Impact of Data Reconciliation at Various Scales in the Construction of Reservoir Model

Abstract

Abstract Reservoir model is widely used in oil and gas industry for hydrocarbon resources assessment, development, and management with different depletion strategies. The reservoir model is built through integration of multi-disciplinary information, data and interpretation at various scales. Integration of data from various resolutions and scales is usually a big challenge in constructing reservoir model due to its impact to the model's ability to make a reliable production forecast. The objective of this study is to analyze the impact of scale changes in clastic reservoir modelling and to evaluate its implication to hydrocarbon volume in-place and fluid flow behavior. Several examples from clastic reservoirs of different geological environments were evaluated and data from various scale were incorporated in reservoir description in constructing a representative reservoir models. Reconciliation of various reservoirs properties using database such as core data, logs, DST and production/pressure were performed. Permeability upscaling was observed posing significant challenges compared to the other properties at each stage. Therefore, the paper puts more emphasize on permeability while briefly discuss the other properties. Other challenges including complex reservoir types such as thinly laminated reservoirs are also evaluated. The study demonstrates that different permeability modeling methods may give significant impact on the hydrocarbon in-place and fluid flow characteristic. In the absent of production data to verify the in-place, the uncertainty of in-place is inevitable. In addition to that, those different permeability models may also give different flow characteristic. It is concluded that by recognizing the scale difference and impact of averaging/upscaling with the guidance from production/pressure performance, robust reservoir model with representative reservoir properties could be achieved. This paper shares the best practices in integrating data from various scale/discipline and highlights the impact of the data integration at right scale in constructing robust reservoir model.