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A new look into the prediction of static Young's modulus and unconfined compressive strength of carbonate using artificial intelligence tools

View ORCID ProfileZeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, View ORCID ProfileSalaheldin Elkatatny, Abdulwahab Z. Ali, Dhafer Al-Shehri and Mandefro W. A. Belayneh
Petroleum Geoscience, 25, 389-399, 21 October 2019, https://doi.org/10.1144/petgeo2018-126
Zeeshan Tariq
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Abdulazeez Abdulraheem
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Mohamed Mahmoud
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Salaheldin Elkatatny
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Abdulwahab Z. Ali
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Dhafer Al-Shehri
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Mandefro W. A. Belayneh
2Reservoir Characterization Department, Saudi Aramco, PO Box 11795, , Saudi Arabia
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Abstract

Accurate estimation of rock elastic and failure parameters plays a vital role in petroleum, civil and geotechnical engineering applications. During drilling operations, continuous logs of rock elastic and failure parameters are considered very helpful in optimizing geomechanical earth models. Commonly, rock elastic and failure parameters are estimated using well logs and empirical correlations. These are calibrated with rock mechanics laboratory experiments conducted on core samples. However, since these samples are expensive to get and time-consuming to test, artificial intelligence (AI) models based on available petrophysical well logs such as bulk density, compressional wave and shear wave travel times are utilized to predict the static Young's modulus (Estatic) and the unconfined compressive strength (UCS) – with an emphasis on carbonate rocks. We present two AI techniques in this study: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The dataset used in this study contains 120 data points obtained from a Middle Eastern carbonate reservoir from which we develop an empirically correlated ANN model to predict Estatic and an ANFIS model to predict the UCS. A comparison between the UCS, predicted by the proposed ANFIS model, and the published correlations show that the ANFIS model predicted the UCS with less error and with a high coefficient of determination. The error obtained from the ANFIS model was 4.5%, while other correlations resulted in up to 30% error on a published dataset. On the basis of the results obtained, we can say that the developed models will help geomechanical engineers to predict Estatic and the UCS using well logs without the need to measure them in the laboratory.

Thematic collection: This article is part of the Naturally Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/naturally-fractured-reservoirs

  • © 2019 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights reserved
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Petroleum Geoscience: 25 (4)
Petroleum Geoscience
Volume 25, Issue 4
November 2019
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A new look into the prediction of static Young's modulus and unconfined compressive strength of carbonate using artificial intelligence tools

Zeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, Salaheldin Elkatatny, Abdulwahab Z. Ali, Dhafer Al-Shehri and Mandefro W. A. Belayneh
Petroleum Geoscience, 25, 389-399, 21 October 2019, https://doi.org/10.1144/petgeo2018-126
Zeeshan Tariq
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zeeshan Tariq
Abdulazeez Abdulraheem
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Mohamed Mahmoud
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Salaheldin Elkatatny
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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  • Search for this author on this site
  • ORCID record for Salaheldin Elkatatny
Abdulwahab Z. Ali
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Dhafer Al-Shehri
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, , Saudi Arabia
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Mandefro W. A. Belayneh
2Reservoir Characterization Department, Saudi Aramco, PO Box 11795, , Saudi Arabia
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A new look into the prediction of static Young's modulus and unconfined compressive strength of carbonate using artificial intelligence tools

Zeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, Salaheldin Elkatatny, Abdulwahab Z. Ali, Dhafer Al-Shehri and Mandefro W. A. Belayneh
Petroleum Geoscience, 25, 389-399, 21 October 2019, https://doi.org/10.1144/petgeo2018-126
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  • Article
    • Abstract
    • Materials and methods
    • Results and discussion
    • Development of an empirical model using the ANN
    • Field validation
    • Comparison of the proposed UCS model prediction with commonly used correlations
    • Conclusions and recommendations
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