An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Hongyang Chu, Xinwei Liao, Peng Dong, Zhiming Chen, Xiaoliang Zhao and Jiandong Zou
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Hongyang Chu: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Xinwei Liao: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Peng Dong: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Zhiming Chen: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Xiaoliang Zhao: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Jiandong Zou: College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China

Energies, 2019, vol. 12, issue 15, 1-27

Abstract: The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases.

Keywords: convolutional neural network; well testing; tight reservoirs; pressure derivative; automatic classification (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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