Methods and systems for optimizing gas-lifting operations

Abstract

A method for optimal execution of a gas-lifting procedure. The method includes obtaining gas-lift data from a well site with gas-lift, the well site with gas-lift including an oil and gas well with access to a hydrocarbon reservoir and a gas-lift system including a gas pump. The method further includes obtaining a set of gas-lift parameters related to the well site with gas-lift and determining, with a machine learning (ML) model, a predicted gas-lift based on the gas-lift data and the set of gas-lift parameters, and determining, with an optimizer applied to the ML model, an optimal set of gas-lift parameters such that the predicted gas-lift is optimized. The method further includes adjusting, automatically, the set of gas-lift parameters to the optimal set of gas-lift parameters and injecting gas into the well using the gas-lift system according to the optimal set of gas-lift parameters.

What is claimed is: 
     
         1 . A method, comprising:
 obtaining gas-lift data from a well site with gas-lift, the well site with gas-lift comprising an oil and gas well with access to a hydrocarbon reservoir and a gas-lift system comprising a gas pump;   obtaining a set of gas-lift parameters related to the well site with gas-lift;   determining, with a machine learning (ML) model, a predicted gas-lift based on the gas-lift data and the set of gas-lift parameters;   determining, with an optimizer applied to the ML model, an optimal set of gas-lift parameters such that the predicted gas-lift is optimized;   adjusting, automatically, the set of gas-lift parameters to the optimal set of gas-lift parameters; and   injecting gas into the well using the gas-lift system according to the optimal set of gas-lift parameters.   
     
     
         2 . The method of  claim 1 , wherein the gas-lift data comprises well data describing a gas-to-oil ratio inside the well and a wellhead pressure. 
     
     
         3 . The method of  claim 1 , wherein the set of gas-lift parameters comprise gas injection parameters, the gas injection parameters comprising a rate of gas injection and a depth of gas injection. 
     
     
         4 . The method of  claim 3 , wherein the gas injection parameters comprise defining a fluid conduit medium. 
     
     
         5 . The method of  claim 1 , wherein the ML model is trained using empirical well data, the empirical well data comprising a plurality of histories of well behavior. 
     
     
         6 . The method of  claim 1 , wherein the ML model is trained using simulated well data. 
     
     
         7 . The method of  claim 1 , wherein the ML model uses a random forest algorithm. 
     
     
         8 . A system, comprising:
 a well site with gas-lift comprising an oil and gas well with access to a hydrocarbon reservoir and a gas-lift system comprising a gas pump;   a plurality of field devices disposed throughout the well site with gas-lift, the plurality of field devices gathering gas-lift data from the well site with gas-lift;   a control system configured to adjust one or more field devices in the plurality of field devices and the gas-lift system; and   a computer configured to:
 obtain the gas-lift data from the well and from the reservoir, 
 obtain a set of gas-lift parameters for the well site with gas-lift, 
 determine, with a machine learning (ML) model, a predicted gas-lift based on the gas-lift data and gas-lift parameters, 
 determine, with an optimizer applied to the ML model, an optimal set of gas-lift parameters such that the predicted gas-lift is optimized, 
 adjust, automatically, the set of gas-lift parameters to the optimal set of gas-lift parameters, and 
 transmit a signal to inject gas into the well using the gas-lift system according to the optimal set of gas-lift parameters. 
   
     
     
         9 . The system of  claim 8 , wherein the gas-lift data comprises well data describing a gas-to-oil ratio inside the well and a wellhead pressure. 
     
     
         10 . The system of  claim 8 , wherein the set of gas-lift parameters comprise gas injection parameters, the gas injection parameters comprising a rate of gas injection and a depth of nitrogen injection. 
     
     
         11 . The system of  claim 10 , wherein the gas injection parameters comprise defining a fluid conduit medium. 
     
     
         12 . The system of  claim 8 , wherein the ML model is trained using empirical well data, the empirical well data comprising a plurality of histories of well behavior. 
     
     
         13 . The system of  claim 8 , wherein the ML model is trained using simulated well data. 
     
     
         14 . 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 well site with gas-lift, the well site with gas-lift comprising an oil and gas well with access to a hydrocarbon reservoir and a gas-lift system comprising a gas pump,   obtaining a set of gas-lift parameters related to the well site with gas-lift,   determining, with a machine learning (ML) model, a predicted gas-lift based on the gas-lift data and the set of gas-lift parameters,   determining, with an optimizer applied to the ML model, an optimal set of gas-lift parameters such that the predicted gas-lift is optimized,   adjusting, automatically, the set of gas-lift parameters to the optimal set of gas-lift parameters, and   transmitting a signal to inject gas into the well using the gas-lift system according to the optimal set of gas-lift parameters.   
     
     
         15 . The non-transitory computer-readable memory of  claim 14 , wherein the gas-lift data comprises well data describing a gas-to-oil ratio inside the well and a wellhead pressure. 
     
     
         16 . The non-transitory computer-readable memory of  claim 14 , wherein the set of gas-lift parameters comprise gas injection parameters, the gas injection parameters comprising a rate of gas injection and a depth of gas injection. 
     
     
         17 . The non-transitory computer-readable memory of  claim 16 , wherein the gas injection parameters comprise defining a fluid conduit medium. 
     
     
         18 . The non-transitory computer-readable memory of  claim 14 , wherein the ML model is trained using empirical well data, the empirical well data comprising a plurality of histories of well behavior. 
     
     
         19 . The non-transitory computer-readable memory of  claim 14 , wherein the ML model is trained using simulated well data. 
     
     
         20 . The non-transitory computer-readable memory of  claim 14 , wherein the ML model uses a random forest algorithm.