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International Journal of Advances in Engineering & Scientific Research

PETROPHYSICAL ESTIMATION OF LOW RESISTIVITY PAY ZONES OF RESERVOIRS IN CAPE THREE POINT BASIN, GHANA

*Benjamin Aboagye, **Joseph O. Thompson

*Benjamin Aboagye, Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, United States. ORCID iD: 0009-0001-3323-9363,

**Joseph O. Thompson, Department of Earth Science, University of Ghana, Legon Boundary, Accra, Ghana ORCID iD: 0000-0002-3026-1506

DOI : https://doi.org/10.53882/IJAESR.2024.1101002 Page No : 01-16

Published Online : 2024-06-30

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ABSTRACT

This study investigates the petrophysical evaluation of well logs to gauge the productivity potential of low resistivity pay zones in the Cape Three Point Basin, offshore Ghana. Data from CTP-Ghana-11X, CTP-Ghana-12X, and Axim 4-3X wells were analyzed, revealing six hydrocarbon-bearing zones in each. We examined gamma ray, Indonesia water saturation, Archie water saturation, and resistivity logs in the CTP-Ghana-12X well, and gamma-ray, spontaneous potential, density, neutron, and resistivity logs in the CTP-Ghana-11X and Axim 4-3X wells, along with Indonesia and Archie water saturation logs. Our findings underscore the impact of shale volume percentages on effective porosity and water saturation. High shale content decreases effective porosity while increasing water saturation, a trend accentuated by the Archie water saturation model but rectified by the Indonesian model, indicating potentially high hydrocarbon saturation. In the CTP-Ghana-11X well, the average shale volume is 28%, with an effective porosity of 58.4%. The Archie and Indonesia water saturation averages are 25.27% and 22.83%, respectively. Similar trends are observed in the CTP-Ghana-12X well. The average shale volume is 25.34%, with water saturation readings of 53.93% (Archie model) and 40.80% (Indonesia model). In the Axim 4-3X well, with an average shale volume of 37.8%, effective porosity is 20.35%, with Archie and Indonesia water saturation averages of 50.89% and 46.81%, respectively. Despite high shale volumes hinting at potential unproductivity, the Indonesia water saturation values and the resistivity pay concept suggest economically viable prospects in the Cape Three Point Basin, emphasizing the importance of petrophysical analysis in exploration and production decision-making.

Keywords: Effective porosity, Water saturation, Permeability, Clay minerals, Resistivity pay

References

Al-Baldawi, B. A. (2021). Evaluation of petrophysical properties using well logs of yamama Formation in abu amood oil field, southern Iraq. The Iraqi Geological Journal, 67-77.
Bai, Z., Tan, M., Shi, Y., Guan, X., Wu, H., & Huang, Y. (2022). Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine. Scientific Reports, 12(1), 1046.
Barustan, M. I., Siki, D. F. C., Butarbutar, E. F., & Suseno, P. (2021). Identification and Characterization of Low Resistivity Pay Zone, Case Study “L” Field. Journal IATMI.
Boyd, A., Davis, B., Flaum, C., Klein, J., Sneider, R. M., Sibbit, A. And Singer, J. (1995), Low down on Low-Resistivity Pay. OilField Review, 7(3), 1-15.
Carcione, J. M., Gei, D., Yu, T., & Ba, J. (2019). Effect of clay and mineralogy on permeability. Pure and Applied Geophysics, 176, 2581-2594.
Ellis, D. V., & Singer, J. M. (2007). An Overview of Well Logging. Well Logging for Earth Scientists, 1-15.
Fan, P., Deng, R., Qiu, J., Zhao, Z., & Wu, S. (2021). Well logging curve reconstruction based on kernel ridge regression. Arabian Journal of Geosciences, 14, 1-10.
Ghosh, S. (2022). A review of basic well log interpretation techniques in highly deviated wells. Journal of Petroleum Exploration and Production Technology, 12(7), 1889-1906.
Gupta, A. R. (2023). A theoretical approach for water saturation estimation in shaly sandstones. Geoenergy Science and Engineering, 228, 212001.
Hashim, R. A., & Farman, G. M. (2023). How to Estimate the Major Petrophysical Properties: A Review. Iraqi Journal of Oil & Gas Research, 3(1), 43-58.
Iranfar, S., Karbala, M. M., Shakiba, M., & Shahsavari, M. H. (2023). Effects of type and distribution of clay minerals on the physico-chemical and geomechanical properties of engineered porous rocks. Scientific Reports, 13(1), 5837.
Joshi, D., Patidar, A. K., Mishra, A., Mishra, A., Agarwal, S., Pandey, A., ... & Choudhury, T. (2021). Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles. GeoJournal, 1-22.
Kamali, M. Z., Davoodi, S., Ghorbani, H., Wood, D. A., Mohamadian, N., Lajmorak, S., ... & Band, S. S. (2022). Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Marine and Petroleum Geology, 139, 105597.
Lai, J., Wang, G., Fan, Q., Pang, X., Li, H., Zhao, F., ... & Wang, Q. (2022). Geophysical well-log evaluation in the era of unconventional hydrocarbon resources: a review on current status and prospects. Surveys in Geophysics, 43(3), 913-957.
Lai, J., Wang, G., Fan, Q., Zhao, F., Zhao, X., Li, Y., ... & Pang, X. (2023). Toward the scientific interpretation of geophysical well logs: Typical misunderstandings and countermeasures. Surveys in Geophysics, 44(2), 463-494.
Li, M., Hong, G. L., Ma, Y., Bai, Z., & Hu, G. (2021). Low Resistivity Pay Zones Evaluation in South Sumatra Basin, Indonesia: In Integrated Method. In Proceedings of the International Field Exploration and Development Conference 2020 (pp. 2144-2152). Springer Singapore.
Lin, T., Mezghani, M., Xu, C., & Li, W. (2021, September). Machine learning for multiple petrophysical properties regression based on core images and well logs in a heterogenous reservoir. In SPE Annual Technical Conference and Exhibition? (p. D011S018R006). SPE.
Mohamed, O., & Abdulaziz, A. (2023). A novel approach to model water saturation in shaly sandstone petroleum reservoirs.
Pan, W., Torres-Verdín, C., Duncan, I. J., & Pyrcz, M. J. (2023). Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models. Geophysics, 88(2), D159-D175.
Risha, M., Tsegab, H., Rahmani, O., & Douraghi, J. (2023). The impact of clay minerals on the porosity distribution of clastic reservoirs: a case study from the Labuan Island, Malaysia. Applied Sciences, 13(6), 3427.
Senosy, A. H., Ewida, H. F., Soliman, H. A., & Ebraheem, M. O. (2020). Petrophysical analysis of well logs data for identification and characterization of the main reservoir of Al Baraka Oil Field, Komombo Basin, Upper Egypt. SN Applied Sciences, 2(7), 1293.
Shahrina, R. J. R. K., Sideka, A., Agia, A., Junina, R., Jaafara, M. Z., & Ariffinc, H. (2022). Comparison of Water Saturation Models Based on Well Logging Data: A Case Study of MX Field in Malay Basin. J. Kejuruter, 34, 101-108.
Worthington, P. F. (1985). Evolution of Shaly-Sand Concept in Reservoir Evaluation, The Log Analyst, v.26, no.1, pp. 23-40
Wyllie M.R.J. and Southwick P.K. (1954). An experimental investigation of the S.P. and resistivity phenomena in dirty sands. J. Petrol. Tech. 6, pp. 44-57
Yongjun, W., Yuanhui, S., Siyu, Y., Shuhong, W., Hui, L., Min, T., & Hengyu, L. (2022). Saturation evaluation of microporous low resistivity carbonate oil pays in Rub Al Khali Basin in the Middle East. Petroleum Exploration and Development, 49(1), 94-106.
Zhao, X., Wang, W., Li, Q., Zhen, G., Zhou, B., Liu, B., ... & Zhang, Y. (2023). Calculation of oil saturation in water-flooded layers based on the modified Archie model. Interpretation, 11(2), T329-T337.

CITE THIS ARTICLE AS: Benjamin A and Joseph O.T. (2024), “Petrophysical Estimation of Low Resistivity Pay Zones of Reservoirs in Cape Three Point Basin, Ghana”, International Journal of Advances in Engineering & Scientific Research, Volume 11, Issue 1, 2024, pp 12-27. DOI : https://doi.org/10.53882/IJAESR.2024.1101002

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References

Al-Baldawi, B. A. (2021). Evaluation of petrophysical properties using well logs of yamama Formation in abu amood oil field, southern Iraq. The Iraqi Geological Journal, 67-77.
Bai, Z., Tan, M., Shi, Y., Guan, X., Wu, H., & Huang, Y. (2022). Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine. Scientific Reports, 12(1), 1046.
Barustan, M. I., Siki, D. F. C., Butarbutar, E. F., & Suseno, P. (2021). Identification and Characterization of Low Resistivity Pay Zone, Case Study “L” Field. Journal IATMI.
Boyd, A., Davis, B., Flaum, C., Klein, J., Sneider, R. M., Sibbit, A. And Singer, J. (1995), Low down on Low-Resistivity Pay. OilField Review, 7(3), 1-15.
Carcione, J. M., Gei, D., Yu, T., & Ba, J. (2019). Effect of clay and mineralogy on permeability. Pure and Applied Geophysics, 176, 2581-2594.
Ellis, D. V., & Singer, J. M. (2007). An Overview of Well Logging. Well Logging for Earth Scientists, 1-15.
Fan, P., Deng, R., Qiu, J., Zhao, Z., & Wu, S. (2021). Well logging curve reconstruction based on kernel ridge regression. Arabian Journal of Geosciences, 14, 1-10.
Ghosh, S. (2022). A review of basic well log interpretation techniques in highly deviated wells. Journal of Petroleum Exploration and Production Technology, 12(7), 1889-1906.
Gupta, A. R. (2023). A theoretical approach for water saturation estimation in shaly sandstones. Geoenergy Science and Engineering, 228, 212001.
Hashim, R. A., & Farman, G. M. (2023). How to Estimate the Major Petrophysical Properties: A Review. Iraqi Journal of Oil & Gas Research, 3(1), 43-58.
Iranfar, S., Karbala, M. M., Shakiba, M., & Shahsavari, M. H. (2023). Effects of type and distribution of clay minerals on the physico-chemical and geomechanical properties of engineered porous rocks. Scientific Reports, 13(1), 5837.
Joshi, D., Patidar, A. K., Mishra, A., Mishra, A., Agarwal, S., Pandey, A., ... & Choudhury, T. (2021). Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles. GeoJournal, 1-22.
Kamali, M. Z., Davoodi, S., Ghorbani, H., Wood, D. A., Mohamadian, N., Lajmorak, S., ... & Band, S. S. (2022). Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Marine and Petroleum Geology, 139, 105597.
Lai, J., Wang, G., Fan, Q., Pang, X., Li, H., Zhao, F., ... & Wang, Q. (2022). Geophysical well-log evaluation in the era of unconventional hydrocarbon resources: a review on current status and prospects. Surveys in Geophysics, 43(3), 913-957.
Lai, J., Wang, G., Fan, Q., Zhao, F., Zhao, X., Li, Y., ... & Pang, X. (2023). Toward the scientific interpretation of geophysical well logs: Typical misunderstandings and countermeasures. Surveys in Geophysics, 44(2), 463-494.
Li, M., Hong, G. L., Ma, Y., Bai, Z., & Hu, G. (2021). Low Resistivity Pay Zones Evaluation in South Sumatra Basin, Indonesia: In Integrated Method. In Proceedings of the International Field Exploration and Development Conference 2020 (pp. 2144-2152). Springer Singapore.
Lin, T., Mezghani, M., Xu, C., & Li, W. (2021, September). Machine learning for multiple petrophysical properties regression based on core images and well logs in a heterogenous reservoir. In SPE Annual Technical Conference and Exhibition? (p. D011S018R006). SPE.
Mohamed, O., & Abdulaziz, A. (2023). A novel approach to model water saturation in shaly sandstone petroleum reservoirs.
Pan, W., Torres-Verdín, C., Duncan, I. J., & Pyrcz, M. J. (2023). Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models. Geophysics, 88(2), D159-D175.
Risha, M., Tsegab, H., Rahmani, O., & Douraghi, J. (2023). The impact of clay minerals on the porosity distribution of clastic reservoirs: a case study from the Labuan Island, Malaysia. Applied Sciences, 13(6), 3427.
Senosy, A. H., Ewida, H. F., Soliman, H. A., & Ebraheem, M. O. (2020). Petrophysical analysis of well logs data for identification and characterization of the main reservoir of Al Baraka Oil Field, Komombo Basin, Upper Egypt. SN Applied Sciences, 2(7), 1293.
Shahrina, R. J. R. K., Sideka, A., Agia, A., Junina, R., Jaafara, M. Z., & Ariffinc, H. (2022). Comparison of Water Saturation Models Based on Well Logging Data: A Case Study of MX Field in Malay Basin. J. Kejuruter, 34, 101-108.
Worthington, P. F. (1985). Evolution of Shaly-Sand Concept in Reservoir Evaluation, The Log Analyst, v.26, no.1, pp. 23-40
Wyllie M.R.J. and Southwick P.K. (1954). An experimental investigation of the S.P. and resistivity phenomena in dirty sands. J. Petrol. Tech. 6, pp. 44-57
Yongjun, W., Yuanhui, S., Siyu, Y., Shuhong, W., Hui, L., Min, T., & Hengyu, L. (2022). Saturation evaluation of microporous low resistivity carbonate oil pays in Rub Al Khali Basin in the Middle East. Petroleum Exploration and Development, 49(1), 94-106.
Zhao, X., Wang, W., Li, Q., Zhen, G., Zhou, B., Liu, B., ... & Zhang, Y. (2023). Calculation of oil saturation in water-flooded layers based on the modified Archie model. Interpretation, 11(2), T329-T337.

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