Analytical Modelling of Fault Seal Effectiveness in Hydrocarbon Reservoirs: A Multi-Criteria Decision Analysis and Analytical Hierarchy Process Approach
Article Sidebar
Main Article Content
Abstract
Faults in hydrocarbon reservoirs significantly influence fluid flow behavior, making the prediction of fault seal effectiveness critical in reservoir management. This study introduces the FAULT-SEAL Evaluation Model (FSEM), leveraging a Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) framework. By integrating geological, geophysical, and fluid-related factors, FSEM assigns weights to key parameters, emphasizing the dominant role of clay smears and gouge composition (global weight: 0.8417) in fault-sealing potential. The Shale Gouge Ratio (SGR) and clay content emerged as the most sensitive parameters, with changes of up to 30% impacting fault seal effectiveness by as much as 0.12, underscoring their importance in fluid migration control. Fault throw and offset, with a moderate weight of 0.6262, significantly influenced the juxtaposition of rock types and sealing capacity, while stress conditions, particularly pore pressure (weight: 0.2999), moderately affected seal integrity, highlighting the need to monitor in-situ stress regimes. Validation through sensitivity analysis confirmed the model’s robustness and reliability, with a Consistency Ratio (CR) of -0.0543, ensuring minimal inconsistency in the decision matrix. These findings underscore the critical role of clay-rich fault zones in hydrocarbon trapping and the importance of detailed fault rock characterization. The FSEM offers a scalable, data-driven tool capable of generating faster, more accurate fault seal predictions, advancing exploration and production strategies. By integrating machine learning techniques and decision frameworks, the model provides an innovative approach to optimizing hydrocarbon recovery in faulted reservoirs.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors hold the copyright of all published articles except otherwise stated.
References
Abdallah, W., Flori, R. E., & Ertekin, T. (2021). Data science and machine learning applications in reservoir engineering. Elsevier. DOI: https://doi.org/10.1016/C2019-0-02089-3
Adagunodo, T. A., Momoh, L. O., & Ako, B. D. (2017). Geophysical investigation for fault zones in petroleum reservoirs. Journal of Petroleum Exploration and Production Technology, 7(1), 23-35. https://doi.org/10.1007/s13202-016-0291-1
Alexander, T. J. (1998). Sealing capacity of faults in hydrocarbon traps. AAPG Bulletin, 82(7), 123-139.DOI: https://doi.org/10.1306/1D9BCDB5-172D-11D7-8645000102C1865D
Athmer, W., & Luthi, S. M. (2011). Fault zone characteristics and their impact on reservoir performance. Marine and Petroleum Geology, 28(5), 813-828. https://doi.org/10.1016/j.marpetgeo.2010.12.009
Babangida, Y., & Yelwa, A. (2015). Fault behavior and its impacts on reservoir management. International Journal of Petroleum Science and Technology, 9(4), 127-135.
Bamidele, O., & Ehinola, O. A. (2010). Structural traps and fault seals in petroleum reservoirs: A review. Journal of Earth Science and Engineering, 3(3), 114-120.
Botter, C., Fachri, M., & Manzocchi, T. (2017). Fault zone permeability prediction and its role in hydrocarbon migration. Journal of Petroleum Science and Engineering, 156(2), 85-99.https://doi.org/10.1016/j.petrol.2017.03.038
Cerveny, V., Luthi, S. M., & Haakon, J. (2004). Fault and fracture influence on fluid flow in reservoirs. Petroleum Geoscience, 10(4), 237-250.DOI: https://doi.org/10.1144/1354-079303-605
Childs, C., Nicol, A., Walsh, J. J., & Watterson, J. (1997). Sealing potential of faults in hydrocarbon reservoirs. Geological Society, London, Special Publications, 127(1), 243-252. https://doi.org/10.1144/GSL.SP.1998.127.01.15
Fachri, M., Manzocchi, T., & Childs, C. (2013). Fault zone structure and its impact on fault seal. AAPG Bulletin, 97(4), 637-654. DOI: https://doi.org/10.1306/10231212089
Haakon, J. (2016). Structural complexities of fault zones in hydrocarbon reservoirs. Journal of Geophysical Research, 121(3), 125-138. DOI: https://doi.org/10.1002/2015JB012137
Hansen, B., Jensen, C. M., & Sun, W. (2020). Advancing fault seal analysis with machine learning techniques. Petroleum Geoscience, 26(2), 193-204. https://doi.org/10.1144/petgeo2019-065
Oyedele, A., & Adeyemi, J. (2009). Predictive models of fault seal integrity in deep reservoirs. Journal of Petroleum Science and Engineering, 65(1-2), 34-42.
https://doi.org/10.1016/j.petrol.2008.12.010
Sun, W., Wang, T., & Zhang, D. (2022). Machine learning-based fault seal prediction in hydrocarbon reservoirs. Journal of Geophysical Research: Solid Earth, 127(3), e2021006785. DOI: https://doi.org/10.1029/2021JB006785
Yielding, G., Freeman, B., & Needham, D. T. (1997). Quantitative fault seal prediction. AAPG Bulletin, 81(6), 897-917. DOI: https://doi.org/10.1306/522B485F-1727-11D7-8645000102C1865D
Zhang, L., Chen, H., & Liu, X. (2023). Multi-criteria decision analysis in petroleum geoscience: A case study of fault seal evaluation. Journal of Petroleum Exploration and Production Technology, 13(1), 57-69. DOI: https://doi.org/10.1007/s13202-022-01558-9.