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Modeling of Fluids Flow in Unconventional Reservoirs

Submission Deadline: 30 April 2023 (closed) View: 146

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

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks
Jiangxia Han, Liang Xue, Ying Jia, Mpoki Sam Mwasamwasa, Felix Nanguka, Charles Sangweni, Hailong Liu, Qian Li
CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1323-1340, 2024, DOI:10.32604/cmes.2023.031093
（This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physics-informed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different… More >

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ARTICLE

Horizontal Well Interference Performance and Water Injection Huff and Puff Effect on Well Groups with Complex Fracture Networks: A Numerical Study
Haoyu Fu, Hua Liu, Xiaohu Hu, Lei Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2285-2309, 2023, DOI:10.32604/cmes.2023.027996
（This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract Well interference has become a common phenomenon with the increasing scale of horizontal well fracturing. Recent studies on well interference in horizontal wells do not properly reflect the physical model of the postfracturing well groups and the realistic fracturing process of infill wells. Establishing the correspondence between well interference causative factors and manifestations is of great significance for infill well deployment and secondary oil recovery. In this work, we develop a numerical model that considers low velocity non-Darcy seepage in shale reservoirs to study the inter-well interference phenomenon that occurs in the Santanghu field, and… More >

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ARTICLE

Numerical Stability and Accuracy of Contact Angle Schemes in Pseudopotential Lattice Boltzmann Model for Simulating Static Wetting and Dynamic Wetting
Dongmin Wang, Gaoshuai Lin
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 299-318, 2023, DOI:10.32604/cmes.2023.027280
（This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract There are five most widely used contact angle schemes in the pseudopotential lattice Boltzmann (LB) model for simulating the wetting phenomenon: The pseudopotential-based scheme (PB scheme), the improved virtual-density scheme (IVD scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the fluid layer density above the wall (MPB-C scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the weighted average density of surrounding fluid nodes (MPB-W scheme) and the geometric formulation scheme (GF scheme). But the numerical stability and accuracy of the schemes for wetting simulation remain… More >

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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
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2451-2463, 2023, DOI:10.32604/cmes.2023.021145
（This article belongs to the 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 More >
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ARTICLE

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression
Hongfei Ma, Wenqi Zhao, Yurong Zhao, Yu He
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498
（This article belongs to the 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 More >

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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
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1579-1599, 2022, DOI:10.32604/cmes.2022.019435
（This article belongs to the 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… More >

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Modeling of Fluids Flow in Unconventional Reservoirs

Guest Editors

Summary

Published Papers

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745

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708

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1

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862

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438

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969

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538

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1123

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927

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1981

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1030

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3192

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1387

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