Application of Locality Preserving Projection-Based Unsupervised Learning in Predicting the Oil Production for Low-Permeability Reservoirs
- Yuan Zhang (School of Energy Resources and Beijing Key Laboratory of Unconventional Natural Gas Geology Evaluation and Development Engineering, China University of Geosciences (Beijing)) | Jinghong Hu (School of Energy Resources and Beijing Key Laboratory of Unconventional Natural Gas Geology Evaluation and Development Engineering, China University of Geosciences (Beijing)) | Qi Zhang (University of International Business and Economics, Beijing and Key Laboratory of Machine Perception (Ministry of Education), Peking University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2020
- Document Type
- Journal Paper
- 2020.Society of Petroleum Engineers
- production forecast, locality preserving projection, machine learning
- 18 in the last 30 days
- 143 since 2007
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Predicting well production in unconventional reservoirs has attracted much attention in recent years. However, it is still a challenge because of the heterogeneity of the unconventional formation and the uncertain physical properties of rock and the fluid-flow mechanism. Therefore, the objective of our study is to develop an efficient method for production forecast in low-permeability reservoirs. In this paper, we applied a locality preserving projection (LPP) -based machine-learning method to provide a novel way of predicting production in low-permeability reservoirs. Our model preserves the local geometric information inherent in the oil data and hence is capable of seizing its intrinsic nonlinear feature. Through LPP-based analysis, specific parameters (e.g., the total fluid pumped) are verified to have more important impact than others on estimating oil production. We conducted both parameter testing and comparison experiments, with results indicating that the LPP-based method has good applicability and efficiency in reasonably forecasting well production from low-permeability reservoirs. This work provides petroleum engineers an effective method for the prediction of oil production.
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