Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone

The increasing global demand for energy necessitates exploring and developing low-quality prospects, e.g., low-permeability reservoirs, which contain substantial hydrocarbon resources and have the potential to fill the gap in the energy markets. Typically, modeling fluid flow using reservoir simulat...

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Main Authors: Saafan, M., Mohyaldinn, M., Elraies, K.
Format: Article
Published: Elsevier B.V. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34135/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143690237&doi=10.1016%2fj.petrol.2022.111297&partnerID=40&md5=89908de2abceff76500655203eb7cd49
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spelling oai:scholars.utp.edu.my:341352023-01-04T02:45:55Z http://scholars.utp.edu.my/id/eprint/34135/ Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone Saafan, M. Mohyaldinn, M. Elraies, K. The increasing global demand for energy necessitates exploring and developing low-quality prospects, e.g., low-permeability reservoirs, which contain substantial hydrocarbon resources and have the potential to fill the gap in the energy markets. Typically, modeling fluid flow using reservoir simulators requires capillary pressure curves as an input. Nonetheless, laboratory capillary pressure measurements in low-permeability samples are time-consuming and challenging. On the contrary, resistivity measurements are easier to perform in the laboratory and offer a different prospect for obtaining capillary pressure curves. This paper proposes a new approach for obtaining capillary pressure curves from resistivity measurement in low-permeability sandstone using fractal theory and genetic algorithm. First, the fractal pore system is characterized as tortuous square and triangular capillaries to account for angular pores. Afterward, the drainage process is simulated to develop an innovative electrical resistivity model in fully and partially saturated porous media. Next, the genetic algorithm matches laboratory-measured resistivity data and obtains the developed model parameters. Afterward, the matched parameters are adopted in the drainage capillary pressure model to generate capillary pressure curves. The proposed model's reliability is verified by analyzing the prediction results of eighteen sandstone core samples. Furthermore, the developed model performance is compared with different models from the literature, and the results indicated its superiority in predicting capillary pressure curves. © 2022 Elsevier B.V. Elsevier B.V. 2023 Article NonPeerReviewed Saafan, M. and Mohyaldinn, M. and Elraies, K. (2023) Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone. Journal of Petroleum Science and Engineering, 221. ISSN 09204105 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143690237&doi=10.1016%2fj.petrol.2022.111297&partnerID=40&md5=89908de2abceff76500655203eb7cd49 10.1016/j.petrol.2022.111297 10.1016/j.petrol.2022.111297 10.1016/j.petrol.2022.111297
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The increasing global demand for energy necessitates exploring and developing low-quality prospects, e.g., low-permeability reservoirs, which contain substantial hydrocarbon resources and have the potential to fill the gap in the energy markets. Typically, modeling fluid flow using reservoir simulators requires capillary pressure curves as an input. Nonetheless, laboratory capillary pressure measurements in low-permeability samples are time-consuming and challenging. On the contrary, resistivity measurements are easier to perform in the laboratory and offer a different prospect for obtaining capillary pressure curves. This paper proposes a new approach for obtaining capillary pressure curves from resistivity measurement in low-permeability sandstone using fractal theory and genetic algorithm. First, the fractal pore system is characterized as tortuous square and triangular capillaries to account for angular pores. Afterward, the drainage process is simulated to develop an innovative electrical resistivity model in fully and partially saturated porous media. Next, the genetic algorithm matches laboratory-measured resistivity data and obtains the developed model parameters. Afterward, the matched parameters are adopted in the drainage capillary pressure model to generate capillary pressure curves. The proposed model's reliability is verified by analyzing the prediction results of eighteen sandstone core samples. Furthermore, the developed model performance is compared with different models from the literature, and the results indicated its superiority in predicting capillary pressure curves. © 2022 Elsevier B.V.
format Article
author Saafan, M.
Mohyaldinn, M.
Elraies, K.
spellingShingle Saafan, M.
Mohyaldinn, M.
Elraies, K.
Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
author_facet Saafan, M.
Mohyaldinn, M.
Elraies, K.
author_sort Saafan, M.
title Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
title_short Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
title_full Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
title_fullStr Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
title_full_unstemmed Obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
title_sort obtaining capillary pressure curves from resistivity measurements in low-permeability sandstone
publisher Elsevier B.V.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34135/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143690237&doi=10.1016%2fj.petrol.2022.111297&partnerID=40&md5=89908de2abceff76500655203eb7cd49
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score 13.222552