Identifying an anomaly in a cement layer of a wellbore using dimensionality reduction

Abstract

An anomaly in a cement layer of a wellbore can be identified by applying dimensionality reduction to acoustic data of the wellbore. For example, a computing system can receive the acoustic data from a downhole tool deployed downhole in the wellbore using a tool string positioned within a casing string of the wellbore. The computing system can decrease a number of dimensions associated with the acoustic data to generate a dimension-reduced dataset including a predetermined dimensionality. Subsequently, the computing system can analyze the dimension-reduced dataset to determine a likelihood of the anomaly being present in the cement layer of the wellbore. The computing system can output, via a user interface, a cement map based on the dimension-reduced dataset. The cement map can indicate a presence of the anomaly in the cement layer of the wellbore for use in adjusting a wellbore operation.

What is claimed is: 
     
         1 . A system comprising:
 a downhole tool deployable downhole in a wellbore using a tool string positionable within a casing string of the wellbore;   a processing device; and   a memory device that includes instructions executable by the processing device for causing the processing device to perform operations comprising:
 receiving a plurality of acoustic data from the downhole tool, the plurality of acoustic data used to analyze a cement layer of the wellbore, the cement layer positioned to couple the casing string to the wellbore; 
 decreasing a number of dimensions associated with the plurality of acoustic data to generate a dimension-reduced dataset comprising a predetermined dimensionality; 
 subsequent to generating the dimension-reduced dataset, analyzing the dimension-reduced dataset to determine a likelihood of an anomaly being present in the cement layer of the wellbore; and 
 outputting, via a user interface, a cement map based on the dimension-reduced dataset, the cement map indicating a presence of the anomaly in the cement layer of the wellbore for use in adjusting a wellbore operation. 
   
     
     
         2 . The system of  claim 1 , wherein analyzing the dimension-reduced dataset further comprises:
 applying a proximity search to the dimension-reduced dataset to generate a proximity dataset comprising a plurality of azimuth pairs, a respective distance between each azimuth in an individual azimuth pair of the plurality of azimuth pairs corresponding to the likelihood of the anomaly being present in the cement layer of the wellbore.   
     
     
         3 . The system of  claim 2 , wherein the operations further comprise, subsequent to applying the proximity search to the dimension-reduced dataset:
 determining the respective distance between each azimuth of the individual azimuth pair of the plurality of azimuth pairs;   identifying that a pair distance between a first azimuth and a second azimuth of an azimuth pair is above a predefined threshold; and   flagging a region of the cement layer defined by the first azimuth and the second azimuth to indicate a material change in the region of the cement layer, wherein the material change corresponds to at least part of the anomaly in the wellbore.   
     
     
         4 . The system of  claim 2 , wherein applying the proximity search further comprises, for each azimuth pair of the plurality of azimuth pairs:
 identifying a first azimuth of the dimension-reduced dataset to create an azimuth pair;   determining a set of dissimilarity values including a respective dissimilarity value of each remaining azimuth in the dimension-reduced dataset, wherein each dissimilarity value is determined with respect to the first azimuth;   identifying a minimum dissimilarity value of the set of dissimilarity values, wherein the minimum dissimilarity value corresponds to a second azimuth of the dimension-reduced dataset; and   creating the azimuth pair by pairing the first azimuth and the second azimuth of the dimension-reduced dataset.   
     
     
         5 . The system of  claim 2 , wherein the operations further comprise, subsequent to applying the proximity search to the plurality of acoustic data:
 determining that each azimuth pair of the plurality of azimuth pairs is symmetric with respect to an axis of the wellbore, wherein the axis corresponds to an eccentricity direction to which the tool string is off-center in the casing string of the wellbore.   
     
     
         6 . The system of  claim 1 , wherein the operations further comprise, subsequent to outputting the cement map:
 outputting, via the user interface, a visual indicator on the cement map, wherein the visual indicator indicates a size of the anomaly and a location of the anomaly in the cement layer of the wellbore;   detecting, based on the visual indicator of the cement map, the anomaly in the cement layer, wherein the anomaly corresponds to deterioration of the cement layer;   in response to detecting the anomaly, determining, based on the anomaly in the cement layer, an adjustment to the wellbore operation associated with the wellbore; and   subsequent to determining the adjustment, automatically controlling the wellbore operation to perform the adjustment to address the anomaly in the cement layer.   
     
     
         7 . The system of  claim 6 , wherein the cement map comprises:
 a first axis associated with a depth of the wellbore;   a second axis associated with azimuthal directionality of the wellbore; and   a plurality of color indicators as the visual indicator, wherein the plurality of color indicators indicates the size of the anomaly and the location of the anomaly with respect to the first axis and the second axis of the cement map.   
     
     
         8 . The system of  claim 1 , wherein the predetermined dimensionality of the dimension-reduced dataset is determined by:
 determining a respective variance corresponding to each dimension of the number of dimensions associated with the plurality of acoustic data, wherein each variance indicates an amount of variation in the plurality of acoustic data that is attributed to a corresponding dimension; and   selecting, based on a variance threshold, a subset of the number of dimensions to determine the predetermined dimensionality of the dimension-reduced dataset, wherein a collective variance of the subset meets the variance threshold.   
     
     
         9 . A method comprising:
 receiving a plurality of acoustic data from a downhole tool deployed downhole in a wellbore using a tool string positioned within a casing string of the wellbore, the plurality of acoustic data associated with a cement layer of the wellbore coupling the casing string to the wellbore;   decreasing a number of dimensions associated with the plurality of acoustic data to generate a dimension-reduced dataset comprising a predetermined dimensionality;   subsequent to generating the dimension-reduced dataset, analyzing the dimension-reduced dataset to determine a likelihood of an anomaly being present in the cement layer of the wellbore; and   outputting, via a user interface, a cement map based on the dimension-reduced dataset, the cement map indicating a presence of the anomaly in the cement layer of the wellbore for use in adjusting a wellbore operation.   
     
     
         10 . The method of  claim 9 , wherein analyzing the dimension-reduced dataset further comprises:
 applying a proximity search to the dimension-reduced dataset to generate a proximity dataset comprising a plurality of azimuth pairs, a respective distance between each azimuth in an individual azimuth pair of the plurality of azimuth pairs corresponding to the likelihood of the anomaly being present in the cement layer of the wellbore.   
     
     
         11 . The method of  claim 10 , further comprising, subsequent to applying the proximity search to the dimension-reduced dataset:
 determining the respective distance between each azimuth of the individual azimuth pair of the plurality of azimuth pairs;   identifying that a distance between a first azimuth and a second azimuth of an azimuth pair is above a predefined threshold; and   flagging a region of the cement layer defined by the first azimuth and the second azimuth to indicate a material change in the region of the cement layer, wherein the material change corresponds to at least part of the anomaly in the wellbore.   
     
     
         12 . The method of  claim 10 , wherein applying the proximity search further comprises, for each azimuth pair of the plurality of azimuth pairs:
 identifying a first azimuth of the dimension-reduced dataset to create an azimuth pair;   determining a set of dissimilarity values including a respective dissimilarity value of each remaining azimuth in the dimension-reduced dataset, wherein each dissimilarity value is determined with respect to the first azimuth;   identifying a minimum dissimilarity value of the set of dissimilarity values, wherein the minimum dissimilarity value corresponds to a second azimuth of the dimension-reduced dataset; and   creating the azimuth pair by pairing the first azimuth and the second azimuth of the dimension-reduced dataset.   
     
     
         13 . The method of  claim 10 , further comprising, subsequent to applying the proximity search to the plurality of acoustic data:
 determining that each azimuth pair of the plurality of azimuth pairs is symmetric with respect to an axis of the wellbore, wherein the axis corresponds to an eccentricity direction to which the tool string is off-center in the casing string of the wellbore.   
     
     
         14 . The method of  claim 9 , further comprising, subsequent to outputting the cement map:
 outputting, via the user interface, a visual indicator on the cement map, wherein the visual indicator indicates a size of the anomaly and a location of the anomaly in the cement layer of the wellbore;   detecting, based on the visual indicator of the cement map, the anomaly in the cement layer, wherein the anomaly corresponds to deterioration of the cement layer;   in response to detecting the anomaly, determining, based on the anomaly in the cement layer, an adjustment to the wellbore operation associated with the wellbore; and   subsequent to determining the adjustment, automatically controlling the wellbore operation to perform the adjustment to address the anomaly in the cement layer.   
     
     
         15 . The method of  claim 9 , wherein the predetermined dimensionality of the dimension-reduced dataset is determined by:
 determining a respective variance corresponding to each dimension of the number of dimensions associated with the plurality of acoustic data, wherein each variance indicates an amount of variation in the plurality of acoustic data that is attributed to a corresponding dimension; and   selecting, based on a variance threshold, a subset of the number of dimensions to determine the predetermined dimensionality of the dimension-reduced dataset, wherein a collective variance of the subset meets the variance threshold.   
     
     
         16 . A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:
 receiving a plurality of acoustic data from a downhole tool deployed downhole in a wellbore using a tool string positioned within a casing string of the wellbore, the plurality of acoustic data associated with a cement layer of the wellbore coupling the casing string to the wellbore;   decreasing a number of dimensions associated with the plurality of acoustic data to generate a dimension-reduced dataset comprising a predetermined dimensionality;   subsequent to generating the dimension-reduced dataset, analyzing the dimension-reduced dataset to determine a likelihood of an anomaly being present in the cement layer of the wellbore; and   outputting, via a user interface, a cement map based on the dimension-reduced dataset, the cement map indicating a presence of the anomaly in the cement layer of the wellbore for use in adjusting a wellbore operation.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein analyzing the dimension-reduced dataset further comprises:
 applying a proximity search to the dimension-reduced dataset to generate a proximity dataset comprising a plurality of azimuth pairs, a respective distance between each azimuth in an individual azimuth pair of the plurality of azimuth pairs corresponding to the likelihood of the anomaly being present in the cement layer of the wellbore.   
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the operations further comprise, subsequent to applying the proximity search to the dimension-reduced dataset:
 determining the respective distance between each azimuth of the individual azimuth pair of the plurality of azimuth pairs;   identifying that a distance between a first azimuth and a second azimuth of an azimuth pair is above a predefined threshold; and   flagging a region of the cement layer defined by the first azimuth and the second azimuth to indicate a material change in the region of the cement layer, wherein the material change corresponds to at least part of the anomaly in the wellbore.   
     
     
         19 . The non-transitory computer-readable medium of  claim 16 , wherein the operations further comprise, subsequent to outputting the cement map:
 outputting, via the user interface, a visual indicator on the cement map, wherein the visual indicator indicates a size of the anomaly and a location of the anomaly in the cement layer of the wellbore;   detecting, based on the visual indicator of the cement map, the anomaly in the cement layer, wherein the anomaly corresponds to deterioration of the cement layer;   in response to detecting the anomaly, determining, based on the anomaly in the cement layer, an adjustment to the wellbore operation associated with the wellbore; and   subsequent to determining the adjustment, automatically controlling the wellbore operation to perform the adjustment to address the anomaly in the cement layer.   
     
     
         20 . The non-transitory computer-readable medium of  claim 16 , wherein the predetermined dimensionality of the dimension-reduced dataset is determined by:
 determining a respective variance corresponding to each dimension of the number of dimensions associated with the plurality of acoustic data, wherein each variance indicates an amount of variation in the plurality of acoustic data that is attributed to a corresponding dimension; and   selecting, based on a variance threshold, a subset of the number of dimensions to determine the predetermined dimensionality of the dimension-reduced dataset, wherein a collective variance of the subset meets the variance threshold.