A New Artificial Intelligence Recognition Method of Dominant Channel Based on Principal Component Analysis
- Cunliang Chen (Tianjin Branch of CNOOC Co., Ltd) | Xiaodong Han (Tianjin Branch of CNOOC Co., Ltd) | Ming Yang (Tianjin Branch of CNOOC Co., Ltd) | Wei Zhang (Tianjin Branch of CNOOC Co., Ltd) | Xiang Wang (Changzhou University) | Peng Dong (China University of Petroleum)
- Document ID
- Society of Petroleum Engineers
- SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, 29-31 October, Bali, Indonesia
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Principal Component Analysis, Dominant Channel, Artificial Intelligence
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Long term waterflooding leads to the formation of dominant channels in sandstone oil reservoirs, which aggravates the heterogeneity of the reservoir and decreases the displacement performance of the injected water. The ineffective water circulation through the dominant channel would significantly increase the cost of water injection and reduce the oilfield exploitation effciency. Therefore, valid identification and controlling of the dominate channel are essential for enhancing the oil production efficiency of waterflooding reservoirs. Although many methods have been proposed to identify the dominant channel, the accuracy of these methods is always unsatisfactory caused. The results obtained by using various methods are not consistent with the others.
A new method that can comprehensively utilize multiple data is proposed here for improving the identification accuracy of dominate channels. The formation of the dominant channel is affected by both geological factors and development factors. And all these parameters change during the waterflooding process and could reflect the formation of dominant channels. In our method, the evaluation index system consisted of both geological factors and development factors that are firstly formed and analyzed. Consequently, the principal component analysis (PCA) method is applied to aggregate the multiple independent indexes into a comprehensive index. The calculated relative value of the comprehensive index is then considered as the assessment criterion to identify the dominant channel. As an artificial intelligence method, PCA is widely used for reducing dimension statistics. This method has good advantages in the identification of the dominant channel since it can take various data into consideration and reduce subjectivity during the identification process.
The proposed method has been applied in several oilfields for dominant channel identification, and the results are entirely satisfactory. Accurate identification of dominant channels is helpful for the design of an effective adjustment plan, which could provide technical support for achieving higher production efficiency and better economic benefits.
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Ding Shuaiwei, Jiang Hanqiao, Wang Lei, 2015. Identification and Characterization of High-permeability Zones in Waterflooding Reservoirs with an Ensemble of Methodologies. Paper 176158 presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition held in Bali, Indonesia, 20-22 October.