Fracture density determination using a novel hybrid computational scheme: a case study on an Iranian Marun oil field reservoir

Most oil production all over the world is from carbonated reservoirs. Carbonate reservoirs are abundant in the Middle East, the Gulf of Mexico and in other major petroleum fields that are regarded as the main oil producers. Due to the nature of such reservoirs that are associated with low matrix permeability, the fracture is the key parameter that governs the fluid flow in porous media and consequently oil production. Conventional methods to determine the fracture density include utilizing core data and the image log family, which are both time consuming and costly processes. In addition, the cores are limited to certain intervals and there is no image log for the well drilled before the introduction of this tool. These limitations motivate petroleum engineers to try to find appropriate alternatives. Recently, intelligent systems on the basis of machine learning have been applied to various branches of science and engineering. The objective of this study is to develop a mathematical model to predict the fracture density using full set log data as inputs based on a combination of three intelligent systems namely, the radial basis function neural network, the multilayer perceptron neural network and the least square supported vector machine. The developed committee machine intelligent system (CMIS) is the weighted average of the individual results of each expert. Proper corresponding weights are determined using a genetic algorithm (GA). The other important feature of the proposed model is its generalization capability. The ability of this model to predict data that have not been introduced during the training stage is very good.

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