Application of Support Vector Regression Machine to Productivity Prediction in Gas Well

Abstract

Abstract: The gas well productivity prediction is one of the main tasks to guide the reasonable production of a gas field in gas pool engineering study. It has intensely forecastive and initiative in the whole evaluation of a gas field and in the process of high efficiency development. Based on the well log interpretation, this paper introduces the support vector regression machine (SVRM)which has preferable forecasting effect to set up the forecasting model for gas well productivity, being suitably applied to prediction of the gas well productivity without dynamic data. The case study shows that this method has more accuracy than traditional nerve network and is a successful evaluating method for gas well productivity prediction. Meantime, it offers a new way for fast quantitative assessment of oil-gas reservoirs.

Key words: gas well, productivity prediction, gas pool, support vector machine

CLC Number:

TE313.8

TONG Kai-jun, SHAN Yu-ming, LI Hai-peng, LI Xing-yun. Application of Support Vector Regression Machine to Productivity Prediction in Gas Well[J]. Xinjiang Petroleum Geology, 2008, 29(3): 382-384.

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Application of Support Vector Regression Machine to Productivity Prediction in Gas Well

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