Neural Network Approach Predicts U.S. Natural Gas Production

Abstract

The industrial and residential market for natural gas produced in the United States has become increasingly significant. Within the past 10 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 with an artificial neural network (NN).We developed an NN model to forecast the U.S. natural gas supply to 2020. Our results indicate that the U.S. will maintain its 1999 production of natural gas until 2001, after which production increases. The network model indicates that natural gas production will increase by an average rate of 0.5%/yr from 2002 to 2012. This increase will more than double from 2013 to 2020.The NN was developed with a large initial 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 NN. The remaining parameters included data from gas exploratory wells, oil/gas exploratory wells, oil exploratory wells, gas depletion rate, proved reserves, gas wellhead prices, and growth rate of the gross domestic product. The three-layer NN was successfully trained with yearly data from 1950 to 1989 using the quick-propagation learning algorithm. The NN's target output 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 prediction performance. Analysis of the test results showed that the NN approach provides an excellent match with actual gas production data. An econometric approach, called stochastic modeling or time-series analysis, was used to develop forecasting models for NN input parameters. A comparison of forecasts between this study and another is presented.The NN model has use as a short-term as well as a long-term predictive tool for natural gas supply. The model can also be used to quantitatively examine the effects of the various physical and economic factors on future gas production.

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