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Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

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Abstract

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

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Acknowledgements

Few aspects of the recurrent neural network model implementation are based upon the work supported by the Geosciences Research Program in the Office of Basic Energy Sciences, U.S. Department of Energy, under Award Number DE-SC0019266. We thank Mr. Gary Simpson, Dr. Carl Sondergeld, and Dr. Chandra Rai for their technical insights and suggestions related to nuclear magnetic resonance measurements and other well logs.

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  1. Mewbourne School of Petroleum and Geological Engineering, The University of Oklahoma, Norman, OK, 73069, USA

    Hao Li, Siddharth Misra & Jiabo He

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  1. Hao Li
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  2. Siddharth Misra
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Correspondence to Siddharth Misra.

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Li, H., Misra, S. & He, J. Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints. Neural Comput & Applic 32, 3873–3885 (2020). https://doi.org/10.1007/s00521-019-04124-w

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