Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods
Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. I...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2020/1651549 |
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| Summary: | Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. The connectivity is calculated by using the deep learning model, and the connectivity factor K is defined. The results show that compared with BP, CNN has better performance in connectivity, average absolute relative deviation (AARD) below 10.01% higher. Moreover, CNN prediction results are close to the traditional methods. This paper provides new insights and methods to evaluate the interwell connectivity in conventional or unconventional reservoirs. |
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| ISSN: | 1468-8115 1468-8123 |