Browsing by Author "Lawal, Ahmad"

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    A Novel Framework for Reservoir Permeability Prediction using GPR with Grey Relational Grades and Uncertainty Quantification
    (2024) Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei
    Reservoir permeability prediction is crucial for hydrocarbon exploration and production. Traditional methods have limitations, and Gaussian Process Regression (GPR) offers a powerful alternative. However, GPR can be sensitive to kernel parameters. This paper proposes a novel framework, GPR with Grey Relational Lengthscale Adaptation (GRLA-GPR), that incorporates Grey Relational Grades (GRG) from NMR log data into GPR lengthscale updates to improve permeability prediction with a focus on uncertainty quantification. The framework utilizes a Radial Basis Function (RBF) and Matern kernels' GPR model and calculates GRG to capture relationships between NMR data sequences. The calculated GRG values are then used to update the GPR lengthscale during training. A validation strategy is employed to evaluate the performance. The effectiveness of the framework is assessed using accuracy metrics (mean absolute error, mean squared error and R2) and uncertainty quantification metrics (variance and prediction interval normalized average width). The results are compared to a baseline GPR model without GRG-based updates. The proposed framework achieved a better performance in terms of accuracy and uncertainty quantification, providing more reliable permeability estimates for informed decision-making in reservoir characterization.
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    Machine Learning in Oil and Gas Exploration: A Review
    (IEEE, 2024-02-01) Lawal, Ahmad; Yang, Yingjie; He, Hongmei; Baisa, Nathanael L.
    A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning’s involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field’s dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into less-charted territories in this evolving field, offering valuable insights and guidance for future research.
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    Proceedings of the Faculty of Computing Engineering and Media’s Annual Post-Graduate Researcher Conference, CEMexus 2024
    (De Montfort University, 2024-11-10) Harwood, Tracy; Abdi, Meisam; Chen, Feng; Villa, Raffaela; Abudayeh, Mohammad; Afkhami, Ebi; Ali, Ahmad; Anakwenze, Uche; Bashir, Reem; Carroll, Sean; Clijsen, Eddie; Daneshvar, Bahareh; Garratt, James; Lawal, Ahmad; Mikhaylova, Anjela; Minhas, Asif; Morris, Aiden; Odewale, Stephen; Ojji, Immanuel; Okoya, Silifat Abimbola (Abi); Osowobi, Ayo; Padariya, Debalina; Pasbanigoloojeh, Rahim; Thompson, Shanique; Wang, Ruichao; Wood, Trevor; Xing, Yongkang; Zahedi, Mohsen; Ellim, Timothi; Islam, Rakib; Minhas, Asif; Padariya, Debalina; Salvi, Shweta; Zhang, Wen; Zita, Maggie
    The Proceedings of the annual CEM PGR conference includes abstracts and posters. Each abstract/poster included in the proceedings is contributed by a single identified author.
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    Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements
    (ACM, 2025-03-01) Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei
    This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination ($R^{2}$) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction.