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Processes
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Data Acquisition, Processing, Analysis Methods and Process Control in Energy...
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Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems

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

Deadline for manuscript submissions: 15 February 2026 | Viewed by 1777

Special Issue Editors

Dr. Xin Nie

E-Mail Website
Guest Editor
College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China
Interests: digital rock physics; shale oil and gas; formation evaluation
Special Issues, Collections and Topics in MDPI journals
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. Mehdi Ostadhassan

E-Mail Website
Guest Editor
Faculty of Mathematics and Natural Sciences, Christian-Albrechts-Universität, 24118 Kiel, Germany
Interests: fractal; multifractal analysis; geochemistry; geomechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The energy sector is undergoing a transformation, driven by advances in digital technologies and the increasing demand for sustainable resource management. This Special Issue, “Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems”, invites researchers, engineers, and industry experts to submit cutting-edge research and case studies on innovative methodologies and technologies that enhance efficiency, accuracy, and sustainability in energy exploration systems. We seek high-quality interdisciplinary studies that focus on the latest novel advances in the integration of emerging tools (e.g., digital twins, edge computing, blockchain) to improve decision making, reduce environmental impact, and ensure operational safety. Contributions may include theoretical frameworks, experimental validations, field applications, or reviews that highlight trends and future directions across oil, gas, geothermal, and renewable energy sectors. Topics include, but are not limited to, challenges and solutions in the following areas:

  • Data acquisition (e.g., sensor networks, well/mud logging tools);
  • Data processing (e.g., AI/ML algorithms, real-time analytics, noise reduction);
  • Advanced data analysis (e.g., predictive modeling, reservoir characterization);
  • Process control (e.g., automation, optimization, cyber–physical systems).

Dr. Xin Nie
Dr. Hongyan Yu
Prof. Dr. Mehdi Ostadhassan
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

  • data acquisition
  • data processing
  • data analysis
  • process control
  • energy exploration

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Published Papers (4 papers)

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Research

26 pages, 7178 KiB  
Article
Super-Resolution Reconstruction of Formation MicroScanner Images Based on the SRGAN Algorithm
by Changqiang Ma, Xinghua Qi, Liangyu Chen, Yonggui Li, Jianwei Fu and Zejun Liu
Processes 2025, 13(7), 2284; https://doi.org/10.3390/pr13072284 - 17 Jul 2025
Viewed by 377
Abstract
Formation MicroScanner Image (FMI) technology is a key method for identifying fractured reservoirs and optimizing oil and gas exploration, but its inherent insufficient resolution severely constrains the fine characterization of geological features. This study innovatively applies a Super-Resolution Generative Adversarial Network (SRGAN) to [...] Read more.
Formation MicroScanner Image (FMI) technology is a key method for identifying fractured reservoirs and optimizing oil and gas exploration, but its inherent insufficient resolution severely constrains the fine characterization of geological features. This study innovatively applies a Super-Resolution Generative Adversarial Network (SRGAN) to the super-resolution reconstruction of FMI logging image to address this bottleneck problem. By collecting FMI logging image of glutenite from a well in Xinjiang, a training set containing 24,275 images was constructed, and preprocessing strategies such as grayscale conversion and binarization were employed to optimize input features. Leveraging SRGAN’s generator-discriminator adversarial mechanism and perceptual loss function, high-quality mapping from low-resolution FMI logging image to high-resolution images was achieved. This study yields significant results: in RGB image reconstruction, SRGAN achieved a Peak Signal-to-Noise Ratio (PSNR) of 41.39 dB, surpassing the optimal traditional method (bicubic interpolation) by 61.6%; its Structural Similarity Index (SSIM) reached 0.992, representing a 34.1% improvement; in grayscale image processing, SRGAN effectively eliminated edge blurring, with the PSNR (40.15 dB) and SSIM (0.990) exceeding the suboptimal method (bilinear interpolation) by 36.6% and 9.9%, respectively. These results fully confirm that SRGAN can significantly restore edge contours and structural details in FMI logging image, with performance far exceeding traditional interpolation methods. This study not only systematically verifies, for the first time, SRGAN’s exceptional capability in enhancing FMI resolution, but also provides a high-precision data foundation for reservoir parameter inversion and geological modeling, holding significant application value for advancing the intelligent exploration of complex hydrocarbon reservoirs. Full article
(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)
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19 pages, 3729 KiB  
Article
The Application of Migration Learning Network in FMI Lithology Identification: Taking Glutenite Reservoir of an Oilfield in Xinjiang as an Example
by Yangshuo Dou, Xinghua Qi, Weiping Cui, Xinlong Ma and Zhuwen Wang
Processes 2025, 13(7), 2095; https://doi.org/10.3390/pr13072095 - 2 Jul 2025
Viewed by 340
Abstract
Formation Microresistivity Scanner Imaging (FMI) plays a crucial role in identifying lithology, sedimentary structures, fractures, and reservoir evaluation. However, during the lithology identification process of FMI images relying on transfer learning networks, the limited dataset size of existing models and their relatively primitive [...] Read more.
Formation Microresistivity Scanner Imaging (FMI) plays a crucial role in identifying lithology, sedimentary structures, fractures, and reservoir evaluation. However, during the lithology identification process of FMI images relying on transfer learning networks, the limited dataset size of existing models and their relatively primitive architecture substantially compromise the accuracy of well-log interpretation results and practical production efficiency. This study employs the VGG-19 transfer learning model as its core framework to conduct preprocessing, feature extraction, and analysis of FMI well-log images from glutenite formations in an oilfield in Xinjiang, with the objective of achieving rapid and accurate intelligent identification and classification of formation lithology. Simultaneously, this paper emphasizes a systematic comparative analysis of the recognition performance between the VGG-19 model and existing models, such as GoogLeNet and Xception, to screen for the model exhibiting the strongest region-specific applicability. The study finds that lithology can be classified into five types based on physical structures and diagnostic criteria: gray glutenite, brown glutenite, fine sandstone, conglomerate, and mudstone. The research results demonstrate the VGG-19 model exhibits superior accuracy in identifying FMI images compared to the other two models; the VGG-19 model achieves a training accuracy of 99.64%, a loss value of 0.034, and a validation accuracy of 95.6%; the GoogLeNet model achieves a training accuracy of 96.1%, a loss value of 0.05615, and a validation accuracy of 90.38%; and the Xception model achieves a training accuracy of 91.3%, a loss value of 0.0713, and a validation accuracy of 87.15%. These findings are anticipated to provide a significant reference for the in-depth application of VGG-19 transfer learning in FMI well-log interpretation. Full article
(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)
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23 pages, 10704 KiB  
Article
Classification Method and Application of Carbonate Reservoir Based on Nuclear Magnetic Resonance Logging Data: Taking the Asmari Formation of the M Oilfield as an Example
by Baoxiang Gu, Juan He, Chen Hui, Hengyang Lv, Zhansong Zhang and Jianhong Guo
Processes 2025, 13(7), 2045; https://doi.org/10.3390/pr13072045 - 27 Jun 2025
Viewed by 337
Abstract
The strong heterogeneity of carbonate reservoirs poses significant technical challenges in reservoir classification and permeability evaluation. This study proposes a new method for reservoir classification based on nuclear magnetic resonance (NMR) logging data for the Asmari formation of the Middle East M Oilfield, [...] Read more.
The strong heterogeneity of carbonate reservoirs poses significant technical challenges in reservoir classification and permeability evaluation. This study proposes a new method for reservoir classification based on nuclear magnetic resonance (NMR) logging data for the Asmari formation of the Middle East M Oilfield, a carbonate reservoir. By integrating NMR T2 spectrum characteristic parameters (such as T2 geometric mean, T2R35/R50/R65, and pore volume fraction) with principal component analysis (PCA) for dimensionality reduction and an improved slope method, this study achieves fine reservoir type classification. The results are compared with core pressure curves and petrographic pore types. This study reveals that the Asmari reservoir can be divided into four categories (RT1 to RT4). RT1 reservoirs are characterized by large pore throats (maximum pore throat radius >3.8 μm), low displacement pressure (<0.2 MPa), and high permeability (average 22.16 mD), corresponding to a pore structure dominated by intergranular dissolution pores. RT4 reservoirs, on the other hand, exhibit small pore throats (<1 μm), high displacement pressure (>0.7 MPa), and low permeability (0.66 mD) and are primarily composed of dense dolostone or limestone. The classification results show good consistency with capillary pressure curves and petrographic pore types, and the pore–permeability relationships of each reservoir type have significantly higher fitting goodness (R2 = 0.48~0.68) compared with the unclassified model (R2 = 0.24). In the new well application, the root mean square error (RMSE) of permeability prediction decreased from 0.34 mD using traditional methods to 0.21 mD, demonstrating the method’s effectiveness. This approach does not rely on a large number of mercury injection experiments and can achieve reservoir classification solely through NMR logging. It provides a scalable technological paradigm for permeability prediction and development scheme optimization of highly heterogeneous carbonate reservoirs, offering valuable references for similar reservoirs worldwide. Full article
(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)
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20 pages, 6888 KiB  
Article
A New Method for Calculating Carbonate Mineral Content Based on the Fusion of Conventional and Special Logging Data—A Case Study of a Carbonate Reservoir in the M Oilfield in the Middle East
by Baoxiang Gu, Kaijun Tong, Li Wang, Zuomin Zhu, Hengyang Lv, Zhansong Zhang and Jianhong Guo
Processes 2025, 13(7), 1954; https://doi.org/10.3390/pr13071954 - 20 Jun 2025
Viewed by 490
Abstract
In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and [...] Read more.
In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article
(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)
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