The Big Clifty (Jackson) Sandstone Member of the Golconda Formation is the most important of the Mississippian (Chesterian) heavy-oil reservoirs in the southeastern Illinois Basin. Heavy oil reservoirs, or asphalt rock deposits, have been studied extensively in south central and western Kentucky, and ~2 billion barrels of original oil in place (OOIP) have been proposed to occur in the Big Clifty Sandstone. Despite high OOIP estimates, heterogeneities in the reservoir negatively impact the production of heavy oil deposits. Heterogeneities related to depositional facies changes are poorly understood in the Big Clifty Sandstone of Kentucky, where it has been mostly described as a 60-120 feet thick sandstone unit. In some locations, the Big Clifty occurs as two distinct sand bodies with intercalated mud-rich units and, most typically, with the greatest clay- and silt-rich units present between sandstone bodies. Questions exist as to how such muddy facies occur in the reservoir. This study couples sedimentary facies analysis with sequence stratigraphy to assess how lithological factors affect the occurrence of petroleum in Big Clifty reservoirs. Multiple datasets were integrated to develop a depositional model for lithologic facies observed in this study. Datasets include core, exposure descriptions, petrographic analysis, bitumen concentrations, electrical resistivity tomography (ERT), and borehole geophysical analysis. This study occurred in Logan, Warren, and Butler counties, with emphasis on an active asphalt-rock mine in Logan County. Surface geophysical methods aided in demarcating Chesterian limestones, sandstone bodies and, in particular, highly resistive heavy-oil laden Big Clifty channel bodies. In Warren County, located E-NE of the Stampede Mine, the Big Clifty coalesces into a single amalgamated sandstone channel or a series of superimposed stacked channels as observed in outcrop along Indian Creek at McChesney Field Station and at Jackson’s Orchard. In these locations, the tidal influence is subtle with large-scale trough cross bedding dominating, and the contact on the Beech Creek Limestone is sharp. Facies changes related to the environment of deposition greatly impact the quality of heavy-oil reservoirs and must be taken into consideration during exploration and siting of asphalt rock mines.

Most investments in the oil and gas industry involve considerable risk with a wide range of potential outcomes for a particular project. However, many economic evaluations are based on the &ldquo / most likely&rdquo / results of variables that could be expected without sufficient consideration given to other possible outcomes and it is well known that initial estimates of all these variables have uncertainty. The data is usually obtained during drilling of the initial oil well and the sources are geophysical (seismic surveys) for formation depths and areal extent of the reservoir trap, well logs for formation tops and bottoms, formation porosity, water saturation and possible permeable strata, core analysis for porosity and saturation data and DST (Drill-Stem Test) for possible oil production rates and samples for PVT (Pressure Volume Temperature) analysis to obtain FVF (Formation Volume Factor) and others. The question is how certain are the values of these variables and what is the probability of these values to occur in the reservoir to evaluate the possible risks. One of the most highly appreciable applications of the risk assessment is the estimation of volumetric reserves of hydrocarbon reservoirs. Monte Carlo and moment technique consider entire ranges of the variables of Original Oil in Place (OOIP) formula rather than deterministic figures. In the present work, predictions were made about how statistical distribution and descriptive statistics of porosity, thickness, area, water saturation, recovery factor, and oil formation volume factor affect the simulated OOIP values. The current work presents the case of two different oil fields in Turkey. It was found that both techniques produce similar results for 95%. The difference between estimated values increases as the percentages decrease from 50% and 5% probability.