Intelligent Classification of Deep-water Submarine Fan Lithofacies Based on Small Sample

doi:10.11885/j.issn.1674-5086.2024.09.15.02

Abstract

Abstract: Aiming at the problems of high cost, difficulty in operation, high technical requirements, limited number of rock core-taking and small number of lithofacies samples obtained in the exploration and development of deep-water submarine fan reservoir, an intelligent classification method of deep-water submarine fan lithofacies based on small samples was proposed. First, the Empirical Mode Decomposition (EMD) and sliding window are used to construct multi-layer image styles input for each well point as inputs. Secondly, the lithofacies recognition model is constructed by using Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) algorithms. The Generative Adversarial Networks (GAN) model was used to expand a few class samples. Finally, a Genetic Algorithm (GA) was introduced to optimize the model parameters. Taking Akpo Oilfield in the Niger Delta Basin of West Africa as the research area, this method is used to carry out the intelligent identification of lithofacies. Research shows that the lithofacies classification accuracy of the GAN-GA-CNN model proposed in this paper can reach 94.22%, which greatly improves the prediction accuracy compared with the original CNN model, proving the feasibility of the proposed method.

Key words: lithofacies identification, submarine fan reservoir, small sample, deep learning, genetic algorithm

CLC Number:

TE319

ZHEN Yan, LIU Xiaowei, ZHANG Yihao, ZHAO Zhen, XIAO Yifei, ZHAO Xiaoming. Intelligent Classification of Deep-water Submarine Fan Lithofacies Based on Small Sample[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2025, 47(6): 60-71.

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