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Marine Well Logging and Reservoir Characterization
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Marine Well Logging and Reservoir Characterization

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Geological Oceanography".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 2737

Special Issue Editors

Prof. Dr. Changchun Zou

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Guest Editor
School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing, China
Interests: petrophysics; well logging and borehole geophysics; integrated geophysical exploration; deep-sea and polar geophysical exploration
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng Peng

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Guest Editor
School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing, China
Interests: petrophysics; well logging; cyclostratigraphy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reservoir Characterization is an essential topic in offshore energy exploration and development. It refers to the comprehensive analysis of well logging data, seismic data, and geological data to characterize the properties of the reservoirs such as lithology, physical properties, and hydrocarbon-bearing properties. During the exploration stage, seismic data plays a critical role, while in the development stage, well logging offers more value for precisely characterizing hydrocarbon reservoirs.

This Special Issue focuses on using marine well logging data primarily, integrated with geological and core analysis data, to address the challenges of characterizing complex offshore reservoirs. Potential topics include, but are not limited to, the following:

(1) Characterization of offshore reservoirs, including low-permeability reservoirs, shallow gas reservoirs, and buried-hill reservoirs;

(2) Research on petrophysics, fracture characterization, pore-structure analysis, heterogeneity evaluation, and reservoir performance evaluation;

(3) Sedimentological and stratigraphic analysis;

(4) Applications of artificial intelligence and machine learning.

Prof. Dr. Changchun Zou
Dr. Cheng Peng
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. Journal of Marine Science and Engineering 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 2600 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

  • well logging
  • petrophysics
  • reservoir characterization
  • oil and gas exploration
  • sedimentological analysis

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Published Papers (3 papers)

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Research

15 pages, 11540 KB  
Article
A Novel Model for Predicting Permeability Using Porosity Frequency Spectrum in Fractured Deep Metamorphic Rock Reservoirs
by Yunjiang Cui, Peichun Wang, Yi Qi, Ruihong Wang and Liang Xiao
J. Mar. Sci. Eng. 2026, 14(6), 534; https://doi.org/10.3390/jmse14060534 - 12 Mar 2026
Viewed by 358
Abstract
Permeability prediction of deep metamorphic rock reservoirs in the southwestern Bohai Bay Basin poses an enormous challenge due to the strong heterogeneity. Fractures widely develop in such reservoirs, yet their contributions to permeability were neglected in traditional prediction models. To develop an effective [...] Read more.
Permeability prediction of deep metamorphic rock reservoirs in the southwestern Bohai Bay Basin poses an enormous challenge due to the strong heterogeneity. Fractures widely develop in such reservoirs, yet their contributions to permeability were neglected in traditional prediction models. To develop an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19–6 Region—a typical deep metamorphic rock reservoir in the southwestern Bohai Bay Basin—as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and the correlations between the shape of porosity frequency spectrum and rock pore structure were analyzed. Afterwards, two parameters, which were defined as the logarithmic mean (φgm) and standard deviation between two golden section points (φgsr), were extracted to reflect the main peak position and wide porosity frequency spectrum, and a novel permeability prediction model was established. After the target formations were classified into two types according to the differences in pore types and pore–fracture configuration relationships, the model coefficients were calibrated. Consecutive permeability curves were derived from the proposed model in the intervals where porosity frequency spectra were obtained. Comparisons of predicted permeabilities from the proposed model, traditional method and core-measured results showed that the proposed model yielded far more reliable results, with an average relative error of only 11.12% between the predicted and core-measured permeabilities. In contrast, the average relative error of the traditional method reached 36.10%. The proposed model contributed significantly to the characterization and effectiveness evaluation of fractured deep metamorphic rock reservoirs. Full article
(This article belongs to the Special Issue Marine Well Logging and Reservoir Characterization)
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22 pages, 1658 KB  
Article
Deep Hierarchical Graph Correlation: A Two-Stage Approach to Well-Log Alignment Using CNNs and Dynamic Programming
by Sushil Acharya, Karl Fabian, Anis Yazidi and Kjetil Westeng
J. Mar. Sci. Eng. 2026, 14(1), 66; https://doi.org/10.3390/jmse14010066 - 30 Dec 2025
Cited by 2 | Viewed by 990
Abstract
Precise depth alignment of well logs is essential for reliable subsurface characterization, enabling accurate correlation of geological features across multiple wells. This study presents the Deep Hierarchical Graph Correlator (DHGC), a two-stage deep learning framework for scalable and automated well-log depth alignment. DHGC [...] Read more.
Precise depth alignment of well logs is essential for reliable subsurface characterization, enabling accurate correlation of geological features across multiple wells. This study presents the Deep Hierarchical Graph Correlator (DHGC), a two-stage deep learning framework for scalable and automated well-log depth alignment. DHGC aligns a target log to a reference log by comparing fixed-size windows extracted from both signals. In the first stage, a one-dimensional convolutional neural network (1D CNN) trained on 177,026 triplets using triplet-margin loss learns discriminative embeddings of gamma-ray (GR) log windows from eight Norwegian North Sea wells. In the second stage, a feedforward scoring network evaluates embedded window pairs to estimate local similarity. Dynamic programming then computes the optimal nonlinear warping path from the resulting cost matrix. The feature extractor achieved 99.6% triplet accuracy, and the scoring network achieved 98.93% classification accuracy with an ROC-AUC of 0.9971. Evaluation on 89 unseen GR log pairs demonstrated that DHGC improves the mean Pearson correlation coefficient from 0.35 to 0.91, with successful alignment in 88 cases (98.9%). DHGC achieved an 8.2× speedup over DTW (3.16 s versus 25.83 s per log pair). While DTW achieves a higher mean correlation (0.96 versus 0.91), DHGC avoids singularity artifacts and exhibits lower variability in distance metrics than CC, suggesting improved robustness and scalability for well-log synchronization. Full article
(This article belongs to the Special Issue Marine Well Logging and Reservoir Characterization)
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23 pages, 6052 KB  
Article
Evaluating Gas Saturation in Unconventional Gas Reservoirs Using Acoustic Logs: A Case Study of the Baiyun Depression in the Northern South China Sea
by Jiangbo Shu, Changchun Zou, Cheng Peng, Liang Xiao, Keyu Qiao, Xixi Lan, Wei Shen, Yuanyuan Zhang and Hongjie Zhang
J. Mar. Sci. Eng. 2025, 13(11), 2078; https://doi.org/10.3390/jmse13112078 - 31 Oct 2025
Viewed by 778
Abstract
Shallow gas is an unconventional natural gas resource with great potential and has received growing attention recently. Accurate estimation of gas saturation is crucial for reserves assessments and for development program formulations. However, such reservoirs are characterized by weak diagenesis, a high clay [...] Read more.
Shallow gas is an unconventional natural gas resource with great potential and has received growing attention recently. Accurate estimation of gas saturation is crucial for reserves assessments and for development program formulations. However, such reservoirs are characterized by weak diagenesis, a high clay content, and low resistivity. These properties pose significant challenges for saturation evaluations. To address the challenge of insufficient accuracy in evaluating the saturation of gas-bearing reservoirs, we propose an acoustic-based saturation evaluation method. In this study, a shallow unconsolidated rock physics model is first constructed to investigate the effect of variations in the gas saturation on elastic wave velocities. The model especially considers the patchy distribution of fluids within pores. In addition, we propose an iterative algorithm based on the updated relationship between porosity and gas saturation by introducing a correction term for the saturation to the density porosity, and successfully apply it to the logging data collected from the shallow gas reservoirs in the Pearl River Mouth Basin of the South China Sea. It is evident from the results that the saturation derived from the array acoustic logs is comparable to that obtained from the resistivity logs, with a mean absolute error of less than 6%. Additionally, it is also consistent with the drill stem test (DST) data, which further verifies the validity and reliability of this method. This study provides a novel non-electrical method for estimating the saturation of shallow gas reservoirs, which is essential to promote the evaluation of unconsolidated sandstone gas reservoirs. Full article
(This article belongs to the Special Issue Marine Well Logging and Reservoir Characterization)
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