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Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

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Abstract

Accurate prediction of pore-pressures in the subsurface is paramount for successful planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid drilling problems. As it is expensive and time-consuming to measure pore-pressure directly in wellbores, it is useful to be able to predict it from various petrophysical input variables on a supervised learning basis calibrated to a benchmark wellbore. This study developed and compared three-hybrid machine-learning optimization models applied to a diverse suite of 9 petrophysical input variables to predict pore-pressure across a 273-m-thick, predominately carbonate, reservoir sequence in the giant Marun oil field (Iran) using 1972 data records. The analysis identified that the multilayer extreme learning machine model hybridized with a particle swarm optimization (MELM–PSO) applied to seven input variables by feature selection provided the most accurate pore-pressure predictions for the full dataset (RMSE = 11.551 psi (1 psi = 6.8947590868 kPa) for well MN#281). The Savitzky–Golay (SG) filter was applied to pre-process the data, and the properties were filter-ranked using the wrapping method. The MELM–PSO model outperformed the pore-pressure prediction accuracy achieved by commonly used empirical formulas involving sonic or resistivity log data or calculated pore compressibility. To further verify and generalize the applicability of the MELM–PSO model, it was applied to two other Marun oil field wells (MN#297 and MN#378) achieving RMSE prediction accuracy of 10.031 psi and 10.150 psi, respectively. These results confirmed that the trained model can be reliably applied to multiple locations across the Marun oil field for predicting pore-pressure.

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Notes

  1. 1 inch = 0.0254 m.

  2. 1 cubic feet = 0.0283168466 cubic meter.

  3. 1 psi = 6.8947590868 kPa.

  4. 1 psi = 6.8947590868 kPa.

  5. 1 psi = 6.8947590868 kPa.

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Acknowledgment

This research was supported by Tomsk Polytechnic University under Grant Number VIU-CPPSND-214/2020.

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Authors and Affiliations

  1. Department of Petroleum Engineering, Faculty of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammad Farsi

  2. Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

    Nima Mohamadian

  3. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Hamzeh Ghorbani

  4. DWA Energy Limited, Lincoln, LN5 9JP, UK

    David A. Wood

  5. School of Earth Sciences and Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia

    Shadfar Davoodi

  6. Petroleum Engineering Department, Petroleum Industry University, Ahvaz, Iran

    Jamshid Moghadasi

  7. Faculty of Engineering, Department of Computer Engineering, Shahid Chamran University, Ahwaz, Iran

    Mehdi Ahmadi Alvar

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  1. Mohammad Farsi
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  2. Nima Mohamadian
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Correspondence to David A. Wood.

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11053_2021_9852_MOESM1_ESM.xlsx

Supplementary file 1 An Excel file containing the ten variable values for the1792 data records evaluated in this study is available to download as a supplementary file. (XLSX 233 kb)

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Farsi, M., Mohamadian, N., Ghorbani, H. et al. Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms. Nat Resour Res 30, 3455–3481 (2021). https://doi.org/10.1007/s11053-021-09852-2

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