Invention Grant
- Patent Title: Artificial neural network models for determining relative permeability of hydrocarbon reservoirs
- Patent Title (中): 用于确定油气藏相对渗透率的人造神经网络模型
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Application No.: US12733357Application Date: 2008-08-27
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Publication No.: US08510242B2Publication Date: 2013-08-13
- Inventor: Saud Mohammad A. Al-Fattah
- Applicant: Saud Mohammad A. Al-Fattah
- Applicant Address: SA Dhahram
- Assignee: Saudi Arabian Oil Company
- Current Assignee: Saudi Arabian Oil Company
- Current Assignee Address: SA Dhahram
- Agency: Abelman, Frayne & Schwab
- International Application: PCT/US2008/010285 WO 20080827
- International Announcement: WO2009/032220 WO 20090312
- Main IPC: G06F15/18
- IPC: G06F15/18 ; G06N7/06

Abstract:
A system and method for modeling technology to predict accurately water-oil relative permeability uses a type of artificial neural network (ANN) known as a Generalized Regression Neural Network (GRNN) The ANN models of relative permeability are developed using experimental data from waterflood core test samples collected from carbonate reservoirs of Arabian oil fields Three groups of data sets are used for training, verification, and testing the ANN models Analysis of the results of the testing data set show excellent correlation with the experimental data of relative permeability, and error analyses show these ANN models outperform all published correlations
Public/Granted literature
- US20100211536A1 ARTIFICIAL NEURAL NET WORK MODELS FOR DETERMINING RELATIVE PERMEABILITY OF HYDROCARBON RESERVOIRS Public/Granted day:2010-08-19
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