Hybrid Machine Learning-Mathematical Programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

Abstract

This paper presents a novel mathematical programming approach that simultaneously incorporates a mixed-integer nonlinear programming formulation with machine learning models to determine the operating conditions, gas production, and optimal water management for the completion phase in shale gas fields. The dataset for the development of an artificial neural network model has been collected from the Eagle Ford Texas formation. The total cumulative gas production and flowback water generated in shale gas wells are selected as output variables. The mathematical optimization model considers machine learning models for each well, mass balances, treatment, storage, reuse, and disposal options as well as well location selection and associated costs and revenues from the sale of the shale gas produced. A case study has been used to illustrate the benefits of the proposed approach. The compromise solution offers a water consumption per produced energy of 6.71 L/GJ in addition to the fact that 27% of the total fracture water required can be obtained by reusing the flowback water with attractive economic indicators.