PREDICTION OF RESERVOIR PROPERTIES OF OIL-GAS RESERVOIRS WITH THE APPLICATION OF THE THEORY OF FUZZY INFERENCE
The article considers an extremely topical and scientifically significant task, which is the basis for calculating the reserves of hydrocarbon raw materials – the fulfillment of the forecast of the filter and reservoir parameters of oil and gas reservoirs based on seismic attributes. In the process of studying and developing oil and gas fields characterized by a complex geological environment, the lack of correlation between the parameters characterizing it and the present «blurring» of the initial data, uncertainty and multivariance are inevitable as a result of modeling the forecast performance parameters. In this case, the uncertainty is additionally caused by the incompleteness of the actually measured data, in combination with the complex structure of the geological media, as well as by the presence of zones of local inhomogeneity in the reservoir-filtration characteristics of the reservoir. An obligatory element of the forecast is the estimation of the reliability and the width of the uncertainty area of the forecast parameter associated with it. The article proposes to perform a forecast based on the technology of fuzzy inference. For this purpose, the apparatus for forming relations between fuzzy forecast parameters and parameters of seismic attributes is developing. The forecast of filtering and capacitive parameters breaks down into a number of sub-stages: first of all, the development of those parameters that, based on materials on the reference area, best ensure the quality of the forecast of the filter and capacitance parameters estimated by the magnitude of the forecast error and then it is necessary to produce the training forecast rules and their adjustment on the reference area. The application of this method is considered in the article by the example of predicting the oil saturation coefficient for the interval time of passage of a longitudinal wave. The area of uncertainty and the level of reliability are calculated by the method of alpha cross sections for the proxy parameter membership function.