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Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

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Abstract

The estimation of accurate reservoir parameters is essential for conventional and non-conventional hydrocarbon prospects. An artificial neural network has been developed to predict the reservoir parameters (porosity and saturation of gas hydrates) in a silty-sand, sandy-silt and pelagic-poor clay reservoir at two neighbour wells using the petrophysical information at another well in the Krishna–Godavari basin. The well log data were acquired during the Expedition-02 of Indian National Gas Hydrates Program (NGHP Exp-02). The estimation of gas hydrate saturation using Archie’s equation may be erroneous, as it is valid for the quantification of conventional hydrocarbons in the clean sand reservoir. Since the study area is clay dominated, it is subjective to adjust Archie’s exponents so that it matches with the saturation, measured from the core data. To overcome this problem of estimating the reservoir parameters in such a scenario, first of all we have derived porosity from the density log data and estimated saturation by employing modified Archie’s equation to the resistivity log data at one well. In order to train the network, the log data at one well are taken as inputs and corresponding porosity and saturation are taken as outputs. The reservoir parameters are then predicted at two neighbour wells using the wireline log data as input in those two wells. The predicted porosity and saturation of gas hydrates are alike to the traditionally estimated porosity and saturation at the neighbour wells. The predicted porosity in the studied region varies between 33 and 76%, whereas the saturation of gas hydrates ranges between 3.39 and 86.92%. This shows that the designed network can be used to estimate the reservoir parameters directly from the well log data in the same reservoirs.

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Acknowledgements

We are thankful to the Director, CSIR–National Geophysical Research Institute, Hyderabad for permission to publish this work (Ref. No.: NGRI/Lib/2019/Pub-32). We are grateful to the Ministry of Petroleum and Natural Gas (Government of India), Oil and Natural Gas Corporation Limited (ONGC), Directorate General of Hydrocarbons (DGH), Oil India Limited (OIL), Gas Authority of India Limited (GAIL), Indian Oil Corporation Limited (IOCL) and all other NGHP partner organizations for providing the opportunity to contribute to the NGHP Exp-02. The technical and science support from Japan Agency for Marine-Earth Science and Technology (JAMSTEC), United States Geological Survey (USGS), U.S. Department of Energy (US-DOE), the National Institute of Advanced Industrial Science and Technology (AIST), Geotek Coring and Schlumberger is gratefully acknowledged. The author also acknowledges the anonymous reviewers for improving the manuscript. The first author acknowledges the Science and Engineering Research Board (SERB) (Project No. SERB/PDF/2017/001331), Government of India, for the financial support. The Ministry of Earth Sciences is acknowledged for extending support in pursuing research on gas hydrates at CSIR–NGRI. This is contributed to the In-house project MLP-6402-28(KS).

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  1. Seismic Group, CSIR–National Geophysical Research Institute, Uppal Road, Hyderabad, 500 007, India

    Bappa Mukherjee & Kalachand Sain

  2. Wadia Institute of Himalayan Geology, 33, General Mahadeo Singh Road, Dehradun, 248 001, India

    Kalachand Sain

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Mukherjee, B., Sain, K. Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02). J Earth Syst Sci 128, 199 (2019). https://doi.org/10.1007/s12040-019-1210-x

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