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Artificial Intelligence for a Sustainable Oil and Gas Industry and Energy Transition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 2410

Special Issue Editor

Dr. Mohammadali Ahmadi

E-Mail Website1 Website2
Guest Editor
Schulich School of Engineering, University of Calgary, Calgary, AB T2N1T4, Canada
Interests: Smart Surfactants; Enhanced Oil Recovery; Sustainable Oil and Gas Development; Molecular Dynamics; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, the oil and gas industry is undergoing a significant transformation, driven by the urgent need for sustainability and the global shift towards renewable energy sources. Artificial intelligence (AI) plays a pivotal role in this transition, offering innovative solutions to enhance efficiency, reduce the environmental impact, and optimize resource management.

This Special Issue aims to collate cutting-edge research and practical applications of AI that contribute to a more sustainable oil and gas industry and facilitate the energy transition. By leveraging AI technologies, we seek to address critical challenges and promote advancements that align with sustainability goals and the evolving energy landscape.

The topics of interest for publication include, but are not limited to, the following:

  • AI-driven optimization of exploration and production processes;
  • Predictive maintenance and failure prevention using AI;
  • AI applications in reducing emissions and minimizing the environmental impact;
  • Machine learning models for resource management and decision-making;
  • AI-enhanced data analytics for improved efficiency and cost reduction;
  • Integration of AI with renewable energy systems in hybrid models;
  • AI in energy transition planning and policy development;
  • Case studies demonstrating successful AI implementations in the oil and gas sector.

We invite authors to submit original research, reviews, and case studies that highlight the transformative potential of AI in creating a more sustainable and efficient oil and gas industry and supporting the global energy transition.

Dr. Mohammadali Ahmadi
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. Energies 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 2600 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

  • artificial intelligence
  • machine learning
  • oil and gas industry
  • sustainability
  • energy transition
  • predictive maintenance
  • environmental impact
  • resource optimization
  • data analytics
  • renewable energy integration

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

Published Papers (3 papers)

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Research

40 pages, 6523 KiB  
Article
Study on Energy Efficiency and Maintenance Optimization of Run-Out Table in Hot Rolling Mills Using Long Short-Term Memory-Autoencoders
by Ju-Woong Yun, So-Won Choi and Eul-Bum Lee
Energies 2025, 18(9), 2295; https://doi.org/10.3390/en18092295 - 30 Apr 2025
Viewed by 372
Abstract
The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This [...] Read more.
The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This study aims to develop a PdM model for the Run-Out Table (ROT) equipment in hot rolling mills of steel plants, utilizing artificial intelligence (AI) technology, and to propose methods for contributing to energy efficiency through this model. Considering the operational data characteristics of the ROT equipment, an autoencoder (AE), capable of detecting anomalies using only normal data, was selected as the base model. Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. Additionally, the concept of an anomaly ratio was applied to identify equipment anomalies for each coil production. The performance evaluation of the ROT-PMM demonstrated an F1-score of 91%. This study differentiates itself by developing an optimized model that considers the specific environment and large-scale equipment operation of steel plants, and by enhancing its applicability through performance verification using actual failure data. Furthermore, it emphasizes the importance of PdM strategies in contributing to energy efficiency. It is expected that this research will contribute to increased energy efficiency and productivity in industrial settings, including the steel industry. Full article
(This article belongs to the Special Issue Artificial Intelligence for a Sustainable Oil and Gas Industry and Energy Transition)
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31 pages, 7343 KiB  
Article
Exploration of Training Strategies for a Quantile Regression Deep Neural Network for the Prediction of the Rate of Penetration in a Multi-Lateral Well
by Adrian Ambrus, Felix James Pacis, Sergey Alyaev, Rasool Khosravanian and Tron Golder Kristiansen
Energies 2025, 18(6), 1553; https://doi.org/10.3390/en18061553 - 20 Mar 2025
Viewed by 387
Abstract
In recent years, rate of penetration (ROP) prediction using machine learning has attracted considerable interest. However, few studies have addressed ROP prediction uncertainty and its relation to training data and model inputs. This paper presents the application of a quantile regression deep neural [...] Read more.
In recent years, rate of penetration (ROP) prediction using machine learning has attracted considerable interest. However, few studies have addressed ROP prediction uncertainty and its relation to training data and model inputs. This paper presents the application of a quantile regression deep neural network (QRDNN) for ROP prediction on multi-lateral wells drilled in the Alvheim field of the North Sea. The quantile regression framework allows the characterization of the prediction uncertainty, which can inform the end-user on whether the model predictions are reliable. Three different training strategies for the QRDNN model are investigated. The first strategy uses individual hole sections of the multi-lateral well to train the model, which is then tested on sections of similar hole size. In the second strategy, the models are trained for specific formations encountered in the well, assuming the formation tops are known for both the training and test sections. The third strategy uses training data from offset wells from the same field as the multi-lateral well, exploring different offset–well combinations and input features. The resulting QRDNN models are tested on several complete well sections excluded from the training data, each several kilometers long. The second and third strategies give the lowest mean absolute percentage errors of their median predictions of 27.3% and 28.7% respectively—all without recalibration for the unknown test well sections. Furthermore, the third model based on offset training gives a robust prediction of uncertainty with over 99.6% of actual values within the predicted P10 and P90 percentiles. Full article
(This article belongs to the Special Issue Artificial Intelligence for a Sustainable Oil and Gas Industry and Energy Transition)
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14 pages, 1554 KiB  
Article
Multi-Modal Machine Learning to Predict the Energy Discharge Levels from a Multi-Cell Mechanical Draft Cooling Tower
by Christopher Sobecki, Larry Deschaine and Brian d’Entremont
Energies 2024, 17(17), 4385; https://doi.org/10.3390/en17174385 - 2 Sep 2024
Viewed by 1135
Abstract
An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model [...] Read more.
An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about 5%, but individual comparisons resulted in an R2 of 0.36. Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to R2>0.95. This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations. Full article
(This article belongs to the Special Issue Artificial Intelligence for a Sustainable Oil and Gas Industry and Energy Transition)
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