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A Core Logging, Machine Learning and Geostatistical Modeling Interactive Approach for Subsurface Imaging of Lenticular Geobodies in a Clastic Depositional System, SE Pakistan

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Abstract

Facies models are essential tools for imaging subsurface geobodies and for reducing exploration and development risks efficiently. The Lower Goru Formation is one of the principal formations in the Lower Indus Basin, Pakistan. Its substantial hydrocarbon potential is unexplored, as most of the wells within the Sawan gas field are facing relatively low production yields. This study aimed to delineate subsurface geobodies by developing a facies model to study the depositional processes and facies distributions that have been neglected previously. The interactive approaches used in this research consisted of petrophysical, mineral composition, well-log facies, and horizon attribute analyses, as well as an unsupervised vector quantizer artificial neural network (UVQ–ANN) and sequential indicator simulation (SIS) modeling. A series of E–W-oriented lenticular geobodies were delineated. These geobodies had variable thicknesses, and they pinch out to the NW and prograde to the NE. The results of the SIS, UVQ–ANN, petrographic analysis, and attribute analysis show a fluvial fan-delta sedimentary system. The reservoir sands were deposited in distributary mouth bars and deltaic channels in proximal delta front settings. The coarse- to very fine-grained reservoir sands prograde toward the NE. Thinly laminated beds of fine-grained black shales and lime muddy siltstones were deposited under low-energy conditions in mid-shelf marine settings. The adopted methodology for the generated facies model can be extended to different basins within Pakistan with the same geological settings, and it can be used for prospect evaluation, future drilling, and development plans within the Sawan gas field in the Lower Indus Basin.

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Acknowledgments

Dr. John Carranza, Editor-in-Chief of Natural Resources Research, is appreciated for his valuable recommendations in improving this manuscript. The authors would like to thank the Directorate General of Petroleum Concessions (DGPC), Pakistan, to release 3D seismic and well data. The authors would like to acknowledge the dGB Earth Sciences™ for giving academic license of OpendTect software to the School of Ecology and Environmental Science, Yunnan University, China. This work was funded and supported by Yunnan Provincial Government Leading Scientist Program, No. 2015HA024, and the National Natural Science Foundation of China (41820104008).

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Authors and Affiliations

  1. Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming, 650504, China

    Umar Ashraf, Hucai Zhang, Aqsa Anees & Xiaonan Zhang

  2. School of XingFa Mining Engineering, Wuhan Institute of Technology, Wuhan, 430073, China

    Hassan Nasir Mangi

  3. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Muhammad Ali & Jar Ullah

  4. School of Earth Sciences, China University of Geosciences, Wuhan, 430074, China

    Muhammad Imraz

  5. Department of Earth and Environmental Sciences, Bahria University, Islamabad, 44000, Pakistan

    Saiq Shakeel Abbasi

  6. Department of Petroleum Engineering, NED University of Engineering and Technology, Karachi, 75270, Pakistan

    Ayesha Abbas

  7. Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China

    Zaheen Ullah

  8. School of Resource Environment and Earth Science, Yunnan University, Kunming, 650500, China

    Shucheng Tan

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  1. Umar Ashraf
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Ashraf, U., Zhang, H., Anees, A. et al. A Core Logging, Machine Learning and Geostatistical Modeling Interactive Approach for Subsurface Imaging of Lenticular Geobodies in a Clastic Depositional System, SE Pakistan. Nat Resour Res 30, 2807–2830 (2021). https://doi.org/10.1007/s11053-021-09849-x

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