Concentration Prediction in Produced Water

Abstract

The invention notably relates to a computer-implemented method of machine-learning a predictive model configured for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production. The method comprises providing a dataset comprising values of one or more geoscience well-wise variables. Each value corresponds to a respective well of hydrocarbon production in the hydrocarbon reservoir other than the given well. Each value that corresponds to a respective well is associated to a respective ground truth value representing a concentration of the element in the respective well. The method further comprises learning the predictive model based on the dataset. This forms an improved solution for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir.

1 . A computer-implemented method of machine-learning a predictive model configured for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the method comprising:
 providing a dataset comprising values of one or more geoscience well-wise variables, each value corresponding to a respective well of hydrocarbon production in the hydrocarbon reservoir other than the given well, each value that corresponds to a respective well being associated to a respective ground truth value representing a concentration of the element in the respective well; and   learning the predictive model based on the dataset.   
     
     
         2 . The method of  claim 1 , wherein the one or more geoscience variables comprise:
 one or more geochemical variables,   one or more geological variables,   one or more well design variables, and/or   one or more production variables.   
     
     
         3 . The method of  claim 1 , wherein:
 the one or more geochemical variables include:
 a pH, 
 an amount of dissolved salt, 
 a water density, 
 a CO2 concentration, 
 a bicarbonate concentration, 
 a chloride concentration, 
 a sulphate concentration, 
 a sodium concentration, 
 a potassium concentration, 
 a magnesium concentration, 
 a calcium concentration, 
 a strontium concentration, 
 a barium concentration, 
 an iron concentration, 
 a hydrogen sulfide concentration, 
 a manganese concentration, and/or 
 a zinc concentration; 
   the one or more geological variables include:
 a water saturation, and/or 
 an identifier for fault presence; 
   the one or more well design variables include:
 one or more dimensional variables, 
 one or more positional variables, 
 an identifier of a connected pipeline, and/or 
 one or more elevation variables; and/or 
   the one or more production variables include:
 a date of first production, 
 a gas production value, 
 a volume of injected fracking water, 
 a volume of injected propane. 
   
     
     
         4 . The method of  claim 1 , wherein the dataset comprises missing values of one or more variables for a number of wells of hydrocarbon production in the hydrocarbon reservoir, and the method comprises determining a respective filling value for each missing value. 
     
     
         5 . The method of  claim 4 , wherein the one or more variables include the following variables which each have at least one missing value:
 a fault presence,   an identifier of a connected pipeline,   a volume of injected fracking water,   a volume of injected propane, and/or   one or more elevation variables.   
     
     
         6 . The method of  claim 4 , wherein the number of missing values of the dataset is lower than 30% of the total number of values in the dataset, for example lower than 20% of the total number of values in the dataset. 
     
     
         7 . The method of  claim 4 , wherein determining a given respective filling value for a given missing value of a given variable for the given well comprises inferring the given missing value from one or more values of the given variable in the dataset. 
     
     
         8 . The method of  claim 7 , wherein the inferring is performed from:
 historical values of the given variable for the given well, and/or   values of the given variable for neighbouring wells.   
     
     
         9 . The method of  claim 7 , wherein the inferring comprises:
 computing a mean; or   applying a machine-learnt inference model.   
     
     
         10 . The method of  claim 1 , wherein the method further comprises, prior to the learning, analysing the dataset to obtain a set of key variables from the one or more geoscience variables, the learning being then based on a restriction of the dataset to the key variables associated to the respective ground truth value. 
     
     
         11 . The method of  claim 1 , wherein the element is one of lithium, cobalt, nickel, or cadmium. 
     
     
         12 . A computer-implemented method for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the predicting method comprising:
 providing a predictive model learnt according to a computer-implemented method of machine-learning a predictive model configured for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the machine-learning method comprising:
 providing a dataset comprising values of one or more geoscience well-wise variables, each value corresponding to a respective well of hydrocarbon production in the hydrocarbon reservoir other than the given well, each value that corresponds to a respective well being associated to a respective ground truth value representing a concentration of the element in the respective well; and 
 learning the predictive model based on the dataset; and 
   predicting the concentration of the element in the given well by applying the predictive model to values of the one or more geoscience well-wise variables corresponding to the given well.   
     
     
         13 . A device including a non-transitory computer readable storage medium having recorded thereon a computer program comprising instructions for performing:
 a computer-implemented method of machine-learning a predictive model configured for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the machine-learning method comprising:
 providing a dataset comprising values of one or more geoscience well-wise variables, each value corresponding to a respective well of hydrocarbon production in the hydrocarbon reservoir other than the given well, each value that corresponds to a respective well being associated to a respective ground truth value representing a concentration of the element in the respective well; and 
 learning the predictive model based on the dataset; and/or 
   a computer-implemented method for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the predicting method comprising:
 providing a predictive model learnt according to a computer-implemented method of machine-learning a predictive model configured for predicting a concentration of an element in produced water of a given well of hydrocarbon production in a hydrocarbon reservoir having wells of hydrocarbon production, the machine-learning method comprising:
 providing a dataset comprising values of one or more geoscience well-wise variables, each value corresponding to a respective well of hydrocarbon production in the hydrocarbon reservoir other than the given well, each value that corresponds to a respective well being associated to a respective ground truth value representing a concentration of the element in the respective well; and 
 learning the predictive model based on the dataset; and 
 
 predicting the concentration of the element in the given well by applying the predictive model to values of the one or more geoscience well-wise variables corresponding to the given well. 
   
     
     
         14 . (canceled) 
     
     
         15 . The device of  claim 13 , wherein the device further comprises a processor coupled to a memory and a graphical user interface, the memory having recorded thereon the computer program. 
     
     
         16 . The device of  claim 13 , wherein the one or more geoscience variables comprise:
 one or more geochemical variables,   one or more geological variables,   one or more well design variables, and/or   one or more production variables.   
     
     
         17 . The device of  claim 13 , wherein:
 the one or more geochemical variables include:
 a pH, 
 an amount of dissolved salt, 
 a water density, 
 a CO2 concentration, 
 a bicarbonate concentration, 
 a chloride concentration, 
 a sulphate concentration, 
 a sodium concentration, 
 a potassium concentration, 
 a magnesium concentration, 
 a calcium concentration, 
 a strontium concentration, 
 a barium concentration, 
 an iron concentration, 
 a hydrogen sulfide concentration, 
 a manganese concentration, and/or 
 a zinc concentration; 
   the one or more geological variables include:
 a water saturation, and/or 
 an identifier for fault presence; 
   the one or more well design variables include:
 one or more dimensional variables, 
 one or more positional variables, 
 an identifier of a connected pipeline, and/or 
 one or more elevation variables; and/or 
   the one or more production variables include:
 a date of first production, 
 a gas production value, 
 a volume of injected fracking water, 
 a volume of injected propane. 
   
     
     
         18 . The device of  claim 13 , wherein the dataset comprises missing values of one or more variables for a number of wells of hydrocarbon production in the hydrocarbon reservoir, and the method comprises determining a respective filling value for each missing value. 
     
     
         19 . The device of  claim 17 , wherein the one or more variables include the following variables which each have at least one missing value:
 a fault presence,   an identifier of a connected pipeline,   a volume of injected fracking water,   a volume of injected propane, and/or   one or more elevation variables.