One critical step towards building accurate reservoir simulation models is to efficiently assess the quality of the model's history match through identifying discrepancies between historical data and simulation model output to assess the history match quality. The role of Business Intelligence in history matching is crucial as it easily helps simulation engineers mine and manipulate data, visualize plots, identify patterns and run statistical algorithms. The History Match Quality Check (HMQC) tool capitalizes on these techniques and provides the required procedures to perform qualitative and quantitative analysis for simulation results in an efficient manner.

The developed tool helps engineers graphically identify wells, or cluster of wells, with discrepancies in pressure and water-cut trend, and offer a wide range of flexibility to rank simulation runs according to history match quality. One of the main advantages of the tool is that it can load data from different database sources as it combines both historical data and simulation output in one platform, and guide engineers to perform necessary adjustments to achieve a better history match. The HMQC tool is a dashboard which provides several tabs primarily for the main history match parameters such as first water or breakthrough timing, last and water-cut deviation and pressure evolution. Moreover, it allows engineers to extract statistical information from the simulation runs for quality check purposes and examine how close the model represents the field/reservoir performance. The tool uses advanced statistical algorithms to estimate the simulated properties' trend based on well performance, including first water breakthrough, water-cut and pressure trend against historical data.

This paper explains the algorithms used in the HMQC tool that utilizes Business Intelligence data mining and visual analytics to enhance and streamline the history match process.

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