Artificial neural network models for determining relative permeability of hydrocarbon reservoirs
AssigneeSaudi Arabian Oil Company
InventorSaud Mohammad A. Al-Fattah
Life of this patent
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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
Claims21
A system for determining an actual relative permeability value for reservoir rock in a hydrocarbon reservoir comprising:
- a processor for receiving, storing and processing actual reservoir data corresponding to the characteristics of the hydrocarbon reservoir, the processor including:
- a trained generalized regression neural network trained using test reservoir data and test relative permeability values, with the trained generalized regression neural network for processing the actual reservoir data to determine a relative permeability prediction of an actual relative permeability in the hydrocarbon reservoir from the actual reservoir data;
- and
- an output device for outputting the relative permeability prediction.
A computer program product for determining an actual relative permeability in a hydrocarbon reservoir, the computer program product comprising a non-transitory computer readable medium having computer readable program code embodied therein that, when executed by a processor, causes the processor:
- to establish a plurality of computing nodes trained from test reservoir data and test relative permeability values, whereby the plurality of computing nodes, after training, processes actual reservoir data to determine a relative permeability prediction of an actual relative permeability in the hydrocarbon reservoir from the actual reservoir data;
- and
- to output the relative permeability prediction.
A method for determining an actual relative permeability value for reservoir rock in a hydrocarbon reservoir comprising the steps of:
- training a generalized regression neural network using test reservoir data and test relative permeability values;
- receiving actual reservoir data corresponding to the hydrocarbon reservoir;
- inputting the actual reservoir data to the trained generalized regression neural network;
- determining a relative permeability prediction of an actual relative permeability in the hydrocarbon reservoir from the actual reservoir data;
- and
- outputting the relative permeability prediction through an output device.
Dependencies are inferred from the claim text.