Study on Morphological Identification of Tight Oil Reservoir Residual Oil after Water Flooding in Secondary Oil Layers Based on Convolution Neural Network

Ling Zhao, Xianda Sun, Fang Liu, Pengzhen Wang and Lijuan Chang
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Ling Zhao: College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
Xianda Sun: Key Laboratory of “Continental Shale Oil and Gas Accumulation and Efficient Development” of Ministry of Education, Northeast Petroleum University, Daqing 163318, China
Fang Liu: College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
Pengzhen Wang: Information Technology Center, Daqing Oilfield Design Institute Co., Ltd., Daqing 163712, China
Lijuan Chang: College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China

Energies, 2022, vol. 15, issue 15, 1-12

Abstract: In this paper, a microscopic oil displacement visualization experiment based on the glass etching model to simulate the tight oil reservoir of underground rocks is carried out. At present, water flooding technology is widely used in the development of oil and gas fields, and the remaining oil content is still very high after water flooding. It is the key to improving oil recovery to identify and study the remaining oil form distribution after water flooding. The experiment result shows there are five types of residual oil after water flooding: columnar residual oil, membranous residual oil, oil droplet residual oil, blind terminal residual oil and cluster residual oil. A convolution neural network is suitable for complex image characteristics with good robustness. In recent years, it has made a breakthrough in a set of small and efficient neural networks with SqueezeNet, Google Inception and the flattened network method put forward. In order to solve the problems of low automation, low efficiency and high error rate in the traditional remaining oil form recognition algorithm after water flooding in tight oil reservoirs, an image recognition algorithm based on the MobileNets convolutional neural network model was proposed in this paper to achieve accurate recognition of the remaining oil form. Based on traditional image processing methods which, respectively, extracted the whole picture of the different types of remaining oil in the image block, it uses the MobileNets network structure to classify different types of image block and realizes the layered depth convolution neural network system. The experiment result shows that the model can accurately identify the remaining oil forms, and the overall recognition accuracy is up to 83.8% after the convergence of the network model, which infinitely identifies the remaining oil forms in the morphological library, proving the strong generalization and robustness of the model.

Keywords: convolution neural network; residual oil shape; deep learning (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: 2022
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