Bi-LSTM Deep Neural Network Reservoir Classification Model Based on the Innovative Input of Logging Curve Response Sequences

• Published in: IEEE access Vol. 9; pp. 19902 - 19915
• Main Authors: Xueqing, Zhou, Zhansong, Zhang, Chaomo, Zhang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Bi-LSTM; Biological system modeling; Classification; Data logging; deep neural network; logging curve response sequences; Machine learning; Magnetic cores; Mathematical models; Metamorphic rocks; Neural networks; Parameter identification; Predictive models; Recurrent neural networks; reservoir classification; Reservoirs; Rocks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3053289

Reservoir classification is an important component of reservoir geological modelling and reservoir evaluation and identification. Using a single conventional logging curve to identify complex heterogeneous reservoir types has always been a difficult task in logging interpretation. For the first time, this study reveals the advantages of recurrent neural networks in the identification of heterogeneous reservoirs and proposes an optimal parameter bidirectional long short-term memory (Bi-LSTM) recurrent neural network reservoir classification model with optimal parameters that can make full use of logging sequence information. The data used in this work originate from 3 wells in the BZ gas field in China. First, the rationality of the data set and the generation of sequence data were studied in detail, and the logging curve response sequence data, which can fully characterize the reservoir characteristics, were obtained. Then, through multiple simulation experiments, the optimal network structure and hyperparameters were determined, and a Bi-LSTM network model with 5 hidden layers and the optimal network parameters was established. The model was used to predict fractured, pore-fracture and fracture-pore reservoirs in the buried hill metamorphic rock buried beneath the BZ gas field. A comparison with the prediction results of 5 classic machine learning methods and baseline models shows that the Bi-LSTM model with the optimal parameters is superior to the other machine learning methods, especially regarding the prediction accuracy of pore-fracture reservoirs, and the overall accuracy is 92.69%. The method proposed in this paper can accurately identify the strata developed in different types of storage space and significantly improves the reservoir identification accuracy.