Guest Editors
Prof. Jianchao Cai, China University of Petroleum, China
Prof. Victor Manuel Calo, Curtin University, Australia
Dr. Shuangmei Zou, China University of Geosciences, China
Summary
Unconventional oil/gas resources have become significant contributors to global hydrocarbon production in the past two decades and continue to grow in importance for the next few decades. Due to the complex pore structures and geological storage in unconventional rocks, it remains challenging to describe and model fluid flow and transport in unconventional reservoirs. Therefore, new multiscale, multiphase, and multiphysics transport modelling methods are being continuously developed to describe these complex flows within them.
This Special Issue aims to cover the recent advances and challenges for modelling low and transport for unconventional reservoirs, including shale gas/oil, coal seam gas, tight gas/oil, gas hydrate, etc. Papers that apply cutting-edge technologies and novel techniques to investigate flow and transport in unconventional reservoirs, case studies, and comprehensive overviews are all welcome.
Potential topics include but are not limited to:
• Reservoir characterization
• New simulation methods
• Multiphase flow
• Multiscale modelling
• Multiphysics modelling
• immiscible flow
• Rock–fluid interface interactions
• Pore network modelling/Lattice Boltzmann modelling
• Machine learning and big data applications for unlocking new insights in unconventional reservoirs production
Published Papers
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Open Access
ARTICLE
A Novel Method to Enhance the Inversion Speed and Precision of the NMR T2 Spectrum by the TSVD Based Linearized Bregman Iteration
Yiguo Chen, Congjun Feng, Yonghong He, Zhijun Chen, Xiaowei Fan, Chao Wang, Xinmin Ge
Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2451-2463, 2023, DOI:10.32604/cmes.2023.021145
（This article belongs to this Special Issue:
Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract The low-field nuclear magnetic resonance (NMR) technique has been used to probe the pore size distribution and the fluid composition in geophysical prospecting and related fields. However, the speed and accuracy of the existing numerical inversion methods are still challenging due to the ill-posed nature of the first kind Fredholm integral equation and the contamination of the noises. This paper proposes a novel inversion algorithm to accelerate the convergence and enhance the precision using empirical truncated singular value decompositions (TSVD) and the linearized Bregman iteration. The L1 penalty term is applied to construct the objective function, and then the linearized…
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Graphic Abstract
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Open Access
ARTICLE
A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression
Hongfei Ma, Wenqi Zhao, Yurong Zhao, Yu He
Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498
（This article belongs to this Special Issue:
Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production
plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction
methods are based on years of oil field production experience and expertise, and the application conditions
are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods
are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven
artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer
production by considering geological data, fluid PVT…
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Open Access
ARTICLE
Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models
Shaohua Gu, Jiabao Wang, Liang Xue, Bin Tu, Mingjin Yang, Yuetian Liu
Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1579-1599, 2022, DOI:10.32604/cmes.2022.019435
（This article belongs to this Special Issue:
Modeling of Fluids Flow in Unconventional Reservoirs)
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 recurrent neural network (RNN), long…
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