Study on the Classification and Formation Mechanism of Microscopic Remaining Oil in High Water Cut Stage Based on Machine Learning
- Yuyun Zhao (China University of Petroleum) | Hanqiao Jiang (China University of Petroleum) | Junjian Li (China University of Petroleum) | Chuan Wang (China University of Petroleum) | Yajun Gao (China University of Petroleum) | Fuwei Yu (China University of Petroleum) | Hang Su (China University of Petroleum)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 13-16 November , Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 7 Management and Information, 7.6.6 Artificial Intelligence, 5.7.2 Recovery Factors, 7.6.4 Data Mining, 5 Reservoir Desciption & Dynamics, 5.7 Reserves Evaluation, 7.6 Information Management and Systems, 5.4 Improved and Enhanced Recovery, 5.4 Improved and Enhanced Recovery
- visualization technology, high water cut stage, machine learning, microscopic remaining oil, remaining oil classification
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- 190 since 2007
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The formation mechanism and utilization conditions of the remaining oil in the high water cut period play significant roles in improved tapping potential and enhanced oil recovery. The classification of the remaining oil is a difficult point, meanwhile a burning issue. However, the current classification method is mainly through the manual method to determine the boundaries of classification, time-consuming and has a great subjectivity.
Machine learning and data mining methods in recent years have been widely used in the field of petroleum engineering, such as prediction of the recovery factor and so on, especially the well-known k-means classification algorithm.
The first objective of this paper is to use the semi-supervised learning (SSL) method to realize the classification of remaining oil in the high water cut period, based on the database obtained from experiments of 2D etched glass micro-model, with the help of the technique of quantitative characterization of pore structure and micro-residual oil. The method of principal component analysis (PCA) is used to reduce the dimension of the data. According to the formation causes, remaining oil can be divided into four types: oil film, throat retained oil, heterogeneous multi-pores oil and clustered oil. Two typical blocks are identified manually for each class, with an increased weight coefficients, then the other oil blocks with smaller weights are clustered into their types by the seeded k-means algorithm. The result shows that semi-supervised method is more effective than both supervised learning (with manual boundaries) and unsupervised learning methods.
Based on the classification, the effects on the formation of heterogeneous multi-pores oil and throat retained oil are analyzed by statistical method. All of these quantitative studies can provide theoretical guidance for the use of residual oil in high water cut periods and increased oil recovery.
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