Using Least Square Support Vector Machines to Approximate Single Phase Flow
- He Zhong (University of Calgary) | Keliu Wu (University of Calgary) | Dongqi Ji (University of Calgary) | Zhangxing Chen (University of Calgary)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 79th EAGE Conference and Exhibition, 12-15 June, Paris, France
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 6.1.5 Human Resources, Competence and Training, 6.1 HSSE & Social Responsibility Management, 5.5 Reservoir Simulation, 6 Health, Safety, Security, Environment and Social Responsibility, 7.6.6 Artificial Intelligence, 7 Management and Information, 7.6 Information Management and Systems, 5 Reservoir Desciption & Dynamics, 7.6.7 Neural Networks
- Least Square Support Vector Mechine, Machine Learning, Artificial Neural Network, Reservoir simulation, Single Phase Flow
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- 128 since 2007
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Data driven modelling has earned more and more attention in oil and gas industry, but most of these models have been applied to make decision or estimate correlation between properties. This paper proposed an approach to simulate the reservoir without reservoir simulation process.
The complete reservoir model consists of partial differential equations, boundary and initial conditions, expresses the information of the reservoir behaves. The Least Square Support Vector Machines (LS-SVM) is applied to train the flow model with the feed of the partial differential equations, whereas the initial and boundary conditions as act as constraints of an optimization problem. Only the kernel of the support vector machines is used to demonstrate the flow model without explicitly computing the feature mapping function.
Specific modification is required to deal with Neumann boundary conditions since the derivatives of the kernel function do not necessarily satisfy the Mercer theorem. Artificial Neural Network (ANN) is presented to modify LS-SVM formulation to Single phase flow problem. With the advent of artificial intelligence and data science the method becomes particularly interesting due to the expected essential gains in the execution speed.
|File Size||1 MB||Number of Pages||9|
SPE Argentina Exploration and Production of Unconventional Resources Symposium held in Buenos Aires, Argentina, 2016. Shahab D. Mohaghegh, O Grujic, and S Zargari. Modeling, History Matching, Forecasting and Analysis of Shale Reservoirs performance Using Artificial Intelligence. SPE Digital Energy Conference and Exhibition, (September), 2011.