Quantitative Evaluation, Efficient Development, Seepage, and Simulation of Geo-Energy Resources

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 10 January 2025 | Viewed by 981

Special Issue Editors


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Guest Editor
School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Interests: quantitative characterization and geological modeling of complex oil and gas reservoirs; theory and methods of numerical simulation of complex oil and gas reservoirs; theory and simulation of EGS development

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Guest Editor
College of Energy, Chengdu University of Technology, Chengdu 610059, China
Interests: THMC-coupled processes; inverse modeling; geothermal energy development; carbon storage

Special Issue Information

Dear Colleagues,

With the increasing demand for quantitative evaluation, efficient development, and accurate simulation of geo-energy resources, the field of geoscience and energy engineering has been witnessing significant advancements. This Special Issue aims to explore the latest developments and applications in the evaluation of geo-energy resources, effective methods for promoting their extraction, and the simulation of reservoir flow in geological formations.

The topics covered in this Special Issue include, but are not limited to:

  • Quantitative evaluation of geo-energy resources, including methods for assessing the potential and feasibility of various forms of geothermal, oil, gas, and other underground resources.
  • Efficient development of geo-energy resources, focusing on innovative techniques and technologies for enhancing extraction efficiency, reducing environmental impact, and optimizing the overall performance of energy extraction processes.
  • Seepage and simulation of geological resources, encompassing advanced modeling and simulation techniques for understanding the flow behavior of fluids, gases, and other substances within subsurface reservoirs and the impact on resource extraction.

Contributions from researchers, practitioners, and experts in the field are welcome to share their latest findings, methodologies, and case studies. We encourage interdisciplinary approaches that combine geoscience, engineering, and computational modeling to address the challenges and opportunities in harnessing geo-energy resources.

Dr. Zhixue Sun
Prof. Dr. Xiaoguang Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geo-energy resources
  • quantitative evaluation
  • efficient development
  • geothermal exploitation
  • unconventional oil and gas
  • complex reservoir
  • seepage
  • flow simulation

Published Papers (3 papers)

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Research

13 pages, 4148 KiB  
Article
Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application
by Xin Wang, Min Mao, Yi Yang, Shengbin Yuan, Mingyu Guo, Hongru Li, Leli Cheng, Heng Wang and Xiaobin Ye
Processes 2024, 12(5), 975; https://doi.org/10.3390/pr12050975 (registering DOI) - 10 May 2024
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Abstract
In order to further understand the complex spatial distribution caused by the extremely strong heterogeneity of buried hill reservoirs, this paper proposes a new method for predicting the development pattern of buried hill reservoirs based on the traditional pre-drilling prediction and post-drilling evaluation [...] Read more.
In order to further understand the complex spatial distribution caused by the extremely strong heterogeneity of buried hill reservoirs, this paper proposes a new method for predicting the development pattern of buried hill reservoirs based on the traditional pre-drilling prediction and post-drilling evaluation methods that mainly rely on seismic, logging, and core data, which are difficult to meet the timeliness and accuracy of drilling operations. Firstly, the box method and normalization formula are used to process and normalize the abnormal data of element logging and engineering logging, and then the stepwise regression analysis method is used to optimize the sensitive parameters of element logging and engineering logging. The Light Gradient Boosting Machine (LightGBM) algorithm, deep neural network (DNN), and support vector machine (SVM) are used to establish a new method for predicting the development pattern of buried hill reservoirs. Lastly, a comprehensive evaluation index F1 score for the model is established to evaluate the prediction model for the development pattern of buried hill reservoirs. The F1 score value obtained from this model’s comprehensive evaluation index indicates that the LightGBM model achieves the highest accuracy, with 96.7% accuracy in identifying weathered zones and 95.8% accuracy in identifying interior zones. The practical application demonstrates that this method can rapidly and accurately predict the development mode of buried hill reservoirs while providing a new approach for efficient on-site exploration and decision-making in oil and gas field developments. Consequently, it effectively promotes exploration activities as well as enhances the overall process of oil and gas reservoir exploration. Full article
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12 pages, 847 KiB  
Article
Well Selection for CO2 Huff-n-Puff in Unconventional Oil Reservoirs Based on Improved Fuzzy Method
by Yunfeng Liu, Yangwen Zhu, Haiying Liao, Hongmin Yu, Xin Fang and Yao Zhang
Processes 2024, 12(5), 958; https://doi.org/10.3390/pr12050958 - 9 May 2024
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Abstract
The implementation of CO2 huff-n-puff in unconventional oil reservoirs represents a green development technology that integrates oil recovery and carbon storage, emphasizing both efficiency and environmental protection. A rational well selection method is crucial for the success of CO2 huff-n-puff development. [...] Read more.
The implementation of CO2 huff-n-puff in unconventional oil reservoirs represents a green development technology that integrates oil recovery and carbon storage, emphasizing both efficiency and environmental protection. A rational well selection method is crucial for the success of CO2 huff-n-puff development. This paper initially identifies eight parameters that influence the effectiveness of CO2 huff-n-puff development and conducts a systematic analysis of the impact of each factor on development effectiveness. A set of factors for well selection decisions is established with seven successful CO2 huff-n-puff cases. Subsequently, the influencing factors are classified into positive, inverse, and moderate indicators. By using an exponential formulation, a method for calculating membership degrees is calculated to accurately represent the nonlinearity of each parameter’s influence on development, resulting in a dimensionless fuzzy matrix. Furthermore, with the oil exchange ratio serving as a pivotal parameter reflecting development effectiveness, recalibration of weighting factors is performed in conjunction with the dimensionless fuzzy matrix. The hierarchical order of weighting factors, from primary to secondary, is as follows: porosity, reservoir temperature, water saturation, formation pressure, reservoir thickness, crude oil density, crude oil viscosity, and permeability. The comprehensive decision factor and oil exchange ratio exhibit a positive correlation, affirming the reliability of the weighting factors. Finally, utilizing parameters of the Ordos Basin as a case study, the comprehensive decision factor is calculated, with a value of 0.617, and the oil exchange ratio is predicted as 0.354 t/t, which falls between the Chattanooga and Eagle Ford reservoirs. This approach, which incorporates exponential membership degrees and recalibrated weighting factors derived from actual cases, breaks the limitations of linear membership calculation methods and human factors in expert scoring methods utilized in existing decision-making methodologies. It furnishes oilfield decision-makers with a swifter and more precise well selection method. Full article
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20 pages, 10450 KiB  
Article
Understanding Plugging Agent Emplacement Depth with Polymer Shear Thinning: Insights from Experiments and Numerical Modeling
by Shanbin He, Chunqi Xue, Chang Du, Yahui Mao, Shengnan Li, Jianhua Zhong, Liwen Guo and Shuoliang Wang
Processes 2024, 12(5), 893; https://doi.org/10.3390/pr12050893 - 28 Apr 2024
Viewed by 340
Abstract
Polymer-plugging agents are widely employed in profile control and water-plugging measures, serving as a crucial component for efficient reservoir development. However, quantitatively monitoring the emplacement depth of polymer-plugging agents in low-permeability and high-permeability layers remains a challenging bottleneck. Presently, insufficient attention on shear [...] Read more.
Polymer-plugging agents are widely employed in profile control and water-plugging measures, serving as a crucial component for efficient reservoir development. However, quantitatively monitoring the emplacement depth of polymer-plugging agents in low-permeability and high-permeability layers remains a challenging bottleneck. Presently, insufficient attention on shear thinning, a critical rheological property for water shut-off and profile control, has limited our understanding of polymer distribution laws. In this study, polymer shear-thinning experiments are firstly conducted to explore polymer variations with flow rate. The novelty of the research is that varying polymer viscosity is implemented instead of the fixed-fluid viscosity that is conventionally used. The fitted correlation is then integrated into the 2D and 3D heterogeneous numerical models for simulations, and a multivariate nonlinear regression analysis is performed based on the simulation results. The results show that lower polymer emplacement depth ratios corresponded to higher viscosity loss rates under the same flow rate. An increase in the initial permeability ratio corresponds to a decrease in the emplacement ratio, along with a reduction in the fraction of the plugging agent penetrating the low permeability formations. The model was applied to the Kunan Oilfield and demonstrated a polymer reduction of approximately 3000 tons compared to traditional methods. Despite the slightly complex nature of the multivariate nonlinear mathematical model, it presents clear advantages in controlling plugging agent distribution and estimating dosage, laying good theoretical ground for the effective and efficient recovery of subsurface resources. Full article
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