Submit to Special Issue Submit Abstract to Special Issue Review for Processes Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 27 January 2027 | Viewed by 6396

Share This Special Issue

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

1. Faculty of Mathematics and Natural Sciences, Christian-Albrechts-Universität, 24118 Kiel, Germany
2. State Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China
Interests: fractal; multifractal analysis; geochemistry; geomechanics; AI in Geosciences; AI/ML for geostorage; AI/ML for sustainable resource production
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 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. Processes 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

data acquisition
data processing
data analysis
process control
energy exploration

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

23 pages, 5963 KB

Open AccessArticle

A Transient Thermo-Hydraulic Study of Mass and Heat Transfer and Phase Behavior of CO₂ in Fractured Wellbores

by Zefeng Li, Hongzhong Zhang, Guoliang Liu, Yining Zhou, Jianping Lan, Long Chai, Zihao Yang and Jiarui Cheng

Processes 2026, 14(9), 1330; https://doi.org/10.3390/pr14091330 - 22 Apr 2026

Viewed by 193

Abstract

This research presents a two-dimensional transient thermo-hydraulic model designed to study how temperature and pressure change within a wellbore during CO₂ tubing fracturing. The model integrates one-dimensional axial compressible flow with radial heat transfer across the tubing, annulus, casing, cement sheath, and surrounding geological formation. Using the predicted temperature and pressure distributions, the phase behavior of the fracturing fluid along the wellbore is assessed. To enhance the accuracy of phase predictions, a visualization experiment is performed on a CO₂-based fracturing fluid containing 5 wt% of the thickener HPG. The critical transition conditions obtained experimentally are used to adjust the model accordingly. The study systematically examines the influence of key operational parameters such as injection rate, wellhead pressure, injection temperature, and the geothermal gradient of the formation. Findings reveal that injection conditions mainly govern the temperature and velocity fields, while heat transfer from the formation has a lesser impact during short-term injections. Pressure steadily decreases along the wellbore due to friction and fluid compressibility. A method based on density gradients is introduced to determine the depth at which phase transitions occur. Overall, this work offers a practical approach for predicting thermo-hydraulic behavior and phase changes during CO₂ fracturing processes. Full article

(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)

► Show Figures

Figure 1

18 pages, 2036 KB

Open AccessArticle

A Hybrid PNN–XGBoost Framework for Gas–Water Flow Pattern Prediction and 3D Visualization in Near-Horizontal Wells

by Tong Lei, Junfeng Liu, Rongqi Yang, Yu Chen, Tianjun Zhang and Zhongliang Zhao

Processes 2026, 14(7), 1087; https://doi.org/10.3390/pr14071087 - 27 Mar 2026

Viewed by 340

Abstract

The distribution of gas–water two-phase flow in near-horizontal wells is influenced by factors such as wellbore inclination and phase flow rates. To explore these effects, a laboratory loop simulating downhole conditions was used to conduct experiments under varying inclinations and flow parameters. Flow patterns were classified based on visual observations and existing theory, and scatter plots were used to analyze flow regime boundaries. Three classification models were developed and compared. The proposed PNN–XGBoost framework integrates explicit second-order feature crossing with XGBoost-based importance selection prior to probabilistic neural network classification. Among the evaluated models, the PNN–XGBoost approach achieved the highest predictive performance. The model was further validated using 3D wellbore holdup imaging, confirming its robustness in flow pattern identification and its applicability to practical well logging interpretation. Full article

(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)

► Show Figures

Figure 1

28 pages, 5555 KB

Open AccessArticle

Pore Structure Prediction from Well Logs in Deep Tight Sandstone Reservoirs Using Machine Learning Methods

by Jiahui Ke, Peiqiang Zhao, Qiran Lv, Chuang Han, Kang Bie and Tianze Jin

Processes 2026, 14(3), 437; https://doi.org/10.3390/pr14030437 - 26 Jan 2026

Viewed by 418

Abstract

In this study, deep tight sandstone was selected as an example to propose a complete method for predicting reservoir pore structure by capillary pressure curves and conventional well log data. This method pioneers the integration of grey relational analysis, principal component analysis, ensemble clustering, and deep neural networks to establish a systematic predictive framework for transitioning from conventional logging data to pore structure types. A total of 186 core data from three wells were used in this study. First, sensitive pore structure parameters from mercury injection capillary pressure data were extracted using grey correlation analysis and principal component analysis. Then, unsupervised clustering analysis was applied to classify the reservoir pore structures in the study area, dividing it into three categories. These category labels were combined with conventional well logs to create learning samples for a deep neural network (DNN) model developed to predict reservoir pore structure categories. The accuracy of the training set of the model reached 88.2%, while the accuracy of the testing set was 80.43%. Finally, the method was applied to field well log data. The results showed significant differences in pore structure classifications among gas layers, water–gas layers, and dry layers. This method is versatile, with its core components transferable to other deep sandstone reservoir studies, and can accurately predict the pore structure of tight sandstone reservoirs, which is critical for advancing the characterization of deep and complex oil and gas reservoirs. Full article

(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)

► Show Figures

Figure 1

17 pages, 8979 KB

Open AccessArticle

Study on Physical Simulation of Shale Gas Dissipation Behavior: A Case Study for Northern Guizhou, China

by Baofeng Lan, Hongqi Liu, Chun Luo, Shaopeng Li, Haishen Jiang and Dong Chen

Processes 2026, 14(2), 368; https://doi.org/10.3390/pr14020368 - 21 Jan 2026

Viewed by 235

Abstract

The Longmaxi from the Anchang Syncline in northern Guizhou exhibits a high degree of thermal evolution of organic matter and significant variation in gas content. Because the synclinal is narrow, steep, and internally faulted, the mechanisms controlling shale gas preservation and escape remain poorly understood, complicating development planning and engineering design. Research on oil and gas migration and accumulation mechanisms in synclinal structures is therefore essential. To address this issue, three proportionally scaled strata—pure shale, gray shale, and sandy shale—were fabricated, and faults and artificial fractures with different displacements and inclinations were introduced. The simulation system consisted of two glass tanks (No. 1 and No. 2). Each tank had three rows of eight transmitting electrodes on one side, and a row of eight receiving electrodes on the opposite side. Tank 1 remained fixed, while Tank 2 could be hydraulically tilted up to 65° to simulate air and water migration under varying formation inclinations. A gas-water injection device was connected at the base. Gas was first injected slowly into the model. After injecting a measured volume (recorded via the flowmeter), the system was allowed to rest for 24–48 h to ensure uniform gas distribution. Water was then injected to displace the gas. During displacement, Tank 1 remained horizontal, and Tank 2 was inclined at a preset angle. An embedded monitoring program automatically recorded resistivity data from the 48 electrodes, and water-driven gas migration was analyzed through resistivity changes. A gas escape rate parameter (G_d), based on differences in gas saturation, was developed to quantify escape velocity. The simulation results show that gas escape increased with formation inclination. Beyond a critical angle, the escape rate slowed and approached a maximum. Faults and fractures significantly enhanced gas escape. Full article

(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)

► Show Figures

Figure 1

18 pages, 7448 KB

Open AccessArticle

Sedimentary Facies Characteristics of Coal Seam Roof at Qinglong and Longfeng Coal Mines

by Juan Fan, Enke Hou, Shidong Wang, Kaipeng Zhu, Yingfeng Liu, Kang Guo, Langlang Wang and Hongyan Yu

Processes 2025, 13(10), 3353; https://doi.org/10.3390/pr13103353 - 20 Oct 2025

Cited by 1 | Viewed by 593

Abstract

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

(This article belongs to the Special Issue Data Acquisition, Processing, Analysis Methods and Process Control in Energy Exploration Systems)

► Show Figures

Figure 1

26 pages, 7178 KB

Open AccessArticle

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 1165

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 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)

► Show Figures

Figure 1

19 pages, 3729 KB

Open AccessArticle

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

Cited by 1 | Viewed by 939

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 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)

► Show Figures

Figure 1

23 pages, 10704 KB

Open AccessArticle

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

Cited by 3 | Viewed by 839

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, a carbonate reservoir. By integrating NMR T₂ spectrum characteristic parameters (such as T₂ geometric mean, T_2R35/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)

► Show Figures

Figure 1

20 pages, 6888 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1063

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI