Assessment of Static Models by Performing Fast-Track History Matching

Abstract Implementation of robust geological analysis for static reservoir characterization does not provide a guarantee about the prediction of dynamic behavior in the reservoirs. The reservoir properties are often populated using geostatistical methods in a static model. These methods are dependent on input data density and quality. The fluid movement inside this reservoir is simulated during dynamic modeling. In the case of many complex reservoirs, it is observed that the dynamic model response doesn't match with observed historical well data and modification in a static model is needed. This loop of dynamic-static modeling impacts the project timeline significantly. The objective of this work is to provide a dynamic validation tool used in a very early stage to perform quick assessments of the static model by doing a fast-track history matching. In this paper we have developed a solution that provides fast-track assessment of static models to check the response of dynamic properties before starting the full history-matching exercise. The solution uses the static reservoir properties and observed data as its main input. It provides a list of fluid properties based on some basic fluid types. It enables use of core constants to define special core analysis (SCAL) function and rock compressibility to define rock compaction. After running the fast-track simulation, the key performance indicators (KPIs) are checked to assess the quality of the model. The liquid production rate and pressure in all the wells have been chosen as KPIs to demonstrate the impact of static model properties on the dynamic model. A mismatch analysis is performed on these KPIs in all the wells. The wells are classified as good, acceptable and needs improvement, categories based on different thresholds in mismatch analysis. This mismatch is analyzed in map view, which is used to identify key challenges in static model for improvement. This approach provides a quick assessment of static models and their modifications before starting the detailed dynamic modeling exercise. It saves significant time in the projects, enabling geoscientists to have an early evaluation of the behavior of the models from the dynamic point of view, reducing the number of future iterations in the history-match phase.