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Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and...
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Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and Unconventional Reservoirs

A special issue of Eng (ISSN 2673-4117). This special issue belongs to the section "Chemical, Civil and Environmental Engineering".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 4567

Special Issue Editor

Prof. Dr. Reza Rezaee

E-Mail Website
Guest Editor
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kent St, Bentley, WA 6102, Australia
Interests: formation evaluation; petrophysics; unconventional gas (tight gas sand and shale gas); reservoir characterization and modeling; natural hydrogen
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global energy transition demands innovative approaches to sustainable and unconventional energy resources. GeoEnergy engineering plays a pivotal role in unlocking new energy sources—such as natural hydrogen, geothermal systems, and subsurface energy storage—while optimizing extraction from unconventional reservoirs like shale gas, tight oil, and methane hydrates. These advancements are critical for meeting future energy needs while minimizing environmental impact.

This Special Issue seeks to showcase cutting-edge research on emerging GeoEnergy technologies, geological systems, and engineering solutions. We welcome original papers, critical reviews, rapid communications, technical notes, and discussions that are not limited to the following themes: 

  1. Novel Energy Sources in Geoscience:
    - Natural hydrogen: Geology, generation mechanisms, exploration methods, and sustainable extraction.
    - Geothermal energy advancements: Enhanced geothermal systems (EGSs), low-enthalpy resources, and hybrid energy solutions.
    - Methane hydrates: Exploration techniques, production challenges, and environmental considerations. 
  1. Unconventional Reservoir Engineering:
    - Shale gas, tight oil, and coalbed methane: Innovations in hydraulic fracturing, reservoir characterization, and recovery optimization.
    - Carbonate and fractured reservoirs: New approaches to modeling and production enhancement.
    - Petrophysical and well log interpretation challenges of unconventional reservoirs.
    - Geomechanical and drilling aspects of unconventional reservoirs.
  1. Exploration and Monitoring Technologies:
    - Advanced geophysical/geochemical methods for resource detection (e.g., AI-driven seismic interpretation, microbial prospecting).
    - Remote sensing and machine learning applications in reservoir assessment.
    - Leakage detection and long-term monitoring of subsurface energy systems. 
  1. Sustainable Extraction and Environmental Impact:
    - Low-carbon extraction technologies and circular economy approaches.
    - Lifecycle analysis and environmental risk assessment of GeoEnergy projects.
    - Integration of renewable energy with subsurface storage (e.g., hydrogen, compressed air). 

We encourage submissions from multidisciplinary fields—geology, petroleum engineering, and environmental science—to foster collaborative solutions for the future of GeoEnergy.

We look forward to your contributions toward advancing knowledge and innovation in this transformative field.

Prof. Dr. Reza Rezaee
Guest Editor

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. Eng 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 1400 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

  • GeoEnergy engineering
  • unconventional reservoirs
  • sustainable extraction
  • natural hydrogen
  • geothermal advancements

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Published Papers (4 papers)

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Research

14 pages, 1969 KB  
Article
Study on Microscopic Seepage Simulation of Tight Sandstone Reservoir Based on Digital Core Technology
by Hui Chen, Xiaopeng Cao and Lin Du
Eng 2026, 7(1), 25; https://doi.org/10.3390/eng7010025 - 4 Jan 2026
Viewed by 350
Abstract
Understanding the flow characteristics of tight sandstone reservoirs is crucial for improving resource recovery efficiency. During fluid flow in porous media, surfactant components in the fluid can adsorb onto solid surfaces, forming a boundary layer. This boundary layer has a pronounced impact on [...] Read more.
Understanding the flow characteristics of tight sandstone reservoirs is crucial for improving resource recovery efficiency. During fluid flow in porous media, surfactant components in the fluid can adsorb onto solid surfaces, forming a boundary layer. This boundary layer has a pronounced impact on fluid movement within tight sandstone formations. In this study, digital core analysis is employed to investigate how the boundary layer influences non-Darcy flow behavior. A computational model is first developed to quantify the thickness and viscosity of the boundary layer, followed by the construction of a mathematical flow model based on the Navier–Stokes equations that incorporates boundary layer effects. Using CT scan data from actual core samples, a pore network model is then built to represent the reservoir’s complex pore structure. The impact of boundary layer development on microscale flow is subsequently analyzed under varying pore conditions. The results indicate that both boundary layer thickness and viscosity significantly influence fluid transport in microscopic pores. When the relative boundary layer thickness is 0.5, and the relative viscosity reaches 10, the actual outlet flow rate drops to only 12.89% of the value obtained without considering boundary layer effects. Furthermore, in tight reservoirs with smaller pore throat sizes, the boundary layer introduces considerable flow resistance. When boundary layer effects are incorporated into the pore network model, permeability initially increases with pressure gradient and then stabilizes. Full article
(This article belongs to the Special Issue Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and Unconventional Reservoirs)
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22 pages, 2292 KB  
Article
Collapse Pressure Prediction for Marine Shale Wellbores Considering Drilling Fluid Invasion-Induced Strength Degradation: A Bedding Plane Slip Model
by Zhilei Zhang, Chunping Li, Yuan Geng, Baohua Yu, Sicong Meng and Lihui Wang
Eng 2025, 6(12), 353; https://doi.org/10.3390/eng6120353 - 5 Dec 2025
Viewed by 543
Abstract
The stability of deep marine shale wellbores is influenced by both bedding anisotropy and drilling fluid intrusion. Existing models fail to adequately account for the coupled effects of intrusion depth and strength degradation. This study, targeting Longmaxi Formation shale, established a collapse pressure [...] Read more.
The stability of deep marine shale wellbores is influenced by both bedding anisotropy and drilling fluid intrusion. Existing models fail to adequately account for the coupled effects of intrusion depth and strength degradation. This study, targeting Longmaxi Formation shale, established a collapse pressure prediction model incorporating drilling fluid intrusion depth through direct shear tests and nuclear magnetic resonance (NMR) techniques. Experimental results indicate that shear strength reaches its minimum at β = 45°, decreasing by approximately 60% compared to β = 0° or 90°. Intrusion causes exponential decay in bedding plane strength, with the cohesion degradation coefficient λc = 0.158 mm−1 significantly exceeding the internal friction angle degradation coefficient λφ = 0.089 mm−1. Sensitivity analysis indicates that bedding angle and invasion depth rank third (±3%) and fourth (±1.5%), respectively, in influencing collapse pressure. Field validation confirmed excellent model prediction accuracy (R2 = 0.956; RMSE = 0.55 MPa; MAPE = 1.05%), with all errors below 4%. This model accurately predicts the time-varying characteristics of collapse pressure, providing a theoretical basis for optimizing the design of drilling fluid density. Full article
(This article belongs to the Special Issue Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and Unconventional Reservoirs)
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14 pages, 2426 KB  
Article
Assessing Fault Slip Probability and Controlling Factors in Shale Gas Hydraulic Fracturing
by Kailong Wang, Wei Lian, Jun Li and Yanxian Wu
Eng 2025, 6(10), 272; https://doi.org/10.3390/eng6100272 - 11 Oct 2025
Viewed by 711
Abstract
Fault slips induced by hydraulic fracturing are the primary mechanism of casing de-formation during deep shale gas development in Sichuan’s Luzhou Block, where de-formation rates reach 51% and severely compromise productivity. To address a critical gap in existing research on quantitative risk assessment [...] Read more.
Fault slips induced by hydraulic fracturing are the primary mechanism of casing de-formation during deep shale gas development in Sichuan’s Luzhou Block, where de-formation rates reach 51% and severely compromise productivity. To address a critical gap in existing research on quantitative risk assessment systems, we developed a probabilistic model integrating pore pressure evolution dynamics with Monte Carlo simulations to quantify slip risks. The model incorporates key operational parameters (pumping pressure, rate, and duration) and geological factors (fault friction coefficient, strike/dip angles, and horizontal stress difference) validated through field data, showing >90% slip probability in 60% of deformed well intervals. The results demonstrate that prolonged high-intensity fracturing increases slip probability by 32% under 80–100 MPa pressure surges. Meanwhile, an increase in the friction coefficient from 0.40 to 0.80 reduces slip probability by 6.4% through elevated critical pore pressure. Fault geometry exhibits coupling effects: the risk of low-dip faults reaches its peak when strike parallels the maximum horizontal stress, whereas high-dip faults show a bimodal high-risk distribution at strike angles of 60–120°; here, the horizontal stress difference is directly proportional to the slip probability. We propose optimizing fracturing parameters, controlling operation duration, and avoiding high-risk fault geometries as mitigation strategies, providing a scientific foundation for enhancing the safety and efficiency of shale gas development. Full article
(This article belongs to the Special Issue Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and Unconventional Reservoirs)
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22 pages, 3810 KB  
Article
Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning
by Samuel Nashed and Rouzbeh Moghanloo
Eng 2025, 6(4), 73; https://doi.org/10.3390/eng6040073 - 5 Apr 2025
Cited by 8 | Viewed by 2206
Abstract
Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, [...] Read more.
Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R2 values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R2 correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R2 values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R2 values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article
(This article belongs to the Special Issue Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and Unconventional Reservoirs)
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