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Applications of Artificial Intelligence in Petroleum Geology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 816

Special Issue Editor

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is “Applications of Artificial Intelligence in Petroleum Geology”.

In recent decades, Artificial Intelligence (AI) has been broadly adopted, and considerable progress has been made in the field of petroleum geology. However, challenges may arise when using AI to solve petroleum geology problems, such as how to analyze the data and how to choose the appropriate AI methods. This Special Issue invites authors to submit original articles dedicated to innovative and advanced applications of AI methods in petroleum geology including, but not limited to, the following issues:

  • Applications of AI in reservoir characterization;
  • Applications of AI in CO2 storage;
  • Applications of AI in oil and gas production;
  • Applications of AI in multiphase flow;
  • Applications of AI in high water cut reservoirs;
  • Applications of AI in enhancing simulations;
  • Other applications of AI in petroleum geology.

Dr. Jianchun Xu
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.

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Keywords

  • artificial intelligence
  • reservoir characterization
  • oil and gas production
  • CO2 storage
  • petroleum geology

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Published Papers (1 paper)

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Research

19 pages, 5125 KiB  
Article
OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
by Tianyong Liu, Chengwu Xu, Lu Tang, Yingjie Meng, Weijia Xu, Jinhuan Wang and Jian Xu
Appl. Sci. 2024, 14(7), 2779; https://doi.org/10.3390/app14072779 - 26 Mar 2024
Viewed by 590
Abstract
With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this [...] Read more.
With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Petroleum Geology)
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