Successful Application Of Different QI Techniques To Improve The Static Reservoir Model For A Clastic Onshore Oil Field In Oman

Abstract

Abstract A quantitative interpretation (QI) study has successfully predicted the distribution of sands and shales in the Upper Gharif. This was indisputably confirmed by three wells drilled after completion of the QI study. By integrating a range from low to high "tech" QI technologies, a workflow in a land environment could be developed that enables us to delineate channel sands with challenging rock properties at a fairly deep target. Integral part of this workflow comprehends the alignment of the petrophysical data, seismic data and production data in a consistent manner. Then, joining the maps from RMS amplitudes, spectral decomposition and seismic facies classification with products from the AVO inversion allowed us to construct a confidence map, which assigns areas with different probabilities to encounter these channel sands. Building further on this QI success, the first 3D Close-the-Loop (3DCtL) project in Petroleum Development Oman (PDO) ever was initiated. This methodology strives to obtain an accurate as possible static reservoir model, which is consistent with all available subsurface data. The workflow consists of two main steps. Firstly, a rock property model is derived from well data and then the reservoir properties (net-to-gross, porosity and hydrocarbon saturation) in the static model are used to generate synthetic 3D seismic data and compared with the measured surface seismic data. In case of disagreement, the second phase involves a stochastic seismic inversion which updates the reservoir properties and makes them consistent with the measured seismic data. While running through this workflow, a number of deficiencies in the static reservoir model came to surface. Besides improvement in the thicknesses of the layers away from well control and in the velocity model used for time-depth conversion, the main enhancements from the stochastic inversion amounts to more realistic net-to-gross and porosity estimates and improved insight in the sand distribution in the Al Khlata reservoir. Ultimately, the subsurface uncertainties are considerably reduced and result into improved reserve estimates. The significance for PDO is that although this technology is well established in most major oil companies, this is the first time it has been successfully applied on a PDO seismic data volume.