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Processes
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Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization...
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Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5302

Special Issue Editors

Prof. Dr. Zhiqi Guo

E-Mail Website
Guest Editor
College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
Interests: reservoir geophysics; seismic rock physics; seismic modeling and inversion; quantitative seismic interpretation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Feng Zhang

E-Mail Website
Guest Editor
College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China
Interests: seismic inversion; multi-component seismic and seismic anisotropy; rock physics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yang Liu

E-Mail Website
Guest Editor
College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
Interests: compressive sensing theory in seismic data processing; nonstationarity analysis of seismic data; seismic noise suppression; seismic data regularization and interpolation

Special Issue Information

Dear Colleagues,

Oil and gas extracted from unconventional reservoirs, such as shales, tight sandstones, volcanic rocks, and coalbeds, have significantly contributed to the global energy supply in recent decades. To address the challenges posed by the intricate petroleum system and the complex microstructural characteristics of these unconventional reservoirs, geophysical technologies have undergone notable deevelopment in recent years. Among the various aspects of geophysical exploration, advancements in seismic processing and inversion techniques have facilitated the characterization of unconventional reservoirs by leveraging the elastic properties of subsurface formations. Rock physics methods enhance the understanding of elastic responses and seismic signatures associated with unconventional reservoir properties, enabling the inference of reservoir parameters through quantitative seismic interpretation. Well-log techniques provide extensive petrophysical data for the comprehensive characterization of unconventional reservoirs. Additionally, gravity, magnetic, and electrical methods can be employed in unconventional reservoir exploration, offering diverse insights into subsurface rock properties.

This Special Issue will present and disseminate the latest advancements in geophysical methods, addressing the challenges inherent in exploring and characterizing unconventional oil and gas resources.

Subjects of interest for this Special Issue encompass a broad range of topics, including, but not limited to, the following:

  • All aspects of geophysical methods for the exploration of unconventional resources;
  • Seismic processing, modeling and inversion;
  • Quantitative seismic interpretation;
  • Rock physical experiments, modeling, and inversion;
  • Petrophysical analysis and well-log interpretation;
  • Gravity and magnetic exploration;
  • Electrical and electromagnetic exploration;
  • Geological and engineering sweet spots;
  • Reservoir properties;
  • Pore, fluids, and natural fractures;
  • Pore pressure and geostress.

Prof. Dr. Zhiqi Guo
Prof. Dr. Feng Zhang
Prof. Dr. Yang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

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

  • shale gas and oil
  • tight gas and oil
  • volcanic gas and oil
  • heavy oil
  • oil shale
  • coalbed methane
  • seismic data processing
  • seismic inversion
  • seismic numerical and physical modeling
  • quantitative seismic interpretation
  • rock physics
  • petrophysics
  • well-log
  • gravity and magnetic methods
  • electrical and electromagnetic methods
  • porosity
  • fluids
  • fractures
  • pore pressure
  • geostress
  • interbedded strata

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

19 pages, 13052 KiB  
Article
Seismic Porosity Prediction in Tight Carbonate Reservoirs Based on a Spatiotemporal Neural Network
by Fei Li, Zhiyi Yu, Yonggang Wang, Meixin Ju, Feng Liu and Zhixian Gui
Processes 2025, 13(3), 788; https://doi.org/10.3390/pr13030788 - 8 Mar 2025
Viewed by 523
Abstract
Porosity prediction from seismic data is of significance in reservoir property assessment, reservoir architecture delineation, and reservoir model building. However, it is still challenging to use traditional model-driven methodology to characterize carbonate reservoirs because of the highly nonlinear mapping relationship between porosity and [...] Read more.
Porosity prediction from seismic data is of significance in reservoir property assessment, reservoir architecture delineation, and reservoir model building. However, it is still challenging to use traditional model-driven methodology to characterize carbonate reservoirs because of the highly nonlinear mapping relationship between porosity and elastic properties. To address this issue, this study proposes an advanced spatiotemporal deep learning neural network for porosity prediction, which uses the convolutional neural network (CNN) structure to extract spatial characteristics and the bidirectional gated recurrent unit (BiGRU) network to gather temporal characteristics, guaranteeing that the model accurately captures the spatiotemporal features of well logs and seismic data. This method involves selecting sensitive elastic parameters as inputs, standardizing multiple sample sets, training the spatiotemporal network using logging data, and applying the trained model to seismic elastic attributes. In blind well tests, the CNN–BiGRU model achieves a 54% reduction in the root mean square error and a 6% correlation coefficient improvement, outperforming the baseline models and traditional nonlinear fitting (NLF). The application of the proposed method to seismic data indicates that the model yields a reasonable porosity distribution for tight carbonate reservoirs, proving the strong generalization ability of the proposed model. This method compensates for the limitations of individual deep learning models by simultaneously capturing the spatial and temporal components of data and improving the estimation accuracy, showing considerable promise for accurate reservoir parameter estimation. Full article
(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)
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10 pages, 4030 KiB  
Article
Genesis of an Inorganic CO2 Gas Reservoir in the Dehui–Wangfu Fault Depression, Songliao Basin, China
by Changli Liu, Yunliang Yu, Hongchen Cai, Yingchun Liu and Xiangwei Gao
Processes 2024, 12(11), 2429; https://doi.org/10.3390/pr12112429 - 4 Nov 2024
Viewed by 759
Abstract
This study systematically examines the origins and formation mechanisms of inorganic CO2 gas reservoirs located within the Dehui–Wangfu Fault in the southeastern uplift region of the Songliao Basin. The research aims to clarify the primary sources of inorganic CO2, along [...] Read more.
This study systematically examines the origins and formation mechanisms of inorganic CO2 gas reservoirs located within the Dehui–Wangfu Fault in the southeastern uplift region of the Songliao Basin. The research aims to clarify the primary sources of inorganic CO2, along with its migration and accumulation processes. The identification of the Wanjinta gas reservoir within the Dehui–Wangfu Fault Zone, abundant in inorganic CO2, has sparked significant interest in the pivotal roles of volcanism and tectonic activity in gas generation and concentration. To analyze the release characteristics of CO2, this study conducted degassing experiments on volcanic and volcaniclastic rock samples from various boreholes within the fault trap. It evaluated CO2 release behaviors and controlling factors across varying temperatures (150 °C to 600 °C) and particle sizes (20, 40, and 100 µm). The findings indicated a negative correlation between CO2 release and particle size, with a notable transition at 300 °C—marking this temperature as critical for the release of adsorbed and lattice gases. Moreover, volcaniclastic rocks exhibited higher CO2 release compared to volcanic rocks, attributable to their larger specific surface area and higher porosity. At 600 °C, the decomposition of the rock crystal structure results in substantial gas escape. These observations suggest that the inorganic CO2 in this area derives not only from mantle sources but is also influenced by crustal components. Elevated temperatures prompted by tectonic activity and magmatic intrusion facilitated the degassing of the surrounding rocks, allowing released CO2 to migrate upwards through the fracture system and accumulate in the shallow crust, ultimately forming a gas reservoir. This study enhances the understanding of volcanic rock’s roles in inorganic CO2 gas generation and migration, highlighting the fracture system’s critical controlling influence on gas transport and aggregation. The findings indicate that inorganic CO2 gas reservoirs in the Dehui–Wangfu Fault Zone primarily originate from mantle sources with a mixture of crustal gases. This discovery offers new theoretical insights and practical guidance for the exploration and development of gas reservoirs in the Songliao Basin and similar regions. Full article
(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)
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23 pages, 6663 KiB  
Article
Micro–Nano 3D CT Scanning to Assess the Impact of Microparameters of Volcanic Reservoirs on Gas Migration
by Xiangwei Gao, Yunliang Yu, Zhongjie Xu and Yingchun Liu
Processes 2024, 12(9), 2000; https://doi.org/10.3390/pr12092000 - 17 Sep 2024
Viewed by 1064
Abstract
Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault [...] Read more.
Volcanic rock reservoirs for oil and gas are known worldwide for their considerable heterogeneity. Micropores and fractures play vital roles in the storage and transportation of natural gas. Samples from volcanic reservoirs in Songliao Basin, CS1 and W21, belonging to the Changling fault depression and the Wangfu fault depression, respectively, have similar lithology. This study employs micro–nano CT scanning technology to systematically identify the key parameters and transport capacities of natural gas within volcanic reservoirs. Using Avizo 2020.1software, a 3D digital representation of rock core was reconstructed to model pore distribution, connectivity, pore–throat networks, and fractures. These models are then analyzed to evaluate pore/throat structures and fractures alongside microscopic parameters. The relationship between micropore–throat structure parameters and permeability was investigated by microscale gas flow simulations and Pearson correlation analyses. The results showed that the CS1 sample significantly exceeded the W21 sample in terms of pore connectivity and permeability, with connected pore volume, throat count, and specific surface area being more than double that of the W21 sample. Pore–throat parameters are decisive for natural gas storage and transport. Additionally, based on seepage simulation and the pore–throat model, the specific influence of pore–throat structure parameters on permeability in volcanic reservoirs was quantified. In areas with well–developed fractures, gas seepage pathways mainly follow fractures, significantly improving gas flow efficiency. In areas with fewer fractures, throat radius has the most significant impact on permeability, followed by pore radius and throat length. Full article
(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)
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13 pages, 42314 KiB  
Article
The Seismic Identification of Small Strike-Slip Faults in the Deep Sichuan Basin (SW China)
by Hai Li, Jiawei Liu, Majia Zheng, Siyao Li, Hui Long, Chenghai Li and Xuri Huang
Processes 2024, 12(7), 1508; https://doi.org/10.3390/pr12071508 - 18 Jul 2024
Cited by 2 | Viewed by 1130
Abstract
Recently, the “sweet spot” of a fractured reservoir, controlled by a strike-slip fault, has been found and become the favorable target for economic exploitation of deep (>4500 m) tight gas reservoirs in the Sichuan Basin, Southwestern China. However, hidden faults of small vertical [...] Read more.
Recently, the “sweet spot” of a fractured reservoir, controlled by a strike-slip fault, has been found and become the favorable target for economic exploitation of deep (>4500 m) tight gas reservoirs in the Sichuan Basin, Southwestern China. However, hidden faults of small vertical displacements (<20 m) are generally difficult to identify using low signal–noise rate seismic data for deep subsurfaces. In this study, we propose a seismic processing method to improve imaging of the hidden strike-slip fault in the central Sichuan Basin. On the basis of the multidirectional and multiscale decomposition and reconstruction processes, seismic information on the strike-slip fault can be automatically enhanced to improve images of it. Through seismic processing, the seismic resolution increased to a large extent enhancing the fault information and presenting a distinct fault plane rather than an ambiguous deflection of the seismic wave, as well as a clearer image of the sectional seismic attributes. Subsequently, many more small strike-slip faults, III–IV order faults with a vertical displacement, in the range of 5–20 m, were identified with the reprocessing data for the central Sichuan Basin. The pre-Mesozoic intracratonic strike-slip fault system was also characterized using segmentation and paralleled dispersive distribution in the Sichuan Basin, suggesting that this seismic process method is applicable for the identification of deep, small strike-slip faults, and there is great potential for the fractured reservoirs along small strike-slip fault zones in deep tight matrix reservoirs. Full article
(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)
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18 pages, 4342 KiB  
Article
Shear-Wave Velocity Prediction Based on the CNN-BiGRU Integrated Network with Spatiotemporal Attention Mechanism
by Yaqi Liu, Chuqiao Gao and Bin Zhao
Processes 2024, 12(7), 1367; https://doi.org/10.3390/pr12071367 - 30 Jun 2024
Cited by 2 | Viewed by 1114
Abstract
Shear wave velocity is one of the important parameters reflecting the lithological and physical properties of reservoirs, and it is widely used in the fields of lithology and fluid property identification, reservoir evaluation, seismic data processing, and interpretation. However, due to the high [...] Read more.
Shear wave velocity is one of the important parameters reflecting the lithological and physical properties of reservoirs, and it is widely used in the fields of lithology and fluid property identification, reservoir evaluation, seismic data processing, and interpretation. However, due to the high cost and challenge of obtaining shear wave velocity, only a few key wells are measured. Considering the intricate nonlinear mapping relationship between shear wave velocity and conventional logging data, an integrated network incorporating an attention mechanism, a convolutional neural network, and a bidirectional gated recurrent unit (STACBiN) is proposed for predicting shear wave velocity. The impact of conventional logging data on shear wave velocity is analyzed, thus employing the attention mechanism to focus on data correlated with shear wave velocity, which can enable the prediction results of the method proposed superior to those of conventional methods. Additionally, the prediction results of this method are compared with the prediction results of the two-dimensional convolutional neural network (2DCNN) and bidirectional gated recurrent unit (BiGRU). It is verified that the network proposed can effectively predict the shear wave velocity, with minimal error between predicted and true values. Full article
(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)
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