Analysis Of Gas Production Data Via An Intelligent Model: Application Natural Gas Production

Abstract

Summary Estimation of natural gas reserves and forecasting future gas production throughout gas reservoirs is a critical issue for upstream experts. One of the practical approaches for defeating the aforementioned obstacle is decline curve analysis (DCA) which is a mathematical based approach to coordinate actual gas production rates of group of wells, individual wells, or reservoirs with proper function in order to forecast the efficiency of the production in future with the aim of extrapolation of the fitted decline function. Accordingly, applying robust predictive models in this area is of great interest in a gas production system. The current study demonstrates the framework for applying the predictive approach based on coupling artificial neural network and swarm optimization to estimate initial decline rate and cumulative gas production. Particle swarm optimization (PSO) was employed to choose and optimize weights and biases of a neural network which are embedded in PSO-ANN model. Utilization of this model showed high competence of the applied model in terms of coefficient of determination (R2) of 0.9865 and 0.9955, mean squared error (MSE) of 0.00013 and 2.4618 from experimental values for forecasted cumulative gas production and initial decline rate, correspondingly. Executing the suggested model is quite precise and user-friendly to determine the initial decline rate and cumulative gas production with negligible uncertainty. Petroleum experts can easily evolve their own software or program to determine gas reserves and production efficiency in reservoirs.