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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 606

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: experimental and theoritical geosciences
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 (2 papers)

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Research

24 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
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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
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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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