Importance of conceptual geological models in 3D reservoir modelling

Abstract

Conceptual Geological Models (CGM’s) represent geological knowledge and are traditionally visualized by 2D geologic crosssections. Construction of CGMs is a non-linear and complex process which involves application of geological rules and experience. These models describe essential features of geological situations, illustrate the principal processes of the petroleum system, and provide important information about reservoir characteristics, pressure and fluid flow of the field under study. The CGMs are being extensively used as the key input for 3D reservoir modelling and simulation at different stages of E&P projects. A fully integrated CGM building process consists of at least four stages: construction of one or more structural models, identification of depositional models, construction of sedimentary facies models and finally building of diagenetic facies models. This process requires integration of geological, geochemical, geophysical, and petrophysical data along with information from well testing and production into a comprehensive model describing the physical features of the geologic system. This paper briefly describes the different components of CGM and the model building process. The importance of integrating CGM in the 3D reservoir models has been demonstrated through an example of siliciclastic reservoir. This reservoir has complex internal sedimentary distribution and stratigraphy which leads to high subsurface uncertainties. The study shows that different possible conceptual geologic scenarios provide significantly different Hydrocarbon-In-Place, recoverable resources and production forecasts. Therefore, the successful application of appropriate CGM’s is critical for better 3D reservoir characterization and modelling which enables us to identify and rank the key reservoir uncertainties and assess their impact, establish interdependency of spatial property distribution, make volumetric assessments, plan the number and location of wells including their drainage area, optimize recovery efficiency and obtain reliable production forecasts.