Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

Qin Songhai; Liu Jianyi; Yang XinpingLi YiyangZhang LifengLiu Zhibin

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Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

机译：Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

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It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2021年第37期|5558623.1-5558623.15|共15页
作者
Qin Songhai; Liu Jianyi; Yang XinpingLi YiyangZhang LifengLiu Zhibin;
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Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Sichuan, Peoples R China|CNPC, Dev Dept, Petro China Southwest Oil & Gasfield Co, Chengdu 610051, Sichuan, Peoples R China;

Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Sichuan, Peoples R China;

CNPC, Res Inst Explorat & Dev, Xinjiang Oilfield Co, Karamay 834000, Xinjiang, Peoples R ChinaCNPC, Xinjiang Oilfield Co, Heavy Oil Dev Co, Karamay 834000, Xinjiang, Peoples R China;

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Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

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