A Graph Convolutional Network Approach to Qualitative Classification of Hydrocarbon Zones Using Petrophysical Properties in Well Logs

Venkateshwaran, B.; Ramkumar, Mu.; Siddiqui, Numair Ahmed; Haque, AKM Eahsanul; Sugavanam, G.; Manobalaji, A.

doi:10.1007/s11053-024-10311-x

Nat Resour Res

2024

DOI: 10.1007/s11053-024-10311-x

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A Graph Convolutional Network Approach to Qualitative Classification of Hydrocarbon Zones Using Petrophysical Properties in Well Logs

B. Venkateshwaran,

Mu. Ramkumar,

Numair Ahmed Siddiqui

et al.

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Cited by 3 publications

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Energy Geoscience

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Shear wave velocity prediction: A review of recent progress and future opportunities

Olutoki,

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Energy Geoscience

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Research status and prospects of intelligent logging lithology identification

Jin,

Yutong,

Xuan

2024

Meas. Sci. Technol.

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ABSTRACT：With the increasing of petroleum exploration and development, accurate lithology identification is of crucial. Machine learning plays a key role in logging lithology identification. By introducing traditional logging lithology identification methods, we review the application of machine learning in logging lithology identification from the perspectives of bibliometrics and machine learning classification in this paper. The applications of supervised learning, semi-supervised learning, unsupervised learning, ensemble learning, and deep learning algorithms in logging lithology identification are introduced in detail. Multiple machine learning algorithms have achieved remarkable results in different scenarios. For example, SVM (Support Vector Machine), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and CNN (Convolutional Neural Network) perform well in logging lithology identification and obtain relatively high identification accuracy. However, machine learning for logging lithology identification also faces challenges such as data quality, data imbalance, model generalization, and model interpretability. Future research should focus on algorithm optimization and innovation, improvements in data quality and quantity, deep multidisciplinary integration and practical application to enhance the accuracy and reliability of lithology identification. These findings provide strong support for oil and gas exploration and development.

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A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China

Song,

Zhang,

Guo

et al. 2024

Open Geosciences

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The characterization of reservoir fluid properties is a crucial component of oilfield operations, as it provides a vital data foundation for the development and optimization of oilfield work programs. However, the complexity of water-flooded, along with the mixed data from drilling and cable logging, and the inherently weak foundational research, make the evaluation of water-flooded formations difficult. Therefore, this article aims to address this challenge by proposing a new reservoir fluid identification method. In this article, an improved Markov variation field model is applied to map geophysical logging data and is integrated with a quantum hybrid neural network (HQNN) to address the nonlinear correlations between logging data. By integrating the non-standard Markov variation field with HQNN, this article constructs a novel reservoir fluid identification model. Experimental results demonstrate that the model achieves a recognition accuracy of 90.85% when trained on feature images mapped from logging data. Furthermore, the superiority of the HQNN was validated through eight sets of comparative experiments. Additionally, the model was further validated using logging data from blind wells within the block, demonstrating high predictive accuracy and proving its effectiveness for reservoir fluid identification in the PL block. The method proposed in this article not only addresses the challenge of evaluating water-flooded layers in the absence of key logging curves but also offers a novel approach to reservoir fluid identification using geophysical logging data. The non-standard Markov transition field model is employed to map logging data into feature images, offering a new perspective on the application of geophysical logging data in practical reservoir analysis.

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A Graph Convolutional Network Approach to Qualitative Classification of Hydrocarbon Zones Using Petrophysical Properties in Well Logs

Cited by 3 publications

References 53 publications

Shear wave velocity prediction: A review of recent progress and future opportunities

Shear wave velocity prediction: A review of recent progress and future opportunities

Research status and prospects of intelligent logging lithology identification

A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China

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