Reservoir Geomechanics: A Data-driven Approach

Izuchukwu Josephmartin Korie *

Department of Geological Science, Nnamdi Azikiwe University, Awka, Nigeria.

Chudi-Ajabor Ogochukwu

Department of Geological Science, Nnamdi Azikiwe University, Awka, Nigeria.

Onwuagba Kenechi Innocent

Department of Geological Science, Nnamdi Azikiwe University, Awka, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

Reservoir geomechanics is a crucial aspect of optimising and developing oil and gas activities, especially in maximising production. Recent technological advancements have revolutionised reservoir geomechanics studies, including integrating data-driven approaches. This review examines and integrates machine learning, data science, and data twin in reservoir studies. The primary aim is to identify the benefits, limitations, significant advancements, potential challenges, opportunities, and research gaps of data-driven approaches to reservoir geomechanics. Additionally, this study aims to create opportunities for further research to address these challenges. The review identifies cost-effectiveness, improved reservoir characterisation, and reduced operational risks as the benefits of integrating data-driven approaches in reservoir geomechanics. However, the review also highlights the significant challenges of data-driven approaches, such as insufficient datasets, limited interpretability, and limited transferability of models. By shedding light on these issues, this review provides a foundation for future research toward finding solutions to these challenges.

Keywords: Reservoir, geomechanics, machine learning, data science, digital twin


How to Cite

Korie , Izuchukwu Josephmartin, Chudi-Ajabor Ogochukwu, and Onwuagba Kenechi Innocent. 2023. “Reservoir Geomechanics: A Data-Driven Approach”. Journal of Energy Research and Reviews 15 (2):47-56. https://doi.org/10.9734/jenrr/2023/v15i2306.

Downloads

Download data is not yet available.

References

Joshua Pwavodi, Ibekwe N Kelechi, Perekebina Angalabiri, Sharon Chioma Emeremgini, Vivian O Oguadinma. Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality, Energy Geoscience. 2023;4(3):20-23.

Oguadinma, et al. Lithofacies and Textural Attributes of the Nanka Sandstone (Eocene): Proxies for evaluating the Depositional Environment and Reservoir Quality. Journal of Earth Sciences and Geotechnical Engineering. 2014;4(4). 1-16ISSN: 1792-9040 (print), 1792-9660. DOI: 10.13140/RG.2.2.33124.07042

Oguadinma, et al. Study of the Pleistocene submarine canyons of the south-eastern Niger delta basin: Tectonostratigraphic evolution and infilling Conference/Reunion des sciences de la terre, Lyon, France; 2021.

Oguadinma, et al. The art of integration: A basic tool in effective hydrocarbon field appraisal, Med-GU Conference, Istanbul. Turkey; 2021.

Nwaezeapu, et al. Hydrocarbon Reservoir Evaluation: a case study of Tymot field at southwestern offshore Niger Delta Oil Province, Nigeria. Nanoscience and Nanotechnology. 2018;2(2). DOI: http://dx.doi.org/10.18063/nn.v0i0.618

Oguadinma, et al. An integrated approach to hydrocarbon prospect evaluation of the Vin field, Nova Scotia Basin. S.E.G. Technical Program Expanded Abstracts; 2016. DOI: 10.1190/segam2016- 13843545.1

Oguadinma, et al. Advanced Study of Seismic and Well Logs in the Hydrocarbon Prospectivity of Siram Field, Niger Delta Basin. Geological Society of America Abstracts with Programs. 2017;49.

DOI: 10.1130/abs/2017AM-296312

Oguadinma O Vivian, Ibekwe N Kelechi, Lanisa Ademola, et al. Reservoir and sequence stratigraphic analysis using subsurface data. ESS Open Archive; February 09, 2023.

Oguadinma O Vivian, Ibekwe N Kelechi, Lanisa Ademola, et al. Submarine canyon: A brief review. ESS Open Archive; February 09, 2023

Denney D. Coupled Reservoir-Geomechanics Model for Wellbore Stability and Sand Prediction. Society of Petroleum Engineers; 2001. DOI: 10.2118/0501-0062-JPT

Ibekwe, et al. Enhanced hydrocarbon recovery using the application of seismic attributes in fault detection and direct hydrocarbon indicator in Tomboy Field, western-Offshore Niger Delta Basin. ESS Open Archive; January 24, 2023.

Ibekwe KN, Oguadinma VO, Okoro VK, Aniwetalu E, Lanisa A, Ahaneku CV. Reservoir Characterization Review in Sedimentary Basins. Journal of Energy Research and Reviews. 2023;13(2):20–28.

Available:https://doi.org/10.9734/jenrr/2023/v13i2259

Morgenroth J, Khan UT, Perras MA. An overview of opportunities for machine learning methods in underground rock engineering design. Geosciences. 2019; 9(12):504.

DOI: 10.3390/geosciences9120504

Aniwetalu, et al. Spectral analysis of Rayleigh waves in Southeastern part of Niger delta, Nigeria. International Journal and Advance Geosciences. 2018;6:51-56. Available:http://dx.doi.org/10.14419/ijag.v6i1.8776

Fahad IS, Abdulla A, Amirmasoud KD, Neghabhan S. Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs: A systematic literature review. Petroleum. 2022;8:158-166.

Zhou YX, Wu XP. Use of neural networks in the analysis and interpretation of site investigation data. Comput. Geotechnics. 1994;16:105–122.

DOI: 10.1016/0266-352x(94)90017-5.

Diksha S, Neeraj K. A review on machine learning algorithms, tasks and applications. International Journal of Advanced Research in Computer Engineering & Technology. 2017;6(10):1550-1551.

Zoback MD, Gorelick SM. Earthquake triggering and large-scale geologic storage of carbon dioxide. Proceedings of the National Academy of Sciences. 2012; 109(26):10164-10168.

Khatibi S, Aghajanpour A. Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field. Energies. 2020;13(14): 35-38. Available:https://doi.org/10.3390/en13143528

Rahman SS, Haque A, Islam MR. Integrating geomechanics into reservoir simulation for improved performance prediction. Journal of Petroleum Science and Engineering. 2017;155:330-340.

Wang J, Li X, Wang H, Xu Y, Sun Y, Liu H. Integrating geomechanics into reservoir simulation: A review. Journal of Petroleum Science and Engineering. 2020;190: 107102.

Maxwell SC, Urbancic TI, Zoback MD, Das I. Reservoir geomechanics and hydraulic fracturing: From reservoir characterization to full-field modeling. SPE Journal. 2015; 20(3):549-561.

Zhang C, Huang W, Liu X. A data-driven approach to optimizing water injection strategy in heterogeneous reservoirs. Journal of Petroleum Science and Engineering. 2019;176:609-622.

Song L, Zhang H, Bai B, Shi C. A data-driven approach for predicting rock mechanical properties in shale reservoirs. Journal of Petroleum Science and Engineering. 2019;175:408-421.

Wang K, Gao H, Wang H, Gao Y. Data-driven production optimization based on a hybrid neural network model. Journal of Petroleum Science and Engineering. 2020;193:107350.

Acar E, Unler A. Machine Learning for Reservoir Characterization: A Review. Computers & Geosciences. 2020;135: 104398.

Guo Z, Li H, Li L, Li X, Li G. Digital twin-based reservoir geomechanics simulation and application in oilfield development. Journal of Petroleum Science and Engineering. 2021;196:108017.

Koehrsen W. The Limits and Ethical Considerations of Machine Learning. Medium; 2018.

Available:https://medium.com/@williamkoehrsen/the-limits-and-ethical-considerations-of-machine-learning-1c914b1c869e

Wang X, Zhang Y, Ma J. A Review of data-driven approaches for reservoir characterization and Modeling. Journal of Petroleum Science and Engineering. 2020; 188:106842.

Tariq Z, Aljawad MS, Hasan A, Murtaza M, Mohammed E, El-Husseiny A, et al. A systematic review of data science and machine learning applications to the oil and gas industry. Journal of Petroleum Exploration and Production Technology. 2021;11:4339–4374. Available:https://doi.org/10.1007/s13202-021-01302-2

Tariq Z, Elkatatny S, Mahmoud M, Ali AZ, Abdulraheem A. A new technique to develop rock strength correlation using artificial intelligence tools. In: Society of petroleum engineers—SPE reservoir characterization and simulation conference and exhibition, RCSC 2017. Society of Petroleum Engineers. 2017a;1340– 1353.

Available:https://doi.org/10.2118/186062-MS

Tariq Z, Elkatatny S, Mahmoud M, Ali AZ, Abdulraheem A. A new approach to predict failure parameters of carbonate rocks using artificial intelligence tools. In: Society of petroleum engineers—SPE Kingdom of Saudi Arabia annual technical symposium and exhibition 2017. Society of Petroleum Engineers. 2017b;1428–1440.

Available:https://doi.org/10.2118/187974-MS

Anifowose FA. Advances in hybrid computational intelligence application in oil and gas reservoir characterization. In: Society of petroleum engineers—SPE Saudi Arabia section young professional's technical symposium 2012, YPTS 2012. 2012;1–8. Available:https://doi.org/10.2118/160921-ms

Sun S, Zhang T. A 6M digital twin for modeling and simulation in subsurface reservoirs. Advances in Geo-Energy Research. 2020;4(4):349-351.

DOI: 10.46690/ager.2020.04.01.

Virginia D. Responsible artificial intelligence: designing AI for human values. ITU J. 2018;1(1):1–8.

Ramamoorthy A, Yampolskiy R. Beyond map: The race for artificial general Intelligence. ITU J. 2018;1(1):77–84.

Mohaghegh SD. Shale analytics: Data-driven analytics in unconventional resources. Springer International Publishing, Cham; 2017. Available:https://doi.org/10.1007/978-3-319-48753-3

Busetti S. Guest Editorial: Five innovation themes for integrated geomechanics technology. Journal of Petroleum Technology. 2019;71:14–15. DOI: https://doi.org/10.2118/1019-0014-JPT

Sircar A, Nair A, Bist N, Yadav K. Digital twin in hydrocarbon industry. Petroleum Research; 2022.

Available:https://doi.org/10.1016/j.ptlrs.2022.04.001

Chen Y, Zhao L, Pan J, Li C, Xu M, Li K, Zhang F, Geng J. Deep carbonate reservoir characterization using multi-seismic attributes via machine learning with physical constraints, Journal of Geophysics and Engineering. 2021;18(5): 761–775. DOI: https://doi.org/10.1093/jge/gxab049