Improving geosteering performance using rate of penetration and gas ratio: case studies in a limestone reservoir

Document Type : Research Paper

Authors

1 Department of Chemical & Petroleum Engineering, Sharif University of Technology, Tehran, Iran

2 Reppco Company, Tehran, Iran

10.22078/jpst.2025.5534.1951

Abstract

Geosteering is an essential method employed in oil and gas drilling, particularly for horizontal wells, to precisely locate the wellbore within hydrocarbon-rich formations. To carry out this process, the gamma-ray logs from the laterals are matched with logs from a reference vertical well to position the lateral in the desired path accurately. Numerous studies have been carried out in the field of geosteering, focusing on the application of machine learning and the creation of automated geosteering methods. Due to the high cost of repeated use of steering, it can be helpful to establish a logical mathematical correlation between two or more parameters for movement within the reservoir. This study investigates the relationship between Rate of Penetration (ROP) and gas ratio data in three laterals drilled in a heterogeneous limestone reservoir in Iran by plotting normalized ROP vs. normalized gas ratio. Geomaster software is used to direct the geosteering process in order to ascertain the reservoir’s depth. Once the ROP and gas ratio data have been normalized and outliers removed, different models such as linear, polynomial, power, and exponential are utilized in MATLAB. As a result, we can observe that for the majority of laterals, the second-degree polynomial model offers the best correlation. Also, the presence of heterogeneity affects some results of laterals. These results can be applied to reduce the expenses associated with recurrent geosteering operations, enable the drilling of new or extended laterals, and optimize drilling operations in the field.

Keywords

References

  1. (2018), Starsteer Manual, The most efficient and easy to use Geoscience Software, StarSteer empowers Geologists and Geoscientists to steer wells in real-time, visualize well logs, map subsurface geology,
and plan well trajectories for optimal well placement and reservoir performance, RGOI Technology, Houston, Texas, The USA. ##
  2. Nascimento, A. (2012). Drilling Fluid: A stochastic ROP optimization approach for the Brazilian pre-salt carbonates, Leoben, Montan University. ##
  3. Othmer, K. (2002). Encyclopedia of Chemical Technology, Fourth Editon, Published by Pergamon Press Ltd. (This is a translation of the eleventh edition of the German work Grundriß der Chemischen Technik published by Verlag Chemie, GmbH, Weinheim), Headington Hill Hall, Oxford, The UK, 13, 1-847. ##
  4. Dashti, J., Al-Awadi, M., Al-Ajmi., B., & Rao, S. (2016). Use of advanced mud gas chromatography for reservoir quality prediction while drilling, IPTC, 18624, doi.org/10.2523/IPTC-18624-MS. ##
  5. Al-Maskeen, A., Sung, R., & Ali, S. (2013). Horizontal well correlation using real time data and log prediction in geosteering complex reservoirs of Saudi Arabia, In SPE Middle East Oil and Gas Show and Conference, 164151, doi.org/10.2118/164151-MS. ##
  6. Maus, S., Gee, T., M. Mitkus, A., McCarthy, K., Charney, E., Ferro, A., Liu, Q., Lightfoot, J., Reynerson, P., & Velozzi, D. (2020). Automated geosteering with fault detection and multi-solution tracking, SPE, IADC, 199660, doi.org/10.2118/199660-MS. ##
  7. Heintzelman, L., Willerth, M., Elhaj, N., & Eaton, J. (2022). Field validation of an automated geosteering algorithm in the haynesville shale, IADC/SPE, 208697, doi.org/10.2118/208697-MS. ##
  8. Angelo, J., Zhao, Z., Zhang, Y., Ashok, P., Chen, D., & Oort, E. (2024). Real-time automated geosteering interpretation combining log interpretation and 3D Horizon Tracking, Geosciences, 14030071, doi.org/10.3390/geosciences14030071. ##
  9. Alyaev, S., Suter, E., Brumer Bratvold, R., Hong, A., Luo, H., & Fossum, K. (2019). A decision support system for multi-target geosteering, Elsevier, 183, 106381, doi.org/10.1016/j.petrol.2019.106381. ##
  10. Fjellheim, R., Herbert, M., Arild, O., Bisio, R., & Holo, O. (2010). Collaboration and Decision Support in Geosteering, SPE, 128721, dx.doi.org/10.2523/128721-MS. ##
  11. Rajaieyamchee, M. A., & Bratvold, R. B. (2010). A decision analytic framework for autonomous geosteering. In SPE Annual Technical Conference and Exhibition? (pp. SPE-135416). SPE., doi.org/10.2118/135416-MS. ##
  12. Shen, Q., Wu, X., Chen, J., Han, Z., & Huang, Y. (2017). Solving geosteering inverse problems by stochastic hybrid monte carlo method, Petroleum Science and Engineering, 161, doi.org/10.1016/j.petrol.2017.11.031, 9-16. ##
  13. Winkler, H. (2017). Geosteering by exact inference on a Bayesian network, Geophysics, 0569, dx.doi.org/10.1190/geo2016-0569.1##
  14. Bittar, M., Klein, J., Beste, R., Hu, G., Wu, M., Pitcher, J., Golla, C., Althoff, G., Sitka, M., Minosyan, V., & Paulk, M., (2009). A new azimuthal deep-reading resistivity tool for geosteering and advanced formation evaluation. SPE Reservoir Evaluation & Engineering, 12(02), 270-279, doi.org/10.2118/109971-PA, 270-279. ##
  15. Tilsley-Baker, R., Antonov, Y., Martakov, S., Maurer, H., Mosin, A., Sviridov, M., Sandler Klein, K., Iversen, M. (2013). Eustaquio Barbosa. J and Carneiro. G, Extra-Deep Resistivity Experience in Brazil Geosteering Operations, SPE, 166309, doi.org/10.2118/166309-MS##
  16. Mottahedeh, R. (2008). Horizontal well geosteering: planning, monitoring and geosteering, Canadian Petroleum Technology, doi.org/10.2118/08-11-28-CS##
  17. Thevoux-Chabuel, H., & Fejerskov, M., (2006). Geosteering diagnosis: a new approach to monitor the well position within a 3D geological model, In SPE Annual Technical Conference and Exhibition?, 102602, doi.org/10.2118/102602-MS. ##
  18. Zongguo, W., Xing, L., Jianyi, D., Zhaofeng, L., Zhao, Z., Gaocheng, W., Yang, G., & Xun, L. (2017). Application of 3D geosteering in geology-engineering integration practice, China Petroleum Exploration, 22, 1, doi.org/10.3969/j.issn.1672-7703.2017.01.011. ##
  19. S, Yuan. S, Wang. T, Gao. J and Li. S. (2018). Three-dimensional geosteering coherence attributes for deep-formation discontinuity detection, Geophysics, doi.org/10.1190/geo2017-0642.1. ##
  20. Vladimirovich Denisenko. I, Andreevich Kuvaev. I, Borisovich Uvarov. I, Evgenievich Kushmantzev. O and Igorevich Toporov. A. (2020). Automated Geosteering While Drilling Using Machine Learning. Case Studies, In SPE Russian Petroleum Technology Conference (p. D023S009R004). doi.org/10.2118/202046-MS. ##
  21. Gupta, I., Tran, N., Devegowda, D., Jayaram, V., Rai, C., Sondergeld, C., & Karami, H. (2020). Looking ahead of the bit using surface drilling and petrophysical data: Machine-learning-based real-time geosteering in volve field. SPE Journal, 25(02), 990-1006. Gupta, I., Tran, N., Devegowda, D., Jayaram, V., Rai, C., Sondergeld, C., & Karami, H. (2020). Looking ahead of the bit using surface drilling and petrophysical data: Machine-learning-based real-time geosteering in volve field. SPE Journal, 25(02), 990-1006.. ##
  22. Galkina, A. V., Yalaev, T. R., Lisitsyna, M. Y., & Rakhimov, T. R. (2020). Geosteering based on integration of LWD and surface logging using machine learning. In SPE Russian Petroleum Technology Conference (p. D043S032R006). doi.org/10.2118/201945-MS. ##

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