Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs
Francisco Javier López-Flores, Luis Fernando Lira‐Barragán, Eusiel Rubio‐Castro, Mahmoud M. El‐Halwagi, José María Ponce‐Ortega
Abstract
In this paper, deep learning models are developed based on a multilayer perceptron to forecast 12 month cumulative produced shale gas and 90 day produced flowback water using a study area within the Eagle Ford Formation as a database. These models can help decision-makers have important references when drilling new wells. Latitude, longitude, true vertical depth, lateral longitude, total proppant, and total fracture water are used as input variables. The trained models are evaluated in a study area within the Burgos Basin to analyze the energy benefits, freshwater consumption, and flowback water produced through well drilling for the first time in Mexico. A non-dominated sorting genetic algorithm is used for the minimization of total freshwater consumption and maximization of cumulative produced gas. The multilayer perceptron models showed good results. The coefficient of determination and the mean absolute error of the single-output models remained above 0.93 and below 3.00 for the validation set, respectively.