Enhancing Reservoir Understanding by Utilizing Business Intelligence in History Match Quality Check

Abstract

The technology to process and analyze simulation model outcomes have improved exponentially in the past few years and gave engineers the ability to analyze results of simulation runs efficiently and effectively. Reservoir simulation engineers need to quickly analyze simulation runs based on difference among models calculated data and measured data to determine the quality of the simulation models. With the help of business intelligence tools, engineers are able to do certain quality checks of the model that enhances reservoir fluid flow understanding. History match quality check dashboard provides the required means to perform qualitative and quantitative analysis for simulation runs. The developed reservoir engineering business intelligence tool helps engineers to extract statistical information of the simulation runs to quality check how close the model mimics historical performance. The tool provides means to quantitatively and qualitatively check critical well performance properties that include water cut, pressure, GOR and oil rate against the measured data. Using this tool, engineers will be able to identify wells (or cluster of wells) with issues in those parameters, allowing the engineer to rank simulation runs according to their history match quality. This paper will discuss the algorithms behind the history match quality check dashboard that utilizes advanced data mining and visual analytics. A case study will exemplify identifying problematic wells in the history matched pressure, water cut, and oil production rate for one of Saudi Arabia field.