--> Abstract: Integrated Stochastic Modeling and Reservoir Technique for Project Evaluation and Risk Assessment, by K. J. Tyler, C. Sandsdalen, L. M'Land, J. O. Aasen, E. Siring, and M. Barbieri; #90951 (1996).

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Abstract: Integrated Stochastic Modeling and Reservoir Technique for Project Evaluation and Risk Assessment

K. J. Tyler, C. Sandsdalen, L. M'Land, J. O. Aasen, E. Siring, M. Barbieri

Uncertainties in geosciences are often down-played or neglected at the early stages of field development. In traditional reservoir studies a single reservoir description and set of dynamic resevoir properties are used for reservoir simulation. However, in the offshore field presented, large distances between exploration wells and the poor quality of seismic data resulted in little data for field appraisal and field development planning. Creative techniques for integrating the available data for better understanding of the dynamic behavior of the reservoir and quantification of uncertainty have been applied.

A multidisciplinary modeling approach has been used to achieve a consistent assessment of the reservoir uncertainties for a field in project evaluation. The uncertainty in in-place hydrocarbon pore volumes, including uncertainties in petrophysical properties, contact depths, segmentation, and depth conversion were included in this uncertainty. Stochastic modeling for representation of the uncertainty in sedimentology, petrology, petrophysics and geology for this tidal dominated estuarine environment. These uncertainties have been quantified in 99 realizations where ranking has been used to establish P10, P50 and P90 cases for further integration into dynamic simulation and Monte Carlo simulation.

P10, P50 and P90 scenarios of heterogeneities, hydrocarbon pore volumes, and other reservoir technical parameters (i.e., skin, relative permeability and fluid properties) have been combined in dynamic reservoir simulations. Regression analysis of these simulations allowed for assessment of the uncertainties for plateau length and recoverable reserves. Monte Carlo simulation incorporating uncertainty distribution functions of the uncertain parameters was then used to obtain P10, P50 and P90 production profiles used for economical project assessment and project planning.

AAPG Search and Discovery Article #90951©1996 AAPG International Conference and Exhibition, Caracas, Venezuela