Predictions of reservoir rock quality and distribution are commonly one ofthe major uncertainties in wildcat drilling. The need for improved prediction of reservoir quality has been documented by Rose (1987) and Sluijk and Parker (1984). Rose notes, in one of the few reported examples of assessment of wildcat failures, that incorrect prediction of commercial reservoir rock was the main reason for 40% ofthe dry holes analyzed. Interestingly, the geologists involved in reservoir quality assessment correctly perceived this factor as the prime geologic uncertainty 79% of the time. Comparison of predrill predictions with postdrill results by Shell (Sluijk and Parker) indicated that, in general, reservoir parameters were seriously overestimated, whereas hydrocarbon charge and retention were reasonably well predicted.
In addition to the reduction of exploration risk, there are other potential applications for this course. Examples include basin analysis, where improved porosity and permeability estimation will allow for more detailed assessment of large scale fluid flow patterns, and in hydrocarbon production, where predrill predictions may assist in the recognition of impaired productivity related to formation damage. Although the concepts, techniques, and examples presented in this course relate almost entirely to clastic rocks, there is no reason the approach advocated here cannot be successfully applied to carbonates.
Geochemical models of the various physical and chemaical mechanisms of diagenesis are only rarely linked quantitatively to levels of porosity and permeability. Consequently, attempts to use such models to predict reservoir parameters do not yet have the ability to provide meaningful quantitative estimates in other than
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Reservoir Quality Assessment and Prediction in Clastic Rocks
This course is designed to emphasize the following topics: (1) Historical perspective on previous and current empirical, and geochemical methods of reservoir quality prediction; (2) Overview of diagenetic processes which significantly impact reservoir quality and those factors which act as major controls on those processes; (3) Proper design of a comprehensive or limited-focus predictive analysis of reservoir quality; (4) Methodologies for the accurate measurement of all major dependent and independent variables; (5) Data analysis techniques involved in quality control and the assessment of variability prior to performing multivariate regression; (6) Steps involved in the generation of a multivariate regression to insure that the model developed provides maximum accuracy using a minimum number of independent variables; (7) Case histories from a variety of settings illustrating application of the recommended approach to reservoir quality prediction.