Fang Li^1,*, Juan Wu¹, Haiyong Yi², Lihong Wu², Lingyun Du¹, Yuan Zeng¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.5, pp. 1015-1030, 2024, DOI:10.32604/fdmp.2023.043256

Abstract Methods for horizontal well spacing calculation in tight gas reservoirs are still adversely affected by the complexity of related control factors, such as strong reservoir heterogeneity and seepage mechanisms. In this study, the stress sensitivity and threshold pressure gradient of various types of reservoirs are quantitatively evaluated through reservoir seepage experiments. On the basis of these experiments, a numerical simulation model (based on the special seepage mechanism) and an inverse dynamic reserve algorithm (with different equivalent drainage areas) were developed. The well spacing ranges of Classes I, II, and III wells in the Q gas More > Graphic Abstract

Mingjing Lu^1,2,*, Zenglin Wang^1,3, Aishan Li¹, Liaoyuan Zhang¹, Bintao Zheng¹, Zilin Zhang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.5, pp. 1269-1281, 2023, DOI:10.32604/fdmp.2023.023188

Abstract A mathematical model for the gas-water two-phase flow in tight gas reservoirs is elaborated. The model can account for the gas slip effect, stress sensitivity, and high-speed non-Darcy factors. The related equations are solved in the framework of a finite element method. The results are validated against those obtained by using the commercial software CMG (Computer Modeling Group software for advanced recovery process simulation). It is shown that the proposed method is reliable. It can capture the fracture rejection characteristics of tight gas reservoirs better than the CMG. A sensitivity analysis of various control factors More >

Shaohua Gu^1,2, Jiabao Wang³, Liang Xue^3,*, Bin Tu³, Mingjin Yang³, Yuetian Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1579-1599, 2022, DOI:10.32604/cmes.2022.019435

Abstract Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the… More >