NEURAL NETWORK PERMEABILITY MODELING

NEURAL NETWORK PERMEABILITY MODELING www.sofoil.com

AGENDA

OBJECTIVES

SCOPE OF WORK

DELIVERABLES

RESERVOIR OVERVIEW F1 F2 F3 F4 F5

INPUT DATA PREPROCESSING Well scope 40 wells Porosity Core data6000 data vectors Permeability Core data 6000 data vectors Well logsGamma Ray (GR) Density (RHOB) Resistivity (RT) Porosity (PHIE) Training wells 35 Testing wells 5

INPUT LOGS AND SEGMENTATION Input data: PHIE Log10GR RHOB Log10RT • Porosity log is used • Porosity local minimums are chosen as segments boundaries • Segments boundaries are chosen so that each segment contains at least 20 points

CLUSTERING& PERMEABILITY EVALUATION Determination of parameter’s correlation Distribution of segments into clusters ρ - Spearman'scorrelationcoefficient Permeability calculation by neural network modeling for each cluster Output data - Permeability Input data ………….. for each cluster

RESULTS Better correlation Standard deviation STD = 0.6mD Correlation coefficient R=0.78 Standard deviation STD = 0.37mD Correlation coefficient R=0.91

ADVANTAGES • Any reservoir rock type • Automatic lithofacies analysis • Higher accuracy Neural network permeability modeling is useful for: • Several types of lithofacies • Carbonate rocks • High heterogeneous reservoir

RECOMMENDATIONS • Identification of criteria for the application of the method • Optimization of geophysical survey sets • Programs of coring and geophysical survey • Other recommendations

NEURAL NETWORK PERMEABILITY MODELING

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