Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.1
Journals
Eng
Special Issues
Advances in GeoEnergy Engineering: Innovations in Sustainable Energy Resources and...
share announcement

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 444

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
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 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. 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 1200 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

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3810 KiB  
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
Viewed by 316
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop