Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-15
Main Authors: Qin, Songhai, Liu, Jianyi, Yang, Xinping, Li, Yiyang, Zhang, Lifeng, Liu, Zhibin
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Back propagation networks
Computer simulation
Crude oil
Energy consumption
Fractured reservoirs
Geology
Hybrid systems
Machine learning
Methods
Neural networks
Oil fields
Oil recovery
Oil wells
Optimization
Parameter identification
Petroleum production
Prediction models
Production methods
Recovery
Simulation
Support vector machines
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5558623