Uncertainty in attribute-based inversion for quantification of the estimation of stock-tank oil initially in place in Kalol reservoir, Cambay Basin, India

Abstract

Uncertainty is not an inherent feature of a reservoir; it is the result of lack of knowledge and understanding about the reservoir. Uncertainty can be modeled, but there is no objective measure of uncertainty. All geostatistical methods used in estimation of reservoir parameters are inaccurate; hence, modeling of “estimation error” in the form of uncertainty analysis is important. Uncertainty is associated with each process involved in geologic modeling, including data acquisition, data processing, interpretation, structural modeling, facies modeling, petrophysical modeling, and volume estimation, which affects the ability to understand the reservoir behavior and make reliable production forecasts and risk-free decisions. Hence, uncertainty analysis is a prerequisite measure for initial calculation of oil in place in any field. In the estimation of stock-tank oil initially in place (STOIIP) in Kalol reservoir, Cambay Basin, India, a geologic uncertainty study was initiated to identify and quantify the input parameters of greatest impact in the reservoir model. Results showed that the structural uncertainty has maximum impact and volume expansion factor has minimum impact in the estimation of stock-tank oil initially in place, whereas lithologic and petrophysical uncertainties have equal impact. Hence, during structural, facies, and petrophysical modeling, one has to take special precaution before finalizing the reserve calculation. Therefore, uncertainty and sensitivity analysis in the estimate of stock-tank oil initially in place should be applied on a routine basis because it greatly helps in improving fluid estimate from the field and ultimately the economics of the investments.