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Hydraulic Fracturing and Reservoir Simulation Modeling in Shale, Tight, and Ultra-Low Permeability Gas Reservoirs

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

Deadline for manuscript submissions: 20 February 2025 | Viewed by 75

Special Issue Editors


E-Mail Website
Guest Editor
Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Interests: numerical method; reservoir simulation; machine learning; flow and transport in porous media

E-Mail Website
Guest Editor
Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Interests: shale gas; hydraulic fracturing; unconventional energy; mathematical modeling; porous media

Special Issue Information

Dear Colleagues,

With advancements in primary energy resource extraction technology, the production of natural gas from shale, tight, and ultra-low permeability reservoirs has significantly increased over the past two decades. This has led to an abundant natural gas supply, positively impacting availability and consumer prices, while also providing environmental benefits through cleaner energy sources. However, the production of gas from such reservoirs strongly relies on hydraulic fracturing techniques to achieve the most economic production, introducing more challenges, uncertainties, and environmental concerns. Considering the recent technological improvements such as proppant technology, fracturing fluids, geosteering and monitoring, as well as data science, artificial intelligence (AI), and machine learning (ML) technologies, this Special Issue aims to disseminate the most recent advances in hydraulic fracturing technology and shale gas reservoir modeling.

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

  • Analytical and numerical modeling for hydraulic fracturing simulation and optimization;
  • Fracture characterization and monitoring (e.g., microseismic and fiber-optic enabled monitoring);
  • Refracturing and infill well fracturing;
  • Imbibition and flow back of fracturing fluid;
  • Mathematical modeling of complex fracture networks;
  • Numerical simulation and optimization of shale gas reservoirs;
  • Uncertainty quantification and risk analysis;
  • Machine Learning applications and data-driven models;
  • Geomechanical and geochemical aspects and impacts;
  • Economics, safety, and environmental considerations.

Dr. Yin Feng
Prof. Dr. Boyun Guo
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. 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

  • ultra-low permeability reservoirs
  • hydraulic fracturing
  • machine learning
  • fracture network
  • fracture fluids

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Published Papers

This special issue is now open for submission.
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