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Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin

Author

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  • Fatick Nath

    (Petroleum Engineering Program, Texas A&M International University, Laredo, TX 78041, USA)

  • Sarker Monojit Asish

    (School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA)

  • Deepak Ganta

    (Systems Engineering Program, Texas A&M International University, Laredo, TX 78041, USA)

  • Happy Rani Debi

    (School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA)

  • Gabriel Aguirre

    (Systems Engineering Program, Texas A&M International University, Laredo, TX 78041, USA)

  • Edgardo Aguirre

    (Systems Engineering Program, Texas A&M International University, Laredo, TX 78041, USA)

Abstract

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential.

Suggested Citation

  • Fatick Nath & Sarker Monojit Asish & Deepak Ganta & Happy Rani Debi & Gabriel Aguirre & Edgardo Aguirre, 2022. "Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin," Energies, MDPI, vol. 15(22), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8752-:d:979770
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    References listed on IDEAS

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    1. Ahmad Al-AbdulJabbar & Salaheldin Elkatatny & Ahmed Abdulhamid Mahmoud & Tamer Moussa & Dhafer Al-Shehri & Mahmoud Abughaban & Abdullah Al-Yami, 2020. "Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    2. Salaheldin Elkatatny, 2019. "Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    3. Abdulmalek Ahmed & Abdulwahab Ali & Salaheldin Elkatatny & Abdulazeez Abdulraheem, 2019. "New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    4. Salaheldin Elkatatny & Tamer Moussa & Abdulazeez Abdulraheem & Mohamed Mahmoud, 2018. "A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties," Energies, MDPI, vol. 11(12), pages 1-14, December.
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    Cited by:

    1. Fatick Nath & Gabriel Aguirre & Edgardo Aguirre, 2023. "Characterizing Complex Deformation, Damage, and Fracture in Heterogeneous Shale Using 3D-DIC," Energies, MDPI, vol. 16(6), pages 1-17, March.

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