Until recently, reservoir characterization methods in the industry were limited to use of seismic technologies in exploration of oil and gas and had a very constrained role in production and development. In the past, using characterization for development fields was considered a very perilous task. Technological advancements and the risk-averse mindset have significantly expanded the application of reservoir characterization. Today, reservoir characterization is the basis of any development plans made for a commercial field.

Development of 3D reservoir modeling techniques to generate field development plans (FDPs) marked a step-change in reservoir characterization methods. Introduction of geostatistics and numerical simulation made it possible to build precise models to generate realistic field development scenarios. This is the state-of-the-art seismic-to-simulation method of reservoir characterization used in FDPs today. However, the struggle to estimate reservoir properties spatially away from the well continues.

Surface seismic data provide excellent areal coverage but do not provide the vertical resolution required for a fine-scale reservoir model. Geostatistical methods reduce the uncertainty in spatial distribution of petrophysical properties from pseudo-point supports (wells) but are not calibrated spatially between the wells. Correspondingly, the fluid saturation distribution and the parameters used in dynamically calculating the same during numerical simulation are not calibrated in the interwell space.

This paper details necessary data acquisitions and methods of calibration of 3D reservoir model to reduce uncertainty in the interwell space. The data acquisition methods have been available for some time, but have rarely been electronically incorporated in the 3D reservoir model and have been largely used to analytically guide the modeling and its inferences. A logical way of interpreting the results of acquisitions and calibrating the 3D reservoir model cell-by-cell is detailed in this paper.

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