Predicting Natural Gas Production Using Artificial Neural Network

Abstract

Abstract The industrial and residential market for natural gas produced in the United States has become increasingly significant. Within the past ten years the wellhead value of produced natural gas has rivaled and sometimes exceeded the value of crude oil. Forecasting natural gas supply is an economically important and challenging endeavor. This paper presents a new approach to predict natural gas production for the United States using an artificial neural network. We developed a neural network model to forecast U.S. natural gas supply to the Year 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas to 2001 after which production starts increasing. The network model indicates that natural gas production will increase during the period 2002 to 2012 on average rate of 0.5%/yr. This increase rate will more than double for the period 2013 to 2020. The neural network was developed with an initial large pool of input parameters. The input pool included exploratory, drilling, production, and econometric data. Preprocessing the input data involved normalization and functional transformation. Dimension reduction techniques and sensitivity analysis of input variables were used to reduce redundant and unimportant input parameters, and to simplify the neural network. The remaining input parameters of the reduced neural network included data of gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of gross domestic product. The three-layer neural network was successfully trained with yearly data starting from 1950 to 1989 using the quick-propagation learning algorithm. The target output of the neural network is the production rate of natural gas. The agreement between predicted and actual production rates was excellent. A test set, not used to train the network and containing data from 1990 to 1998, was used to verify and validate the network performance for prediction. Analysis of the test results shows that the neural network approach provides an excellent match of actual gas production data. An econometric approach, called stochastic modeling or time series analysis, was used to develop forecasting models for the neural network input parameters. A comparison of forecasts between this study and other forecast is presented. The neural network model has use as a short-term as well as a long-term predictive tool of natural gas supply. The model can also be used to examine quantitatively the effects of the various physical and economic factors on futuregas production. Introduction In recent years, there has been a growing interest in applying artificial neural networks1–4 (NN) to various areas of science, engineering, and finance. Among other applications to petroleum engineering, NN's have been used for pattern recognition in well test interpretation5 and for prediction in phase behavior.6 Artificial neural networks are an information processing technology inspired by the studies of the brain and nervous system. In other words, they are computational models of biological neural structures. Each NN generally consists of a number of interconnected processing elements (PE) or neurons grouped in layers. Fig. 1 shows the basic structure of a three-layer network: one input layer, one hidden layer, and one output layer. The neuron consists of multiple inputs and a single output. Input is the values of the independent variables and output is the dependent variables. Each input is modified by a weight, which multiplies with the input value. The input can be raw data or output of other PE's or neurons. With reference to a threshold value and activation function, the neuron will combine these weighted inputs and use them to determine its output. The output can be either the final product or an input to another neuron. This paper describes the methodology of developing an artificial neural network model to predict U.S. natural gas production. It presents the results of neural network modeling approach and compares it to other modeling approaches.