System and method for predicting downhole well integrity using machine learning

Abstract

A method may include obtaining static well data for a well. The method may further include obtaining dynamic well data for the well. The method may further include obtaining inspection data regarding the well. The method may further include obtaining maintenance data regarding the well. The method may further include determining predicted well integrity data for the well using a machine-learning model, the static well data, the dynamic well data, the inspection data, and the maintenance data. The machine-learning model may be trained using an ensemble learning algorithm. The method may further include determining a well operation for the well based on the predicted well integrity data. The method may further include transmitting, to a control system coupled to the well, a command that causes the well operation to be performed at the well.

What is claimed: 
     
         1 . A method, comprising:
 obtaining static well data for a first well,
 wherein the static well data describes one or more well design parameters of the first well; 
   obtaining dynamic well data for the first well,
 wherein the dynamic well data describes one or more well properties that change over a predetermined time period; 
   obtaining inspection data regarding the first well;   obtaining first maintenance data regarding the first well,
 wherein the first maintenance data corresponds to one or more maintenance operations that are performed at the first well; 
   determining, by a computer processor, first predicted well integrity data for the first well using a first machine-learning model, the static well data, the dynamic well data, the inspection data, and the first maintenance data, wherein the first machine-learning model is trained using an ensemble learning algorithm;   determining, by the computer processor, a well operation for the first well based on the first predicted well integrity data; and   transmitting, by the computer processor and to a control system coupled to the first well, a command that causes the well operation to be performed at the first well.   
     
     
         2 . The method of  claim 1 ,
 wherein the first machine-learning model comprises a plurality of models,   wherein a respective model among the plurality of models generates a respective prediction using an input dataset to produce a plurality of respective predictions,   wherein the first machine-learning model uses the plurality of respective predictions to determine a final prediction corresponding to the first predicted well integrity data, and   wherein the ensemble learning algorithm is based on a max voting technique, an averaging technique, a stacking technique, or a weighted averaging technique.   
     
     
         3 . The method of  claim 1 , further comprising:
 obtaining an initial model;   obtaining training data comprising second static well data, second dynamic well data, second inspection data, and second maintenance well data; and   performing, using a plurality of machine-learning epochs, a machine-learning algorithm, and the training data, a training operation on the initial model to produce a trained model,   wherein the trained model is configured to determine second predicted well integrity data for a second well.   
     
     
         4 . The method of  claim 1 ,
 wherein the first machine-learning model is a random forest model comprising a plurality of decision tree nodes, and   wherein the first machine-learning model is trained using a bootstrap and aggregation operation.   
     
     
         5 . The method of  claim 1 , further comprising:
 presenting, on a graphical user interface that is provided by a user device, a plurality of well operations based on the first predicted well integrity data; and   obtaining, in response to a user input within the graphical user interface, a user selection of the well operation among the plurality of well operations,   wherein the command is transmitted in response to the user selection.   
     
     
         6 . The method of  claim 1 , further comprising:
 obtaining, from a service provider server, second maintenance data regarding a plurality of maintenance operations performed at a plurality of wells.   
     
     
         7 . The method of  claim 1 , further comprising:
 obtaining, from a remote server, second inspection data regarding a plurality of inspection operations,   wherein at least one inspection operation among the plurality of inspection operations is a casing-casing annulus inspection, and   wherein the second inspection data is used by the first machine-learning model to determine the first predicted well integrity data.   
     
     
         8 . The method of  claim 1 ,
 wherein the inspection data comprises mechanical inspection data and electrical inspection data from respective entities of a plurality of inspection entities.   
     
     
         9 . The method of  claim 1 , further comprising:
 automatically obtaining maintenance data from a service provider server after the control system on a well intervention network uploads information regarding a completed maintenance operation.   
     
     
         10 . The method of  claim 1 ,
 wherein the well operation is selected from a group consisting of a slickline operation, a wireline operation, a well maintenance operation, a snubbing operation, a workover operation, a stimulation operation, and a coiled tubing operation.   
     
     
         11 . The method of  claim 1 , further comprising:
 detecting, based on the first predicted well integrity data, a packer failure of a first packer during a predetermined time period at the first well; and   performing a packer replacement operation before the predetermined time period and based on the first predicted well integrity data, and   wherein the packer replacement operation comprises replacing the first packer with a second packer.   
     
     
         12 . The method of  claim 1 , further comprising:
 determining, using the first machine-learning model, second predicted well integrity data for a second well, third predicted well integrity data for a third well, and fourth predicted well integrity data for a fourth well;   determining a priority ranking based on the second predicted well integrity data, the third predicted well integrity data, and the fourth predicted well integrity data; and   transmitting a plurality of commands to a plurality of control systems coupled to the second well, the third well, and the fourth well, wherein the plurality of commands implement a plurality of well intervention operations based on the priority ranking.   
     
     
         13 . A system, comprising:
 a well control system coupled to a first well at a well site, wherein the first well comprises a plurality of pipe components that are installed in a wellbore; and   a well integrity manager coupled to the well control system, the well integrity manager comprising a computer processor, wherein the well integrity manager is configured to perform a method comprising:
 obtaining static well data for the first well, wherein the static well data describes one or more well design parameters of the first well, 
 obtaining dynamic well data for the first well, wherein the dynamic well data describes one or more well properties that change over a predetermined time period, 
 obtaining inspection data regarding the first well, 
 obtaining first maintenance data regarding the first well, wherein the first maintenance data corresponds to one or more maintenance operations that are performed at the first well, 
 determining first predicted well integrity data for the first well using a first machine-learning model, the static well data, the dynamic well data, the inspection data, and the first maintenance data, wherein the first machine-learning model is trained using an ensemble learning algorithm, 
 determining a well operation for the first well based on the first predicted well integrity data, and 
 transmitting a command to the well control system that causes the well operation to be performed at the first well. 
   
     
     
         14 . The system of  claim 13 , wherein the method further comprises:
 obtaining an initial model;   obtaining training data comprising second static well data, second dynamic well data, second inspection data, and second maintenance well data; and   performing, using a plurality of machine-learning epochs, a machine-learning algorithm, and the training data, a training operation on the initial model to produce a trained model,   wherein the trained model is configured to determine second predicted well integrity data for a second well.   
     
     
         15 . The system of  claim 13 ,
 wherein the first machine-learning model is a random forest model comprising a plurality of decision tree nodes, and   wherein the first machine-learning model is trained using a bootstrap and aggregation operation.   
     
     
         16 . The system of  claim 13 , wherein the method further comprises:
 presenting, on a graphical user interface that is provided by a user device, a plurality of well operations based on the first predicted well integrity data; and   obtaining, in response to a user input within the graphical user interface, a user selection of the well operation among the plurality of well operations,   wherein the command is transmitted in response to the user selection.   
     
     
         17 . The system of  claim 13 , wherein the method further comprises:
 obtaining, from a service provider server, second maintenance data regarding a plurality of maintenance operations performed at a plurality of wells.   
     
     
         18 . The system of  claim 13 , wherein the method further comprises:
 obtaining, from a remote server, second inspection data regarding a plurality of inspection operations,   wherein at least one inspection operation among the plurality of inspection operations is a casing-casing annulus inspection, and   wherein the second inspection data is used by the first machine-learning model to determine the first predicted well integrity data.   
     
     
         19 . The system of  claim 13 , wherein the method further comprises:
 detecting, based on the first predicted well integrity data, a packer failure of a first packer during a predetermined time period at the first well; and   performing a packer replacement operation before the predetermined time period and based on the first predicted well integrity data, and   wherein the packer replacement operation comprises replacing the first packer with a second packer.   
     
     
         20 . The system of  claim 13 , wherein the method further comprises:
 determining, using the first machine-learning model, second predicted well integrity data for a second well, third predicted well integrity data for a third well, and fourth predicted well integrity data for a fourth well;   determining a priority ranking based on the second predicted well integrity data, the third predicted well integrity data, and the fourth predicted well integrity data; and   transmitting commands to a plurality of control systems coupled to the second well, the third well, and the fourth well, wherein the plurality of commands implement a plurality of well intervention operations based on the priority ranking.