Enhancing Subsurface Reservoir Characterisation via Predictive LSTM Model of NPHI

Isam, Sherrif, Liu, Yefeng and Zhang, Qichun (2024) Enhancing Subsurface Reservoir Characterisation via Predictive LSTM Model of NPHI. In: ICAC 2024 - 29th International Conference on Automation and Computing.

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Abstract

Subsurface well logging is a cornerstone in the oil and gas industry, providing critical insights into reservoir properties and aiding in reservoir characterisation. Neutron Porosity Index (NPHI) is a key parameter derived from well logging data, offering valuable information about reservoir porosity. Accurate prediction of NPHI is essential for reservoir evaluation and production forecasting. Traditional methods often rely on empirical relationships or physics-based models, which may be limited in their ability to capture the complex relationships present in the data. With the advancement of machine learning techniques, there has been a growing interest in developing data-driven models for well logs prediction. In this study, we proposed a novel approach for predicting NPHI in subsurface formations via multivariate time series analysis using Long Short-Term Memory (LSTM). To demonstrate the performance advantage of the proposed method, we also conduct a comprehensive comparative analysis of the normal Artificial Neural Networks (ANN). Both LSTM and ANN models are rigorously evaluated on the dataset from Volve oil field well 15/9-F-1A. LSTM has exceeded the aforementioned ANN model during forecasting, thereby offering valuable insights for reservoir characterisation and hydrocarbon exploration in the oil and gas industry.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Publisher Copyright: © 2024 IEEE.; 29th International Conference on Automation and Computing, ICAC 2024 ; Conference date: 28-08-2024 Through 30-08-2024
Keywords:	Multivariate LSTM, NPHI prediction, Reservoir characterisation, Subsurface well logging, TensorFlow
Depositing User:	RED Unit Admin
Date Deposited:	27 May 2025 13:34
Last Modified:	27 May 2025 13:34
URI:	https://bnu.repository.guildhe.ac.uk/id/eprint/20262

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