A Production Prediction Method for Shale Gas Wells Based on Multiple Regression

Wente Niu, Jialiang Lu and Yuping Sun
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Wente Niu: School of Engineering Science, University of Chinese Academy of Sciences, Beijing 101400, China
Jialiang Lu: School of Engineering Science, University of Chinese Academy of Sciences, Beijing 101400, China
Yuping Sun: Research Institute of Petroleum Exploration and Development, Beijing 100089, China

Energies, 2021, vol. 14, issue 5, 1-11

Abstract: The estimated ultimate recovery (EUR) of a single shale gas well is one of the important evaluation indicators for the scale and benefit development of shale gas, which is affected by many factors such as geological and engineering, so its accurate prediction is difficult. In order to realize the accurate prediction of ultimate recovery, this study considered 172 shale gas wells in the Weiyuan block as samples and selected 19 geological and engineering factors that affect the ultimate recovery of shale gas wells. Furthermore, eight key controlling factors were selected by means of the Pearson correlation coefficient and maximum mutual information coefficient comprehensive evaluation method. The data were divided into training and testing samples. Different numbers of training samples were selected and seven schemes were designed. Based on the key controlling factors, the ultimate recovery prediction model for shale gas wells in this block was established through multiple regression methods. The effectiveness of the prediction model was verified by analyzing the testing samples. The result shows that with the increase of the size of training samples, the error of the ultimate recovery predicted by the model gradually decreases gradually. When predicting the single gas well, the average absolute error of ultimate recovery is less than 20% if the number of the training gas well is more than 80. When analyzing the development potential of similar blocks without drilling, the error of the sum of ultimate recovery is less than 10% if the size of the training gas well reaches 60.

Keywords: shale gas well; multiple regression; Weiyuan block; key controlling factors; production prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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