Granular Computing for Pumping Well Parameter Optimization

doi:10.11885/j.issn.1674-5086.2020.05.29.02

Abstract

Abstract: Aiming at the problem of optimization of pumping unit parameters in oil and gas production, a granular computing method is proposed based on the data of pumping unit production control and maintenance measures. In this paper, machine learning methods such as granular computing, cost-sensitive rough sets and recommendation systems are used. Firstly, a decision tree is employed to build a multi-granular fusion model based on the time, space, and business level of the pumping well data. Secondly, a suitable evaluation model is defined for the pumping well business with the cost-sensitive rough set. Finally, a recommended model of core parameters and maintenance measures is designed based on field-aware factorization machine under cost constraints. Based on real oil-gas production data, we have designed comparative experiments with different granularities. We adjusted the production parameters of the pumping well from coarse granule to fine granule, and found that the recommendation accuracy for optimizing the core parameters of production is gradually increased first and then gradually decreased. We can conclude that in parameter optimization, appropriate granularity selection is required.

Key words: pumping well parameter optimization, machine learning, multi-granularity fusion model, cost-sensitive rough set, recommender system

CLC Number:

ZHANG Hengru, ZHU Kelin, XU Yuanyuan, QIAO Ying. Granular Computing for Pumping Well Parameter Optimization[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 115-123.

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http://journal15.magtechjournal.com/Jwk_xnzk/EN/Y2020/V42/I6/115

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