Machine Learning Implications for Sand Management and Geomechanical Characterization: A Case Study in the Nahr Umr Formation, Southern Iraq

Ali M.  Ayal; Dhifaf J.  Sadeq; Dennis Delali Kwesi Wayo

doi:10.52716/jprs.v15i1.927

Authors

Ali M. Ayal Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
Dhifaf J. Sadeq Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
Dennis Delali Kwesi Wayo Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan, and Universiti Malaysia Pahang Al-Sultan Abdullah, Faculty of Chemical and Process Engineering Technology, Kuantan, Malaysia.

DOI:

https://doi.org/10.52716/jprs.v15i1.927

Keywords:

Sand production, CDDP, Sand Management Model, Machine Learning, Wellbore Stability.

Abstract

Sand production is one of the major challenges in the oil and gas industry, so comprehensive geomechanical analysis is necessary to mitigate sand production in mature fields. The absence of crucial well logs is an essential challenge in the oil and gas industry, necessitating geologists and engineers to rely on empirical equations to predict the absence of log intervals.

A comprehensive study was carried out on geomechanical modeling using data logs from two wells located in the Nahr Umr formation in Southern Iraq. Furthermore, the geomechanical parameters used by the predictive model were verified through caliper measurements. A machine learning technique was employed to predict the absence of acoustic log in Well-5 instead of using empirical equations. Additionally, two sand management models were developed and compared - one using the empirical Gardner equation and the other employing machine learning techniques.

The sand management model based on the Gardner equation predicted the production of sand from the beginning. However, it did not match the actual production data observed in real life. On the other hand, the machine learning-based model indicated no probability of sand generation, which aligned with the observed production data. The findings of this study demonstrate the advantages of using machine learning over traditional empirical equations for geomechanical studies in the particular area under study. Also, these findings suggest that machine-learning techniques might be applied to more basins in southern Iraq. The current research improves our understanding of the impact of machine learning on sand management as well as geomechanical characterization. This study has the potential to enhance procedures for making decisions in the petroleum and natural gas industries and contribute valuable knowledge to improved ways of handling sand production problems.

References

M. D. Zoback, Reservoirs geomechanics, Department of Geophysics, Stanford University, 2007.

A. K. Abbas, A. Manhalawi, U. Alameedy, and R. E. Flori, "Using an Analytical Model to Predict Collapse Volume During Drilling: A Case Study from Southern Iraq," in 53rd U.S. Rock Mechanics/Geomechanics Symposium, 2019, vol. All Days, ARMA-2019-1525.

A. M. Ayal, D. J. Sadeq, and F. S. Alakbari, "Mechanical earth model coupled with critical drawdown pressure to mitigate sand production in the Nahr Umr Formation, Southern Iraq," Iraqi Journal of Chemical and Petroleum Engineering, vol. 25, no. 4, pp. 93-106, 2024, doi: https://doi.org/10.31699/IJCPE.2024.4.9

A. E. Radwan, "Modeling pore pressure and fracture pressure using integrated well logging, drilling based interpretations and reservoir data in the Giant El Morgan oil Field, Gulf of Suez, Egypt," Journal of African Earth Sciences, vol. 178, p. 104165, 2021. DOI: https://doi.org/10.1016/j.jafrearsci.2021.104165

A. E. Radwan and S. Sen, "Characterization of in-situ stresses and its implications for production and reservoir stability in the depleted El Morgan hydrocarbon field, Gulf of Suez Rift Basin, Egypt," Journal of Structural Geology, vol. 148, p. 104355, 2021. DOI: https://doi.org/10.1016/j.jsg.2021.104355

V. Alipour Tabrizy and Y. Mirzaahmadian, "Investigation of Sand Production Onset: A New Approach Based on Petrophysical Logs," in SPE International Conference and Exhibition on Formation Damage Control, 2012: SPE, pp. SPE-150529-MS. https://doi.org/10.2118/150529-MS.

A. M. Ayal and D. J. Sadeq, "Prediction of Sand Production from Poorly Consolidated Formations Using Artificial Neural Network: A Case Study, Nahr Umr Formation in Subba Oil Field," The Iraqi Geological Journal, vol. 57, no. 2C, pp. 123-138, 2024, doi: https://doi.org/10.46717/igj.57.2C.10ms-2024-9-18

S. K. Subbiah, A. Samsuri, A. Mohamad-Hussein, M. Z. Jaafar, Y. R. Chen, and R. R. Kumar, "Root cause of sand production and methodologies for prediction," Petroleum, vol. 7, no. 3, pp. 263-271, 2021. DOI: https://doi.org/10.1016/j.petlm.2020.09.007.

E. Khamehchi and E. Reisi, "Sand production prediction using ratio of shear modulus to bulk compressibility (case study)," Egyptian Journal of Petroleum, vol. 24, no. 2, pp. 113-118, 2015. DOI: https://doi.org/10.1016/j.ejpe.2015.05.002.

M. Sanei, A. Ramezanzadeh, and M. R. Delavar, "Applied machine learning-based models for predicting the geomechanical parameters using logging data," Journal of Petroleum Exploration and Production Technology, vol. 13, no. 12, pp. 2363-2385, 2023. DOI: 10.56952/ARMA-2022-0522.

W. S. Iramina et al., "Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships," REM-International Engineering Journal, vol. 71, pp. 89-95, 2018. DOI: 10.1590/0370-44672017710097.

M. A. Al-Janabi, O. F. Al-Fatlawi, D. J. Sadiq, H. A. Mahmood, and M. A. Al-Juboori, "Numerical simulation of gas lift optimization using artificial intelligence for a middle eastern oil field," in Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 2021: SPE, p. D022S183R002, doi: https://doi.org/10.2118/207341-MS

H. A. Mahmood, O. F. Al-Fatlawi, M. A. Al-Janabi, D. J. Sadeq, Y. M. Al-Jumaah, and M. Khan, "Optimizing Well Placement with Genetic Algorithms: A Case Study," Journal of Petroleum Research and Studies, vol. 14, no. 4, pp. 37-51, 2024. doi: https://doi.org/10.52716/jprs.v14i4.895

H. A. Al-Musawi, H. N. Al-Saedi, A. Alaa, and O. F. Hasan, "Formulating new oil properties correlations using machine learning," in AIP Conference Proceedings, 2023, vol. 2809, no. 1: AIP Publishing, doi: https://doi.org/10.1063/5.0155900.

M. AlJuboori, M. Hossain, O. Al-Fatlawi, A. Kabir, and A. Radhi, "Numerical simulation of gas lift optimization using genetic algorithm for a Middle East oil field: feasibility study," in International Petroleum Technology Conference, Dhahran, Kingdom of Saudi Arabia, 2020: IPTC, p. D031S095R001, doi: https://doi.org/10.2523/IPTC-20254-MS

Z. A. Al-Sadi and D. J. Sadeq, "Genetic Algorithm-Based Well Placement Optimization: A Review of Studies," The Iraqi Geological Journal, vol. 56, no. 2F, pp. 246-264, 2023, doi: https://doi.org/10.46717/igj.56.2F.16ms-2023-12-22

E. Khamehchi, I. R. Kivi, and M. Akbari, "A novel approach to sand production prediction using artificial intelligence," Journal of Petroleum Science and Engineering, vol. 123, pp. 147-154, 2014. https://doi.org/10.1016/j.petrol.2014.07.033.

U. Alameedy, A. A. Alhaleem, A. Isah, A. Al-Yaseri, A. El-Husseiny, and M. Mahmoud, "Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir, Iraq," Journal of Applied Geophysics, vol. 205, p. 104760, 2022. https://doi.org/10.1016/j.jappgeo.2022.104760.

A. R. Ngwashi, D. O. Ogbe, and D. O. Udebhulu, "Evaluation of machine-learning tools for predicting sand production," in SPE Nigeria Annual International Conference and Exhibition, 2021: SPE, p. D031S016R001. https://doi.org/10.2118/207193-MS.

N. Mohamadian et al., "A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning," Journal of Petroleum Science and Engineering, vol. 196, p. 107811, 2021. DOI: 10.1016/j.petrol.2020.107811.

A. E. Radwan, D. A. Wood, and A. A. Radwan, "Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand," Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 6, pp. 1799-1809, 2022. https://doi.org/10.1016/j.jrmge.2022.01.012.

O. Salman, O. F. Hasan, and S. Al-Jawad, "Permeability Prediction in One of Iraqi Carbonate Reservoir Using Statistical, Hydraulic Flow Units, and ANN Methods," Iraqi Journal of Chemical and Petroleum Engineering, vol. 23, no. 4, pp. 17-24, 2022, doi: https://doi.org/10.31699/IJCPE.2022.4.3

O. F. Al-Fatlawi, "Numerical simulation for the reserve estimation and production optimization from tight gas reservoirs," Curtin University, 2018.

A. M. Abdulaziz, H. L. Abdulridha, A. S. A. Dahab, S. Alhussainy, and A. K. Abbas, "3D mechanical earth model for optimized wellbore stability, a case study from South of Iraq," Journal of Petroleum Exploration and Production Technology, vol. 11, no. 9, pp. 3409-3420, 2021. DOI: https://doi.org/10.1007/s13202-021-01255-6

M. Samnejad, F. Aminzadeh, and A. Bubshait, "Acoustoelastic Estimation of the In Situ Stresses from Sonic Logs for a Carbonate Reservoir," in SPE Western Regional Meeting, 2017, vol. Day 3 Tue, April 25, 2017, D031S007R001. https://doi.org/10.2118/185644-MS.

B. Wu et al., "Sand Production Prediction for A Mature Oil Field Offshore East Malaysia-A Case Study," in SPE Asia Pacific Oil and Gas Conference and Exhibition, 2010. DOI: https://doi.org/10.2118/133375-MS.

A. Qatari, "A Workflow to Estimate Critical Drawdown Pressure Utilizing 1-D Geomechanics Modelling," in Middle East Oil, Gas and Geosciences Show, 2023. DOI: https://doi.org/10.2118/213325-MS.

J.-F. Nauroy, Geomechanics applied to the petroleum industry. Editions Technip, 2011.

J. J. Zhang, Applied petroleum geomechanics, Gulf Professional Publishing Houston, TX, USA, 2019.

B. A. Eaton, "The equation for geopressure prediction from well logs," in SPE Annual Technical Conference and Exhibition?, 1975: SPE, pp. SPE-5544-MS, doi: https://doi.org/10.2118/5544-MS.

G. L. Bowers, "Pore pressure estimation from velocity data: Accounting for overpressure mechanisms besides undercompaction," SPE Drilling & Completion, vol. 10, no. 02, pp. 89-95, 1995, doi: https://doi.org/10.2118/27488-PA.

C. Chang, M. D. Zoback, and A. Khaksar, "Empirical relations between rock strength and physical properties in sedimentary rocks," Journal of Petroleum Science and Engineering, vol. 51, no. 3-4, pp. 223-237, 2006. DOI: https://doi.org/10.1016/j.petrol.2006.01.003.

H. M. Ghargan, O. Al-Fatlawi, and Y. Bashir, "Reservoir permeability prediction based artificial intelligence techniques," Iraqi Journal of Chemical and Petroleum Engineering, vol. 25, no. 4, pp. 49-60, 2024, doi: https://doi.org/10.31699/IJCPE.2024.4.5.