International Science and Technology Journal

Cut-offs are required to exclude the reservoir portion that does not contribute significantly to evaluating hydrocarbon in situ or reserve estimation. There is no universally accepted set of definitions, but it is necessary to have a firm grasp on the fundamental terms and expressions used in volumetric analysis employing core, well-log data. Oil and gas are trapped in the Blackfoot Field's multiple fluvial and valley-fill reservoirs of the Glauconitic sandstone. Two wells, A-08-023-23W4 and B-08-023-23W4, were chosen to quantify parameters needed to estimate net pay thickness (NPT) and net-to-gross ratio from the cores data in both wells. Understanding charts, which show how average parameters change with a cut-off value, are used to determine the final cut-off. Using the statistical criterion for the assessed wells, an appropriate porosity limit for the net-to-gross ratio (NTG) estimate is being sought. A perspective on choosing porosity cut-off values from a statistical and core data perspective is provided. The cores used in this investigation were analyzed for permeability and porosity under controlled laboratory settings. It is possible to make inaccurate predictions when using least-squares regression to determine porosity (or permeability) cut-off values. Using a probabilistic method, Jensen and Menke assessed the precision and inaccuracy of various porosity cut-off values. To accomplish this, the line indicating the porosity cut-off values (Øc) was fine-tuned to minimize error and produce the most precise estimate possible. In this case study, we apply the tasks of estimating different porosity cut-off values to identify NPT and NTG and reduce the errors. A-8-23-23W4's Øc is 0.3 porosity unit (pu) off the best estimate ØBE values when using the least squares regression line fitted to the porosity and permeability, and the regression line cut-off errors are 2.7% higher. ØBE matches the least squares regression's NPT cut-off values for well B-8-23-23W4. The least regression line has a lower error rate than the best estimate for NTG, which is a 2.1 pu difference. According to the results, the NTG for well A-8-23-23W4 and well B-8-23-23W4 are predicted to be 0.9 and 0.8, respectively. The application of the Jensen and Menke statistical cut-off is contingent upon the reservoir type, and the optimal statistical method for assessing net pay should be combined with all relevant data and analyzed by geologists and engineers.................................... Key Words:............ Glauconitic sandstone, core data, Porosity cut-off, least square regression, Net pay thickness.