Methods and systems for gas-lift anomaly detection using integrated unsupervised learning models

Abstract

A method for determining an anomaly in a gas-lift system. The method includes obtaining gas-lift data from a gas-lift system and associated well, where the gas-lift system injects a gas into a fluid mixture of the well. The method further includes obtaining a set of operation parameters including an injected gas rate and an injected gas pressure. The method further includes determining, with a first machine learned model and a second machine learned model, a first and second anomaly metric each indicative of an anomaly in the gas-lift system or a flow of a production fluid from the well, respectively, based on the gas-lift data. The method further includes forming an aggregate anomaly prediction from the first anomaly metric and the second anomaly metric and adjusting, with a controller, the set of operation parameters based on, at least, the aggregate anomaly prediction.

What is claimed is: 
     
         1 . A method, comprising:
 obtaining gas-lift data from a gas-lift system and associated well, wherein the gas-lift system injects a gas into a fluid mixture of the well;   obtaining a set of operation parameters comprising an injected gas rate and an injected gas pressure;   determining, with a first machine learned model and a second machine learned model, a first and second anomaly metric each indicative of an anomaly in the gas-lift system or a flow of a production fluid from the well, respectively, based on the gas-lift data, wherein the production fluid comprises the fluid mixture and the injected gas;   forming an aggregate anomaly prediction from the first anomaly metric and the second anomaly metric; and   adjusting, with a controller, the set of operation parameters based on, at least, the aggregate anomaly prediction.   
     
     
         2 . The method of  claim 1 ,
 wherein the set of operation parameters further comprises:
 a set of well control parameters defining an operation of the well. 
   
     
     
         3 . The method of  claim 1 , wherein the set of operation parameters further comprises:
 a valve state of a valve that control the flow of the production fluid from the well.   
     
     
         4 . The method of  claim 1 , wherein the gas-lift data comprises:
 a temperature of the production fluid;   a pressure of the production fluid;   the injected gas rate; and   a hydrocarbon production rate.   
     
     
         5 . The method of  claim 1 :
 wherein the first machine learned model is an isolation forest,   wherein the second machine learned model is a one-class support vector machine.   
     
     
         6 . The method of  claim 1 , further comprising:
 determining, with an optimizer, a set of optimal operation parameters based on the aggregate anomaly prediction, wherein the set of optimal operation parameters maximize a production of hydrocarbons from the well.   
     
     
         7 . The method of  claim 5 , further comprising:
 determining a path descriptor for the gas-lift data based on a path traversed by the gas-lift data through the isolation forest;   comparing, with a similarity metric, the path descriptor to anomaly path descriptors of known and classified anomalies; and   determining a class of an anomaly for the gas-lift data based on the comparison of the path descriptor with the anomaly path descriptors.   
     
     
         8 . The method of  claim 1 , further comprising:
 detecting, based on the aggregate anomaly prediction, an anomaly in the gas-lift system; and   recommending a sequence of one or more actions to correct the anomaly.   
     
     
         9 . A system, comprising:
 a gas-lift system that injects a gas into a fluid mixture of a well; and   a controller that can configure one or more configurable parameters of the gas-lift system and well, the one or more configurable parameters comprised by a set of operation parameters, the controller configured to:
 obtain gas-lift data from the gas-lift system and the well; 
 determine, with a first machine learned model and a second machine learned model, a first and second anomaly metric each indicative of a presence of an anomaly in the gas-lift system or a flow of a production fluid from the well, respectively, based on the gas-lift data, wherein the production fluid comprises the fluid mixture and the injected gas; 
 form an aggregate anomaly prediction from the first anomaly metric and the second anomaly metric; and 
 adjust the set of operation parameters based on, at least, the aggregate anomaly prediction. 
   
     
     
         10 . The system of  claim 9 , further comprising:
 wherein the set of operation parameters comprises:
 a gas injection rate and a pressure of the injected gas. 
   
     
     
         11 . The system of  claim 9 , wherein the gas-lift data comprises:
 a temperature of the production fluid;   a pressure of the production fluid;   an injected gas rate; and   a hydrocarbon production rate.   
     
     
         12 . The system of  claim 9 :
 wherein the first machine learned model is an isolation forest,   wherein the second machine learned model is a one-class support vector machine.   
     
     
         13 . The system of  claim 9 , wherein the controller is further configured to:
 determine, with an optimizer, a set of optimal operation parameters based on the aggregate anomaly prediction, wherein the set of optimal operation parameters maximize a production of hydrocarbons from the well.   
     
     
         14 . The system of  claim 12 , the controller further configured to:
 determine a path descriptor for the gas-lift data based on a path traversed by the gas-lift data through the isolation forest;   compare, with a similarity metric, the path descriptor to anomaly path descriptors of known and classified anomalies;   determine a class of an anomaly for the gas-lift data based on the comparison of the path descriptor with the anomaly path descriptors; and   recommend a sequence of one or more actions to correct the anomaly based on, at least, the class of the anomaly.   
     
     
         15 . A non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform steps comprising:
 obtaining gas-lift data from a gas-lift system and associated well;   obtaining a set of operation parameters comprising an injected gas rate and an injected gas pressure;   determining, with a first machine learned model and a second machine learned model, a first and second anomaly prediction metric each indicative of an anomaly in the gas-lift system or flow of a production fluid from the well, respectively, based on the gas-lift data;   forming an aggregate anomaly prediction from the first predicted anomaly metric and the second anomaly metric; and   adjusting, with a controller, the set of operation parameters based on, at least, the aggregate anomaly prediction.   
     
     
         16 . The non-transitory computer-readable memory of  claim 15 ,
 wherein the set of operation parameters comprises a gas injection rate and a pressure of the injected gas.   
     
     
         17 . The non-transitory computer-readable memory of  claim 15 , wherein the gas-lift data comprises:
 a temperature of the production fluid;   a pressure of the production fluid;   an injected gas rate; and   a hydrocarbon production rate.   
     
     
         18 . The non-transitory computer-readable memory of  claim 15 :
 wherein the first machine learned model is an isolation forest,   wherein the second machine learned model is a one-class support vector machine.   
     
     
         19 . The non-transitory computer-readable memory of  claim 15 , the steps further comprising:
 determining, with an optimizer, a set of optimal operation parameters based on the aggregate anomaly prediction, wherein the set of optimal operation parameters maximize a production of hydrocarbons from the well.   
     
     
         20 . The non-transitory computer-readable memory of  claim 15 , the steps further comprising:
 determining a path descriptor for the gas-lift data based on a path traversed by the gas-lift data through the isolation forest;   comparing, with a similarity metric, the path descriptor to anomaly path descriptors of known and classified anomalies;   determining a class of an anomaly for the gas-lift data based on the comparison of the path descriptor with the anomaly path descriptors; and   recommending a sequence of one or more actions to correct the anomaly based on, at least, the class of the anomaly.