Artificial intelligence based framework for the optimization of lithium extraction in reservoir brine

Abstract

A method for joint and optimal production of hydrocarbons and lithium from a well, the method including obtaining field data for an oil and gas field with at least one well accessing at least one hydrocarbon reservoir. The method further includes obtaining a set of well operational parameters related to the oil and gas field and obtaining a set of lithium extraction configuration parameters related to the oil and gas field. The method further includes determining, with an artificial intelligence model, a predicted hydrocarbon production and a predicted lithium extraction from production fluids of the oil and gas field based on the field data, the set of well operational parameters, and the set of the lithium extraction configuration parameters and adjusting, automatically, the set of well operational parameters and the set of the lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction.

What is claimed is: 
     
         1 . A method, comprising:
 obtaining field data for an oil and gas field comprising at least one well with access to at least one hydrocarbon reservoir, the field data comprising:
 a number of wells in the oil and gas field, 
 well history data for each well in the oil and gas field, 
 spatial data for each well in the oil and gas field, and 
 water quality data; 
   obtaining a set of well operational parameters related to the oil and gas field;   obtaining a set of lithium extraction configuration parameters related to the oil and gas field;   determining, with an artificial intelligence model, a predicted hydrocarbon production and a predicted lithium extraction from production fluids of the oil and gas field based on the field data, the set of well operational parameters, and the set of the lithium extraction configuration parameters; and   adjusting, automatically, the set of well operational parameters and the set of the lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction.   
     
     
         2 . The method of  claim 1 , further comprising:
 obtaining an optimization weighting factor, wherein adjusting the set of well operational parameters and the set of lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction is based on, at least in part, the optimization weighting factor.   
     
     
         3 . The method of  claim 1 , wherein the artificial intelligence model is a long short-term memory network. 
     
     
         4 . The method of  claim 1 , wherein the set of lithium extraction configuration parameters includes a selected adsorption material. 
     
     
         5 . The method of  claim 1 , wherein the set of well operational parameters comprise a valve state of at least one well in the oil and gas field. 
     
     
         6 . The method of  claim 1 , wherein the water quality data comprises a concentration analysis of one or more metals in produced water from the oil and gas field, the one or more metals including lithium. 
     
     
         7 . The method of  claim 1 , further comprising validating the adjusted set of well operational parameters and adjusted set of lithium extraction configuration parameters by determining if hydrocarbon production and lithium extraction is improved upon adjusting these sets of parameters. 
     
     
         8 . 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 field data for an oil and gas field comprising at least one well with access to at least one hydrocarbon reservoir, the field data comprising:
 a number of wells in the oil and gas field, 
 well history data for each well in the oil and gas field, 
 spatial data for each well in the oil and gas field, and 
 water quality data; 
   obtaining a set of well operational parameters related to the oil and gas field;   obtaining a set of lithium extraction configuration parameters related to the oil and gas field;   determining, with an artificial intelligence model, a predicted hydrocarbon production and a predicted lithium extraction from production fluids of the oil and gas field based on the field data, the set of well operational parameters, and the set of the lithium extraction configuration parameters; and   adjusting, automatically, the set of well operational parameters and the set of the lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction.   
     
     
         9 . The non-transitory computer-readable memory of  claim 8 , wherein the instructions further comprise the step:
 obtaining an optimization weighting factor, wherein adjusting the set of well operational parameters and the set of lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction is based on, at least in part, the optimization weighting factor.   
     
     
         10 . The non-transitory computer-readable memory of  claim 8 , wherein the artificial intelligence model is a long short-term memory network. 
     
     
         11 . The non-transitory computer-readable memory of  claim 8 , wherein the set of lithium extraction configuration parameters includes a selected adsorption material. 
     
     
         12 . The non-transitory computer-readable memory of  claim 8 , wherein the set of well operational parameters comprise a valve state of at least one well in the oil and gas field. 
     
     
         13 . The non-transitory computer-readable memory of  claim 8 , wherein the water quality data comprises a concentration analysis of one or more metals in produced water from the oil and gas field, the one or more metals including lithium. 
     
     
         14 . The non-transitory computer-readable memory of  claim 8 , wherein the instructions further comprise the step:
 validating the adjusted set of well operational parameters and adjusted set of lithium extraction configuration parameters by determining if hydrocarbon production and lithium extraction is improved upon adjusting these sets of parameters.   
     
     
         15 . A system, comprising:
 an oil and gas field comprising at least one well and at least one lithium extraction system, wherein operation of the at least one well is defined by a set of well operational parameters and operation and configuration of the at least one lithium extraction system is defined by a set of lithium extraction configuration parameters;   a plurality of field devices disposed throughout the oil and gas field, the plurality of field devices collecting field data for the oil and gas field, the field data comprising:
 a number of wells in the oil and gas field, 
 well history data for each well in the oil and gas field, 
 spatial data for each well in the oil and gas field, and 
 water quality data; 
   a control system configured to adjust one or more of the field devices in the plurality of field devices; and   a computer configured to:
 obtain the field data for the oil and gas field, 
 obtain the set of well operational parameters, 
 obtain the set of lithium extraction configuration parameters, 
 determine, with an artificial intelligence model, a predicted hydrocarbon production and a predicted lithium extraction from production fluids of the oil and gas field based on the field data, the set of well operational parameters, and the set of the lithium extraction configuration parameters, and 
 adjust, automatically, the set of well operational parameters and the set of the lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction. 
   
     
     
         16 . The system of  claim 15 , wherein the computer is further configured to:
 obtain an optimization weighting factor, wherein adjusting the set of well operational parameters and the set of lithium extraction configuration parameters to jointly optimize the predicted hydrocarbon production and the predicted lithium extraction is based on, at least in part, the optimization weighting factor.   
     
     
         17 . The system of  claim 15 , wherein the artificial intelligence model is a long short-term memory network. 
     
     
         18 . The system of  claim 15 , wherein the set of lithium extraction configuration parameters includes a selected adsorption material. 
     
     
         19 . The system of  claim 15 , wherein the set of well operational parameters comprise a valve state of at least one well in the oil and gas field, and wherein the water quality data comprises a concentration analysis of one or more metals in produced water from the oil and gas field, the one or more metals including lithium. 
     
     
         20 . The system of  claim 15 , wherein the computer is further configured to:
 validate the adjusted set of well operational parameters and adjusted set of lithium extraction configuration parameters by determining if hydrocarbon production and lithium extraction is improved upon adjusting these sets of parameters, wherein adjustment is performed by the control system acting on one or more of the field devices in the plurality of field devices.