Shi, W. orcid.org/0000-0001-6056-3583, Yin, G., Wang, M. orcid.org/0000-0003-0941-8481 et al. (6 more authors) (2023) Progress of Electrical Resistance Tomography Application in Oil and Gas Reservoirs for Development Dynamic Monitoring. Processes, 11 (10). 2950. ISSN 2227-9717
Abstract
Petroleum engineers need real-time understanding of the dynamic information of reservoirs and production in the development process, which is essential for the fine description of oil and gas reservoirs. Due to the non-invasive feature of electromagnetic waves, more and more oil and gas reservoirs have received attention to capture the development dynamics with electrical resistance tomography (ERT). By measuring the distribution of resistivity on the surface, the ERT can offer information on the subsurface media. The theory and foundation of the ERT technology are presented in this study in the context of monitoring oil and gas reservoir growth dynamics. The characteristics of ERT technology are analyzed, and the progress of ERT application in the development of monitoring dynamics in terms of residual oil distribution, detection of water-driven leading edge, and monitoring of fractures during hydraulic fracturing is reviewed, as well as the progress of ERT technology optimization, including forward and inverse algorithms. This review aims to promote further application of ERT in the field of reservoir dynamics monitoring because of its important engineering significance as well as its academic value in terms of improving production efficiency and reducing risk.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | tomography; residual oil distribution; waterflooding front; hydraulic fracture; inverse algorithms |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jan 2024 15:29 |
Last Modified: | 29 Jan 2024 15:29 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/pr11102950 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208354 |