Development of visual prediction model for shale gas wells production based on screening main controlling factors

Wente Niu, Jialiang Lu, Yuping Sun, Wei Guo, Yuyang Liu and Ying Mu

Energy, 2022, vol. 250, issue C

Abstract: For shale gas development, clarification of the main controlling factors of production and estimated ultimate recovery (EUR) with high accuracy is indispensable. The selection of 16 critical parameters directed toward the visual output of the objective function were the most influential factors determined through a sensitivity analysis. Based on the fundamental parameters, the distance correlation coefficient was used to clarify the main controlling factors affecting the EUR of shale gas wells in Weiyuan block. Then, visual forecasting models of EUR were established using Response Surface Method (RSM), Multi-layer Feedforward Neural Network (MLFNN) and Least Square Support Vector Machine (LSSVM). Furthermore, the models developed by the three methods are compared and analyzed. The field application results of the model indicated that the model based on the LSSVM has the best field application effect. The proposed model is a serviceable tool for EUR prediction. In addition, the use of the model is efficient and convenient, and only six main controlling factors can be used to achieve the prediction of EUR. The results of this study can be extended as the main controlling factors analysis and the development of EUR visual model of shale gas wells in other blocks.

Keywords: Shale gas; Distance correlation coefficient; Least squares support vector machine; Main controlling factors; Production and EUR estimation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222007150
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007150

DOI: 10.1016/j.energy.2022.123812

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().