System and method for hydrocarbon production allocation

Abstract

Systems and methods are disclosed relating to hydrocarbon production allocation. In an example, an allocation system can receive well performance data for one or more wells and constraints data. The allocation system includes a trained machine learning (ML) model. The trained ML model can be used to predict a production volume for the one or more wells based on the well performance data and the constraints data. A production volume of the one or more wells can be adjusted based on the predicted production volume.

The invention claimed is: 
     
         1 . A method comprising:
 receiving, at an allocation system, well performance data for one or more wells and constraints data;   predicting, using a machine learning (ML) model of the allocation system, a production volume for the one or more wells based on the well performance data and the constraints data; and   adjusting a production volume of the one or more wells based on the predicted production volume.   
     
     
         2 . The method of  claim 1 , further comprising repeating the receiving, predicting, and adjusting steps to optimize the production volume of the one or more wells over time. 
     
     
         3 . The method of  claim 2 , further comprising
 simulating, using a simulator, a reservoir model to evaluate an outcome of the predicted production volume for the one or more wells; and   updating, using the allocation system, the predicted production volume for the one or more wells based on the simulation of the reservoir model, the repeating comprising the simulating and updating steps.   
     
     
         4 . The method of  claim 1 , wherein the predicting comprises:
 generating a number of production volumes for the one or more wells; and   selecting one of the production volumes based on user input or selection criteria for adjusting the production volume.   
     
     
         5 . The method of  claim 1 , wherein the well performance data comprises production data, reservoir data, and equipment performance data. 
     
     
         6 . The method of  claim 1 , wherein the predicted production volume is a first predicted production volume, the method further comprising:
 predicting using a linear programming model a second predicted production volume for the one or more wells based on the well performance data and the constraints data;   predicting using an evolutionary algorithm a third predicted production volume for the one or more wells based on the well performance data and the constraints data; and   selecting one of the first, second, and third predicted production volumes for adjusting the production volume of the one or more wells.   
     
     
         7 . The method of  claim 1 , further comprising:
 receiving input data for training the ML model, the input data comprising historical production data, historical reservoir data, and historical equipment performance data; and   preprocessing the input data to provide processed input data for training the ML model.   
     
     
         8 . The method of  claim 7 , wherein the ML model is a first ML model, and the preprocessing comprises predicting using a data prediction model missing data points in the historical production data, the data prediction model corresponding to a second ML model. 
     
     
         9 . The method of  claim 8 , wherein the second ML model is a Long Short-Term Memory Network (LSTM). 
     
     
         10 . The method of  claim 7 , wherein the preprocessing comprises identifying and removing using an outlier detector anomalies and/or inconsistent data points in the historical production data. 
     
     
         11 . The method of  claim 10 , wherein the outlier detector comprises a Z-score method or an inter-quartile (IQR) method. 
     
     
         12 . The method of  claim 7 , wherein the preprocessing comprises generating one or more features based on the historical production data, the one or more features being used in training the ML model. 
     
     
         13 . The method of  claim 7 , further comprising partitioning, using a data partitioner, the processed data  216  into a training dataset for training the ML model and a testing dataset for validating the ML model. 
     
     
         14 . The method of  claim 7 , further comprising:
 modifying a loss function to include one or more penalty terms based on the constraints data; and   training the ML model according to the loss function.   
     
     
         15 . The method of  claim 7 , further comprising selecting an ML algorithm from a number of ML algorithms for use as the ML model. 
     
     
         16 . The method of  claim 7 , wherein the ML model is a constraint satisfaction neural network (CSNN). 
     
     
         17 . A system comprising:
 one or more computing platforms configured to:
 receive input data for training a machine learning (ML) algorithm, the input data comprising historical production data, historical reservoir data, and historical equipment performance data; 
 preprocess the input data to provide processed input data for training the ML algorithm; 
 train the ML algorithm to provide a trained ML model for predicting a production volume for the one or more wells based on the processed input data; and 
 employ the trained ML model to adjust a production volume of the one or more wells based on the predicted production volume. 
   
     
     
         18 . The system of  claim 17 , wherein the ML model is a first trained ML model, and the one or more computing platform are configured to preprocess to:
 predict using a data prediction model missing data points in the historical production data, the data prediction model corresponding to a second trained ML model;   identify and remove using an outlier detector anomalies and/or inconsistent data points in the historical production data; and   generate one or more features based on the historical production data, the one or more features being used in training the ML model.   
     
     
         19 . A method comprising:
 receiving, at a machine learning (ML) training system, input data for training an ML algorithm, the input data comprising historical production data, historical reservoir data, and historical equipment performance data;   preprocessing, using a data input processor of the ML training system, the input data to provide processed input data for training the ML algorithm;   modifying a loss function to include one or more penalty terms to account for one or more constraints for one or more wells;   training, using an ML engine of the ML training system, the ML algorithm according to the modified loss function to provide a trained ML model for predicting a production volume for the one or more wells based on the processed input data;   outputting, using the ML training system, the trained ML model for use in an allocation system in response to the training;   receiving, at an allocation system, well performance data for one or more wells and constraints data;   predicting, using the trained ML model of the allocation system, a production volume for the one or more wells based on the well performance data and the constraints data;   simulating, using a simulator, a reservoir model to evaluate an outcome of the predicted production volume for the one or more wells;   updating, using the allocation system, the predicted production volume for the one or more wells based on the simulation of the reservoir model; and   adjusting a production volume of the one or more wells based on the predicted production volume.   
     
     
         20 . The method of  claim 19 , wherein the predicted production volume is a first predicted production volume, the method further comprising:
 predicting using a linear programming model a second predicted production volume for the one or more wells based on the well performance data and the constraints data;   predicting using an evolutionary algorithm a third predicted production volume for the one or more wells based on the well performance data and the constraints data; and   selecting one of the first, second, and third predicted production volumes, the simulator being provided with the selected predicted production volume for simulation.