Computational advances in reservoir simulation have made possible the simulation of thousands of reservoir cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases remains challenging. This paper presents a new method that enables the efficient analysis of massive reservoir-simulation results by discovering interesting patterns of relationships among variables in large data sets. The method uses association-rule mining together with high-dimensional visualization.
Ensemble-based approaches for reservoir modeling and simulation have been investigated for decades. The majority of the methods are designed to explore a high-dimensional space spanned by uncertainty parameters and find a set of reservoir models that reproduce historical production performance. Once an ensemble of history-matched models is obtained, the effect of subsurface uncertainty on production forecast is evaluated quantitatively by simulating flow performance on individual members of the model ensemble. Exploration of the parameter space during the model calibration is conducted with various stochastic algorithms. Major research efforts on these approaches are devoted to achieving efficient sampling from the parameter space and obtaining predictive capability to capture the range of uncertainty related to geological uncertainty remaining after data assimilation. However, little attention has been paid to qualitative analysis of the effect of geological features on simulated production performance.
One obvious reason for the underutilization of the model ensemble for understanding reservoir sensitivity is the lack of efficient methods to visualize simulation results in such a way that interaction among multiple uncertainty parameters and simulated production response can be revealed rapidly. This paper presents a novel methodology that serves this purpose by coupling a well-known data-mining algorithm, called association-rule mining, with an existing high-dimensional visualization method called dimensional stacking....
Simulation Analysis With Association-Rule Mining Plus High-Dimensional Visualization
01 July 2016