Dynamic Productivity Prediction Method of Shale Condensate Gas Reservoir Based on Convolution Equation

Ping Wang (), Wenchao Liu (), Wensong Huang, Chengcheng Qiao, Yuepeng Jia and Chen Liu
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Ping Wang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Wenchao Liu: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Wensong Huang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Chengcheng Qiao: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Yuepeng Jia: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Chen Liu: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China

Energies, 2023, vol. 16, issue 3, 1-20

Abstract: The dynamic productivity prediction of shale condensate gas reservoirs is of great significance to the optimization of stimulation measures. Therefore, in this study, a dynamic productivity prediction method for shale condensate gas reservoirs based on a convolution equation is proposed. The method has been used to predict the dynamic production of 10 multi-stage fractured horizontal wells in the Duvernay shale condensate gas reservoir. The results show that flow-rate deconvolution algorithms can greatly improve the fitting effect of the Blasingame production decline curve when applied to the analysis of unstable production of shale gas condensate reservoirs. Compared with the production decline analysis method in commercial software HIS Harmony RTA, the productivity prediction method based on a convolution equation of shale condensate gas reservoirs has better fitting affect and higher accuracy of recoverable reserves prediction. Compared with the actual production, the error of production predicted by the convolution equation is generally within 10%. This means it is a fast and accurate method. This study enriches the productivity prediction methods of shale condensate gas reservoirs and has important practical significance for the productivity prediction and stimulation optimization of shale condensate gas reservoirs.

Keywords: shale condensate gas reservoirs; multi-stage fractured horizontal wells; flow-rate deconvolution; Blasingame production decline typical curve; productivity 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: 2023
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