| CPC E21B 47/04 (2013.01) [E21B 47/12 (2013.01); G06N 3/08 (2013.01); E21B 2200/22 (2020.05)] | 19 Claims |
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1. A method for training and using a neural network on a computer system to perform wellbore correlation across multiple wellbores using a machine-learning model implemented via the neural network, the method comprising:
training the neural network wherein the training includes
obtaining a first number of well logs;
generating a training reference signal based on the first number of well logs;
transforming the training reference signal to generate one or more transformed signals;
generating a training input tile and at least one control point mapping across each of the first number of well logs based on the training reference signal and the one or more transformed signals;
inputting, into the neural network, the training input tile and at least one control point for each of the first number of well logs;
inputting, into the neural network, the at least one control point mapping across each of the first number of well logs; and
training the neural network based on the training input tile and the at least one control point mapping across each of the first number of well logs;
predicting a depth alignment across the multiple wellbores based on at least one formation property of subsurface formations in which the multiple wellbores are located, wherein the predicting comprises,
logging the multiple wellbores to obtain wellbore data, wherein the wellbore data includes a second number of well logs of the at least one formation property for the corresponding multiple wellbores;
selecting a reference wellbore from among the multiple wellbores and a corresponding reference well log from the second number of well logs;
defining at least one control point in a reference signal of the reference well log for the reference wellbore, wherein the reference well log includes changes in the at least one formation property at a depth of the reference wellbore;
generating an input tile that comprises the reference signal, the at least one control point, and a number of non-reference well logs,
wherein the number of non-reference well logs, from second number of well logs, corresponds to a set of non-reference wellbores, and
wherein each of the number of non-reference well logs includes changes in the at least one formation property at a depth of each non-reference wellbore of the set of non-reference wellbores;
inputting the input tile into the neural network; and
in response to the inputting the input tile into the neural network,
outputting, from the neural network, a corresponding control point for each of the number of non-reference well logs that corresponds to the at least one control point of the reference well log; and
determining, based on the control point, that the formation property is substantially equivalent at the depth across the multiple well bores.
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