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New Advances in Low-Energy Processes for Geo-Energy Development
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New Advances in Low-Energy Processes for Geo-Energy Development

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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 2696

Special Issue Editors

Dr. Daoyi Zhu

E-Mail Website
Guest Editor
Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Interests: oilfield chemistry; plugging theory and technology; low-energy processes for oil and gas recovery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yiqun Zhang

E-Mail Website
Guest Editor
College of Safety and Ocean Engineering, China University of Petroleum-Beijing, Beijing 102249, China
Interests: offshore oil and gas engineering; drilling and completion engineering; well testing; natural gas hydrates; geothermal
Dr. Xiankang Xin

E-Mail Website
Guest Editor
School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Interests: reservoir engineering; numerical reservoir simulation; enhanced oil recovery
Dr. Shuda Zhao

E-Mail Website
Guest Editor
GGPE, Missouri University of Science and Technology, Rolla, MO 65401, USA
Interests: polymer gel; conformance control; enhanced oil recovery
Dr. Hongbin Yang

E-Mail Website
Guest Editor
College of Petroleum Engineering, China University of Petroleum (Huadong), Qingdao 266580, China
Interests: functional gelling control agent; low-cost and high-efficiency chemical flooding system; production fluid treatment; temperature-resistant cleaning fracturing fluid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The trajectory of industrialisation is tightly correlated with geo-energy. Academics place a high value on all kinds of cutting-edge studies. Every breakthrough, whether theoretical or in engineering, has the potential to significantly advance society. This Special Issue is seeking frontier and innovative research on the low-energy development of geo-energy resources. Several research studies are presently being conducted on EOR flooding materials, such as polymer, CO2, air, steam and composite methods. Additionally, the study of some low-energy and promising heating reservoir technologies has also been gradually increasing, such as nuclear energy, solar energy, in situ upgrading, and electromagnetic heating. Of course, with the continuous innovations in computer and information technology, numerical simulation technology and big data analysis methods also play pivotal roles in the development of high-efficiency and low-energy geological energy.

This Special Issue aims to present and disseminate the most recent advances related to the new advances in low-energy processes for geo-energy development.

Topics of interest for publication include, but are not limited to:

  • Intelligent well technologies
  • New technologies in ROP improvement
  • New technologies in cold production
  • New technologies in waterflooding for geo-energy resources development
  • New technologies in polymer flooding
  • New technologies in emulsion flooding
  • New technologies in enhanced CO2 injection
  • New technologies in enhanced air injection
  • New technologies in enhanced steam injection
  • New technologies in heating geo-energy reservoirs
  • New technologies in geo-energy reservoir simulation
  • Low-energy processes for shale oil recovery
  • Low-energy processes for tight oil recovery

Dr. Daoyi Zhu
Prof. Dr. Yiqun Zhang
Dr. Xiankang Xin
Dr. Shuda Zhao
Dr. Hongbin Yang
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

  • low-energy processes
  • intelligent well technologies
  • cold production
  • cold-enhanced geo-energy recovery
  • thermal-enhanced geo-energy recovery
  • enhanced gas injection
  • enhanced steam injection
  • reservoir simulation of Geo-energy
  • shale oil
  • tight oil

Published Papers (2 papers)

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Research

22 pages, 75937 KiB  
Article
A Practical Model for Gas–Water Two-Phase Flow and Fracture Parameter Estimation in Shale
by Pin Jia, Langyu Niu, Yang Li and Haoran Feng
Energies 2023, 16(13), 5140; https://doi.org/10.3390/en16135140 - 03 Jul 2023
Viewed by 777
Abstract
The gas flow in shale reservoirs is controlled by gas desorption diffusion and multiple flow mechanisms in the shale matrix. The treatment of hydraulic fracturing injects a large amount of fracturing fluids into shale reservoirs, and the fracturing fluids can only be recovered [...] Read more.
The gas flow in shale reservoirs is controlled by gas desorption diffusion and multiple flow mechanisms in the shale matrix. The treatment of hydraulic fracturing injects a large amount of fracturing fluids into shale reservoirs, and the fracturing fluids can only be recovered by 30~70%. The remaining fracturing fluid invades the reservoir in the form of a water invasion layer. In this paper, by introducing the concept of a water invasion layer, the hydraulic fracture network is di-vided into three zones: major fracture, water invasion layer and stimulated reservoir volume (SRV). The mathematical model considering gas desorption, the water invasion layer and gas–water two-phase flow in a major fracture is established in the Laplace domain, and the semi-analytical solution method is developed. The new model is validated by a commercial simulator. A field case from WY shale gas reservoir in southwestern China is used to verify the utility of the model. Several key parameters of major fracture and SRV are interpreted. The gas–water two-phase flow model established in this paper provides theoretical guidance for fracturing effectiveness evaluation and an efficient development strategy of shale gas reservoirs. Full article
(This article belongs to the Special Issue New Advances in Low-Energy Processes for Geo-Energy Development)
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16 pages, 7653 KiB  
Article
A Hybrid Oil Production Prediction Model Based on Artificial Intelligence Technology
by Xiangming Kong, Yuetian Liu, Liang Xue, Guanlin Li and Dongdong Zhu
Energies 2023, 16(3), 1027; https://doi.org/10.3390/en16031027 - 17 Jan 2023
Cited by 3 | Viewed by 1475
Abstract
Oil production prediction plays a significant role in designing programs for hydrocarbon reservoir development, adjusting production operations and making decisions. The prediction accuracy of oil production based on single methods is limited since more and more unconventional reservoirs are being exploited. Artificial intelligence [...] Read more.
Oil production prediction plays a significant role in designing programs for hydrocarbon reservoir development, adjusting production operations and making decisions. The prediction accuracy of oil production based on single methods is limited since more and more unconventional reservoirs are being exploited. Artificial intelligence technology and data decomposition are widely implemented in multi-step forecasting strategies. In this study, a hybrid prediction model was proposed based on two-stage decomposition, sample entropy reconstruction and long short-term memory neural network (LSTM) forecasts. The original oil production data were decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); then these IMFs with different sample entropy (SE) values were reconstructed based on subsequence reconstruction rules that determine the appropriate reconstruction numbers and modes. Following that, the highest-frequency reconstructed IMF was preferred to be decomposed again by variational mode decomposition (VMD), and subsequences of the secondary decomposition and the remaining reconstructed IMFs were fed into the corresponding LSTM predictors based on a hybrid architecture for forecasting. Finally, the prediction values of each subseries were integrated to achieve the result. The proposed model makes predictions for the well production rate of the JinLong volcanic reservoir, and comparative experiments show that it has higher forecasting accuracy than other methods, making it recognized as a potential approach for evaluating reservoirs and guiding oilfield management. Full article
(This article belongs to the Special Issue New Advances in Low-Energy Processes for Geo-Energy Development)
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