Abstract
This paper presents a field scale reservoir evaluation and uncertainty analysis of hydrocarbon reserves estimation for the Upper Morrow B reservoir of the Farnsworth Unit (FWU), Ochiltree County, Texas. The degree of uncertainty in volumetric reserves estimation for hydrocarbon in place is controlled in larger order by the geological complexity of the reservoir and quality of available geologic data.
Morrow B core and thin sections were examined to determine composition, porosity types, depositional environment and diagenetic history. Composition and porosity types determined from XRD and optical microscopy were compared with results from an ELAN analysis. This information together with additional core, well log, borehole image logs, vertical seismic profiles and 3D surface seismic data were used to characterize and subsequently create a fine scale lithofacies based geocellular model (Ampomah et al., 2016b). The reservoir is classified as a highly heterogeneous.
Probability density functions for input uncertain variables were constructed to estimate probabilistic reserves using first order, parametric and Monte Carlo simulation methods. The relative impact of input variables from these methods were compared and analyzed based on geology, petrophyiscs and engineering knowledge from the FWU to ascertain the applicability of these approaches.
The result for each method is presented with expectation curve and log-probability plot elaborating the likelihood of occurrence as P10, P50, P90 and Mean reserves. A P10-to-P90 ratio and a coefficient of variance were used to analyze the total uncertainty of the reserves estimation. Histograms were used to illustrate the sensitivity of input parameter’s contribution towards the overall uncertainty. Statistical mean reserves from the three methodologies were close to the deterministic calculations. Formation thickness and area were the most uncertain variables and have to be topmost priorities to ensure accuracy in reserves estimation for FWU.
The results from this study show that an analytical procedure such as the parametric method which is easily generated within spreadsheets can be used to replace the "black box" Monte Carlo simulation of estimating hydrocarbon reserves in the oil and gas industry.
