Lithologic Identification of Complex Reservoir Based on PSO-LSTM-FCN Algorithm

Yawen He, Weirong Li, Zhenzhen Dong (), Tianyang Zhang, Qianqian Shi, Linjun Wang, Lei Wu, Shihao Qian, Zhengbo Wang, Zhaoxia Liu and Gang Lei
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Yawen He: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Weirong Li: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Zhenzhen Dong: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Tianyang Zhang: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Qianqian Shi: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Linjun Wang: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Lei Wu: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Shihao Qian: College of Petroleum Engineering, Yanta Campus, Xi’an Shiyou University, Xi’an 710065, China
Zhengbo Wang: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Zhaoxia Liu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Gang Lei: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

Energies, 2023, vol. 16, issue 5, 1-18

Abstract: Reservoir lithology identification is the basis for the exploration and development of complex lithological reservoirs. Efficient processing of well-logging data is the key to lithology identification. However, reservoir lithology identification through well-logging is still a challenge with conventional machine learning methods, such as Convolutional Neural Networks (CNN), and Long Short-term Memory (LSTM). To address this issue, a fully connected network (FCN) and LSTM were coupled for predicting reservoir lithology. The proposed algorithm (LSTM-FCN) is composed of two sections. One section uses FCN to extract the spatial properties, the other one captures feature selections by LSTM. Well-logging data from Hugoton Field is used to evaluate the performance. In this study, well-logging data, including Gamma-ray (GR), Resistivity (ILD_log10), Neutron-density porosity difference (DeltaPHI), Average neutron-density porosity(PHIND), and (Photoelectric effect) PE, are used for training and identifying lithology. For comparison, seven conventional methods are also proposed and trained, such as support vector machines (SVM), and random forest classifiers (RFC). The accuracy results indicate that the proposed architecture obtains better performance. After that, particle swarm optimization (PSO) is proposed to optimize hyper-parameters of LSTM-FCN. The investigation indicates the proposed PSO-LSTM-FCN model can enhance the performance of machine learning algorithms on identify the lithology of complex reservoirs.

Keywords: complex reservoir; lithology identification; machine learning; LSTM-FCN; PSO optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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