Integration of Intelligent Technologies and Green Processes in Unconventional Reservoirs

Dear Colleagues,

The energy sector is undergoing a profound transformation driven by the dual challenges of meeting global energy demand and achieving climate goals. Unconventional reservoirs, such as shale and tight formations, remain crucial to the energy mix, but their development must become more efficient, economical, and environmentally sustainable. Concurrently, emerging energy carriers like hydrogen and carbon capture, utilization, and storage (CCUS) technologies are paving the way for a low-carbon future. The convergence of these fields with artificial intelligence (AI) and big data analytics presents unprecedented opportunities for innovation.

This Special Issue aims to explore the cutting-edge integration of intelligent computational methods with core energy processes, specifically targeting unconventional reservoirs, hydrogen systems, and CCUS. We seek to compile research that demonstrates how AI, machine learning, data-driven modeling, and advanced process integration can optimize operations, enhance predictive capabilities, reduce environmental footprints, and unlock new value streams.

Topics of interest include, but are not limited to, the following:

• AI and Machine Learning for Unconventional Reservoirs: application in reservoir characterization, production forecasting, well placement optimization, hydraulic fracturing design, and production data analysis.
• Data-Driven Modeling for Hydrogen Systems: AI-based models for hydrogen production (e.g., from natural gas with CCUS or electrolysis), storage (in geological formations including depleted reservoirs), transport, and safety management.
• Smart CCUS and Carbon Management: machine learning for site selection, monitoring, verification, and accounting (MVA) of CO2 storage; optimization of CO2-EOR processes; and lifecycle analysis.
• Digital Twins and Process Integration: development of digital twins for integrated energy systems, combining unconventional resource development with hydrogen production or CCUS operations.
• Sustainability and Techno-Economic Analysis: AI-assisted lifecycle assessment and techno-economic optimization of hybrid energy systems involving hydrocarbons, hydrogen, and carbon management.

We invite the submission of high-quality original research articles and comprehensive reviews that address these challenges and showcase novel solutions.

Dr. Yang Wang
Dr. Cao Wei
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.