Finite Element and Neural Network Models to Forecast Gas Well Inflow Performance of Shale Reservoirs

Abstract

Shale gas reservoirs are one of the most rapidly growing forms of natural gas worldwide. Gas production from such reservoirs is possible by using extensive and deep well fracturing to contact bulky fractions of the shale formation. In addition, the main mechanisms of the shale gas production process are the gas desorption that takes place by diffusion of gas in the shale matrix and by Darcy’s type through the fractures. This study presents a finite element model to simulate the gas flow including desorption and diffusion in shale gas reservoirs. A finite element model is used incorporated with a quadrilateral element mesh for gas pressure solution. In the presented model, the absorbed gas content is described by Langmuir’s isotherm equation. The non-linear iterative method is incorporated with the finite element technique to solve for gas property changes and pressure distribution. The model is verified against an analytical solution for methane depletion and the results show the robustness of the developed finite element model in this study. Further application of the model on the Barnett Shale field is performed. The results of this study show that the gas desorption in Barnett Shale field affects the gas flow close to the wellbore. In addition, an artificial neural network model is designed in this study based on the results of the validated finite element model and a back propagation learning algorithm to predict the well gas rates in shale reservoirs. The data created are divided into 70% for training and 30% for the testing process. The results show that the forecasting of gas rates can be achieved with an R2 of 0.98 and an MSE = 0.028 using gas density, matrix permeability, fracture length, porosity, PL (Langmuir’s pressure), VL (maximum amount of the adsorbed gas (Langmuir’s volume)) and reservoir pressure as inputs.