the processing system, the borehole data based on the sinusoid-like pattern and the one or more reference features to generate a first filtered borehole data and a first set of false detections. Even further, the method includes determining, via the processing system, one or more additional features corresponding to false detections based on the filtered borehole data and the first set of false detections. Even further, the method includes filtering, via the processing system, the first filtered borehole data based on the one or more additional features to generate a second filtered borehole data and a second set of false detections.

1 . A method, comprising:
 receiving, via a processing system, borehole data comprising a sinusoid-like pattern;   receiving, via the processing system, one or more reference features from a storage component;   filtering, via the processing system, the borehole data based on the sinusoid-like pattern and the one or more reference features to generate a first filtered borehole data and a first set of false detections;   determining, via the processing system, one or more additional features corresponding to false detections based on the filtered borehole data and the first set of false detections; and   filtering, via the processing system, the first filtered borehole data based on the one or more additional features to generate a second filtered borehole data and a second set of false detections.   
     
     
         2 . The method of  claim 1 , further training a sinusoid fracture detection model using the second filtered borehole data, the second set of false detections, or both. 
     
     
         3 . The method of  claim 1 , wherein the first set of false detections correspond to false positives. 
     
     
         4 . The method of  claim 1 , wherein the one or more reference features comprise intensity related features, image quality related features, noise and structure related features, or a combination thereof. 
     
     
         5 . The method of  claim 1 , comprising receiving, via the processing system, the borehole data as an output from a sinusoid detection model. 
     
     
         6 . The method of  claim 1 , wherein the one or more reference features correspond to one or more borehole data artifacts, one or more data quality features, or a combination thereof. 
     
     
         7 . The method of  claim 1 , wherein filtering the borehole data comprises:
 flattening the borehole data along a sinusoid pattern;   determining a subset of the flattened borehole data that includes a minimum intensity value at the sinusoid pattern; and   outputting the subset of the flattened borehole data as the first set of borehole data.   
     
     
         8 . The method of  claim 1 , further comprising training a sinusoid fracture detection model using the one or more additional features. 
     
     
         9 . The method of  claim 1 , wherein filtering the borehole data comprises:
 determining a minimum intensity value of the sinusoid-like pattern;   determining the minimum intensity value is below an intensity threshold; and   outputting a subset of the borehole data as the first set of borehole data based on the minimum intensity value being below the intensity threshold.   
     
     
         10 . A system, comprising
 a computing system comprises one or more processors; and   a memory storing instructions that, when executed by the computing system, are configured to cause the computing system to perform operations comprising:
 receiving borehole data comprising a sinusoid-like pattern; 
 receiving one or more reference features from a storage component; 
 filtering the borehole data based on the sinusoid-like pattern and the one or more reference features to generate a first filtered borehole data and a first set of false detections; 
 determining one or more additional features corresponding to false detections based on the filtered borehole data and the first set of false detections; and 
 filtering the first filtered borehole data based on the one or more additional features to generate a second filtered borehole data and a second set of false detections. 
   
     
     
         11 . The system of  claim 10 , wherein the instructions that, when executed by the computing system, are configured to cause the computing system to train a sinusoid fracture detection model using the second filtered borehole data, the second set of false detections, or both. 
     
     
         12 . The system of  claim 10 , wherein the instructions that, when executed by the computing system, are configured to cause the computing system to filter the borehole data by:
 determining a minimum intensity value of the sinusoid-like pattern;   determining the minimum intensity value is below an intensity threshold; and   outputting a subset of the borehole data as the first set of borehole data based on the minimum intensity value being below the intensity threshold.   
     
     
         13 . The system of  claim 10 , wherein the one or more additional features comprise missing value patterns, line artifact patterns, spiraling patterns, or a combination thereof. 
     
     
         14 . The system of  claim 10 , wherein the instructions that, when executed by the computing system, are configured to cause the computing system to filter the borehole data by:
 flattening the sinusoid-like pattern in the borehole data;   determining a plurality of midpoint values based on the flattened borehole data;   determining whether the plurality of midpoint values is below a pixel intensity threshold; and   outputting a subset of the borehole data as the first set of borehole data based on the midpoint values being below the pixel intensity threshold.   
     
     
         15 . The system of  claim 10 , wherein the instructions that, when executed by the computing system, are configured to cause the computing system to update the one or more reference features to include the one or more extracted features. 
     
     
         16 . One or more tangible non-transitory computer-readable memory media, comprising:
 processor-executable instructions that, when executed by one or more processors, cause the one or more processors to:
 receive borehole data including a sinusoid-like pattern; 
 receive one or more reference features from a storage component; 
 filter the borehole data based on the one or more reference features and the sinusoid-like pattern to generate a first filtered borehole data and a first set of false detections; 
 determine one or more additional features corresponding to false detections based on the filtered borehole data and the first set of false detections; and 
 filter the first filtered borehole data based on the one or more additional features to generate a second filtered borehole data and a second set of false detections. 
   
     
     
         17 . The one or more tangible non-transitory computer-readable memory media of  claim 16 , wherein the instructions that, when executed by the one or more processors, are configured to cause the one or more processors to train a sinusoid fracture detection model using the second filtered borehole data, the second set of false detections, or both. 
     
     
         18 . The one or more tangible non-transitory computer-readable memory media of  claim 16 , wherein the instructions that, when executed by the one or more processors, are configured to cause the one or more processors to filter the borehole data by:
 determining whether the sinusoid-like pattern includes a missing value artifact, wherein the one or more reference features comprise a missing value artifact pattern;   determining an artifact score for the borehole data include the missing value artifact; and   outputting a subset of the borehole data based on the artifact score of the subset of the borehole data.   
     
     
         19 . The one or more tangible non-transitory computer-readable memory media of  claim 16 , wherein the instructions that, when executed by the one or more processors, are configured to cause the one or more processors to filter the second filtered borehole data using a machine learning (ML) model. 
     
     
         20 . The one or more tangible non-transitory computer-readable memory media of  claim 16 , wherein the instructions that, when executed by the one or more processors, are configured to cause the one or more processors to filter the borehole data by determining whether sinusoid-like pattern is a sinusoid corresponding to a sinusoid fracture.