Quantitative Evaluation Method with Ridge Regression for Injected Water Composition in Low-permeability Reservoirs

doi:10.3969/j.issn.1006-6535.2022.04.018

Abstract

Abstract: Aiming at the problems of ineffective water injection in Putaohua reservoir, Block Chao 5, Daqing Oilfield, low calculation efficiency of conventional numerical simulation and complicated evaluation procedure, the composition of injected water was analyzed and a full stratigraphic geological model was established. Firstly, the composition of injected water was quantified with numerical simulation method, then a quantitative evaluation model for injected water composition in blocks with high injection-to-production ratio was constructed by ridge regression method on the basis of geological development factors affecting the injected water, and the prediction results of the model were compared with the results of numerical simulation, which proved that the model constructed by ridge regression was more reliable. The results of the study showed that, the water absorption ratio was 55% in the reservoir in Block Chao 5, 28% in the non-zoned reservoir interval and 8% in mud shale, and the water overflow was 12%; the established quantitative evaluation model for injected water composition could accurately characterize the injected water composition in Block Chao 5; compared with the numerical simulation, the ridge regression model could greatly improve the evaluation efficiency with little difference in calculation accuracy. There is much for reference of the study results for improving the water-flooding development effect of oilfields and guaranteeing oilfield development.

Key words: low-permeability reservoir, high injection-production ratio, quantitative evaluation of water injection composition, ineffective water injection, machine learning, Daqing Oilfield

CLC Number:

TE348

Zheng Xianbao, Wang Hongwei, Miao Zhiguo, Li Meifang. Quantitative Evaluation Method with Ridge Regression for Injected Water Composition in Low-permeability Reservoirs[J]. Special Oil & Gas Reservoirs, 2022, 29(4): 128-134.

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