Prediction of Hydraulic Properties in Carbonate Reservoirs Using Artificial Neural Network

Abstract

Abstract An accurate estimation of porosity and permeability are extremely essential for designing an ideal and efficient program of an oil and gas field development. Numerous methods have been developed to determine the porosity and permeability including laboratory measurements and log derived models. Artificial neural network (ANN) provides an efficient technique that successfully addressed several engineering and geological challenges. In the present study ANN is applied to help in predicting porosity and permeability in carbonate reservoirs using back propagation neural network (BPNN) with high accuracy on well log data from numerous fields worldwide. ANN has the ability to understand a highly non – linear relationship and to perform simulation studies in a rapid manner. The BPNN model of porosity and permeability is developed using a set of well logging data as input layers and core porosity and core permeability as output layers. Two scenarios are considered to develop by ANN. The first scenario considers using all available logging data directly as input for ANN. In the second scenario an additional input, diagenesis parameter, is added as input to ANN which is essentially calculated from logging data. In each scenario two models are developed; the first for porosity prediction and the second predicts permeability in carbonate reservoirs. The optimal learning rate and momentum constant used in the BPNN model are achieved after serial combinative trials. The available data was assigned 80% for training and 20% for verification. The results of the developed porosity and permeability models are well compared to core data in verification. In the first scenario, cross-plot of the actual porosity versus ANN predicted Porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.80. In the second scenario, cross-plot of the actual porosity versus ANN predicted porosity exhibited a good match with a correlation coefficient equal to 0.97. Cross-plot of the actual permeability versus ANN predicted permeability exhibited a good match with a correlation coefficient equal to 0.98. Such data indicate that the developed models are successful in predicting the porosity and permeability for carbonate reservoirs.