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Applied Sciences
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Petrophysical Formation Evaluation and Well Logging in Energy Exploration Systems
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Petrophysical Formation Evaluation and Well Logging in Energy Exploration Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 1178

Special Issue Editors

Prof. Dr. Liang Xiao

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Guest Editor
School of Geophysics and Information Technology, China University of Geosciences, Beijing 10083, China
Interests: formation evaluation; unconventional reservoirs; well logging; applied nuclear magnetic resonance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongyan Yu

E-Mail Website
Guest Editor
Department of Geology, Northwest University, Xi’an 710069, China
Interests: formation evaluation; well logging; unconventional reservoirs; machine learning; CCUS; digital core
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng Feng

E-Mail Website
Guest Editor
Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Interests: reservoir; oil and gas geophysical logging engineering

Special Issue Information

Dear Colleagues, 

Unconventional oil and gas reservoirs—such as shale oil/gas, coalbed methane, and tight oil/gas—often exhibit response characteristics that differ significantly from those of conventional reservoirs. Traditional petrophysical methods and formation evaluation technologies lose their effectiveness in these settings. As a results, geologists have placed higher demands on well log evaluation, interpretation methods, models, and related technologies for unconventional oil and gas resources characterization, “sweet spot” prediction, effectiveness evaluation, and productivity research. To advance both conventional and unconventional reservoir characterization, it is necessary to develop new technologies related to petrophysical experiments, well logging data processing and interpretation, theoretical modeling, and digital rock analysis.

This Special Issue seeks high-quality contributions focusing on the latest advances in petrophysical characterization and well logging evaluation techniques for both conventional and unconventional reservoirs. Topics include, but are not limited to, the following:

  • Conventional and unconventional reservoir characterization and effectiveness prediction;
  • Application of CT scanning technology in reservoir evaluation;
  • Fractured reservoirs evaluation based on advanced well logging techniques;
  • Unconventional reservoir flow mechanisms, dynamic-static integration, and productivity prediction;
  • Application of machine learning methods in conventional and unconventional reservoirs.

Prof. Dr. Liang Xiao
Prof. Dr. Hongyan Yu
Prof. Dr. Cheng Feng
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • pore structure
  • sedimentary microfacies
  • unconventional reservoirs characterization
  • effectiveness prediction
  • productivity estimation

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Published Papers (2 papers)

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Research

15 pages, 4931 KB  
Article
Geology-Constrained Time Series Generative Adversarial Network for Well Log Curve Reconstruction
by Haifeng Guo, Wenlong Liao, Bin Zhao, Xiaodong Cheng and Kun Wang
Appl. Sci. 2026, 16(7), 3421; https://doi.org/10.3390/app16073421 - 1 Apr 2026
Viewed by 283
Abstract
The anomalous logging responses caused by complex geological and downhole engineering conditions, which can be the expansion of a borehole, the formation of fractures, and the mud intrusion, usually result in the absence of some important curves and undermine the accuracy of the [...] Read more.
The anomalous logging responses caused by complex geological and downhole engineering conditions, which can be the expansion of a borehole, the formation of fractures, and the mud intrusion, usually result in the absence of some important curves and undermine the accuracy of the reservoir evaluation. The strong nonlinearity and non-stationarity of the log curves remain problematic to conventional interpolation and statistical techniques; the traditional models do not take into account any sequential relationship between points along the depth axis, whereas the deep sequence models can only regress on the points, which limits their capability of ensuring the overall geological consistency. In order to resolve these difficulties, this paper introduces a Geology-Balanced Time Series Conditional Generative Adversarial Network (GC-TSGAN) in which the lithological data is converted into an initial state in the form of prior conditions and is input into both the generator and the discriminator. The model uses LSTM to learn depth-sequential dependencies and a BCE GAN-based adversarial loss to achieve distributional consistency and local morphological fidelity. Hyperparameter tuning is used with the help of random search and Bayesian optimization. The logging data of 41 wells in the B Basin, Chad, are experimented using GC-TSGAN alongside baseline models such as RF, XGBoost, LSTM and ANN; GC-TSGAN is proven to be much better than baseline models in terms of the RMSE, MAE, and squares of predicate and value. The findings confirm that the proposed model can effectively reconstruct log curves with high precision even in a complicated geological environment, thereby providing quality data for performing geological modeling and evaluating the reservoirs. Full article
(This article belongs to the Special Issue Petrophysical Formation Evaluation and Well Logging in Energy Exploration Systems)
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22 pages, 6280 KB  
Article
Numerical Simulation and Influencing Factor Analysis of Magnetic-Field Antennas and Electric-Field Antennas for Near-Bit Wireless Short-Range Transmission
by Wenjing Cao, Qingyun Di, Fei Tian, Jingyue Liu, Aosai Zhao, Dingjun Chang and Wenhao Zheng
Appl. Sci. 2026, 16(3), 1519; https://doi.org/10.3390/app16031519 - 3 Feb 2026
Viewed by 570
Abstract
Wireless short-range transmission is essential for precise wellbore trajectory control and real-time formation evaluation. Its signal propagation characteristics are influenced by multiple factors, including antenna type, drill collar, mud, and formation resistivity. Most prior studies are based on Magnetic-field Antennas (MFA) and primarily [...] Read more.
Wireless short-range transmission is essential for precise wellbore trajectory control and real-time formation evaluation. Its signal propagation characteristics are influenced by multiple factors, including antenna type, drill collar, mud, and formation resistivity. Most prior studies are based on Magnetic-field Antennas (MFA) and primarily focus on the effects of formation resistivity variations, whereas the investigations on the influence of drill collars and mud resistivity are limited. In this study, a three-dimensional finite-element electromagnetic model of the “antenna–drill collar–mud–formation” system was developed to investigate wireless short-range transmission. The model was used to characterize and compare the electromagnetic field distributions of MFA and Electric-field Antennas (EFA) under in situ conditions. On this basis, a set of parametric sensitivity analyses on transmission performance was performed to quantify the effects of key factors, including drill-collar conductivity and mud resistivity. The results reveal fundamentally different electromagnetic field distributions for the two antenna types: (1) MFA is dominated by localized circumferential magnetic flux loops, whereas EFA transmits signals through axially extended eddy-current channels. (2) The drill collar exerts opposite effects on the two antennas, suppressing signal levels for MFA while significantly enhancing transmission for EFA, resulting in signal amplitudes that are 103−105 times higher. (3) In addition, mud resistivity has little influence on MFA, whereas increasing mud resistivity leads to the pronounced attenuation of EFA signals. These findings provide a quantitative basis for antenna selection and performance optimization in wireless short-range transmission systems under different Logging-While-Drilling (LWD) conditions. Full article
(This article belongs to the Special Issue Petrophysical Formation Evaluation and Well Logging in Energy Exploration Systems)
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