Time-lapse monitoring of well casings

Abstract

Disclosed are systems, apparatuses, methods, and computer readable medium for estimating metal loss of a casing wall including: acquiring at least two electromagnetic measurement data sets that are taken at two or more different times; aligning one or more depths across the at least two electromagnetic measurement data sets; computing a thickness of a plurality of downhole pipes, which at least partially overlap, based upon an applied inversion algorithm to each of the at least two electromagnetic measurement data sets; determine a location of one or more metal loss locations based upon a change in thickness for a given one of the plurality of downhole pipes; estimating one or more parameters of metal loss based upon the applied inversion algorithm.

What is claimed is: 
     
         1 . A method for estimating metal loss of a casing wall, the method comprising:
 acquiring at least two electromagnetic measurement data sets that are taken at two or more different times;   aligning one or more depths across the at least two electromagnetic measurement data sets;   computing a thickness of a plurality of downhole pipes, which at least partially overlap, based upon an applied inversion algorithm to each of the at least two electromagnetic measurement data sets;   determining a location of one or more metal loss locations based upon a change in thickness for a given one of the plurality of downhole pipes; and   estimating one or more parameters of metal loss based upon the applied inversion algorithm.   
     
     
         2 . The method of  claim 1 , wherein the two or more different times can span at least a month. 
     
     
         3 . The method of  claim 2 , wherein the two or more different times can span at least a year. 
     
     
         4 . The method of  claim 1 , wherein the at least two electromagnetic measurement data sets include one or more of: an electromagnetic induction measurement data set acquired in a time domain or a frequency domain, and/or a magnetic flux leakage measurement data set. 
     
     
         5 . The method of  claim 1 , wherein aligning one or more depths across the at least two electromagnetic measurement data sets include aligning a plurality of features across the at least two electromagnetic data sets. 
     
     
         6 . The method of  claim 5 , wherein the depth aligning includes performing a comparison of the at least two electromagnetic measurement data sets using a machine learning model to estimate a shift between the at least two electromagnetic measurement data sets. 
     
     
         7 . The method of  claim 5 , wherein the depth aligning comprises one or more of: a window-based correlation, an edge-based matching, and/or a dynamic time warping. 
     
     
         8 . The method of  claim 5 , wherein the aligning comprises analyzing the at least two electromagnetic measurement data sets for patterns and aligning a positioning of one or more distinct points of the pattern within the at least two electromagnetic measurement data sets. 
     
     
         9 . The method of  claim 1 , wherein the plurality of downhole pipes includes a nested casing arrangement in which multiple pipes of the plurality of downhole pipes are arranged in a well bore. 
     
     
         10 . The method of  claim 9 , further comprising generating a pseudo-thickness of each pipe of the multiple pipes using at least one algorithm. 
     
     
         11 . The method of  claim 10 , further comprising determining a change in pseudo-thickness of one or more of the multiple pipes. 
     
     
         12 . The method of  claim 11 , wherein the location of the one or more metal loss locations is compared with the change in pseudo-thickness of the one or more of the multiple pipes. 
     
     
         13 . The method of  claim 1 , further comprising setting an upper bound of the thickness of the plurality of downhole pipes based on a calculated thickness from one or more former electromagnetic measurement data sets. 
     
     
         14 . The method of  claim 1 , wherein the applied inversion algorithm is a model-based inversion algorithm. 
     
     
         15 . The method of  claim 14 , wherein the model-based inversion algorithm calculates at least one unknown material property at a given depth. 
     
     
         16 . The method of  claim 15 , wherein the computing the thickness of the plurality of downhole pipes further comprise a machine learning model. 
     
     
         17 . A metal loss calculation system comprising:
 a tool having a plurality of receivers and at least one transmitter;   a calculation unit including at least one processor and at least one storage device that stores instructions to cause the processor to:
 acquire at least two electromagnetic measurement data sets that are taken at two or more different times; 
 align one or more depths across the at least two electromagnetic measurement data sets; 
 compute a thickness of a plurality of downhole pipes, which at least partially overlap, based upon an applied inversion algorithm to each of the at least two electromagnetic measurement data sets; 
 determine a location of one or more metal loss locations based upon a change in thickness for a given one of the plurality of downhole pipes; and 
 estimate one or more parameters of metal loss based upon the applied inversion algorithm. 
   
     
     
         18 . The system of  claim 17 , wherein aligning one or more depths across the at least two electromagnetic measurement data sets include aligning a plurality of features across the at least two electromagnetic data sets. 
     
     
         19 . The system of  claim 18 , wherein the aligning comprises one or more of: a window-based correlation, an edge-based matching, and/or a dynamic time warping. 
     
     
         20 . The system of  claim 17 , wherein the at least one storage device further stores instructions to cause the processor to: set an upper bound of the thickness of the plurality of downhole pipes based on a calculated thickness from one or more former electromagnetic measurement data sets.