Mining a Massive Reservoir Engineering Database for Determinants of Recovery Efficiency

Abstract

A global and industry wide dataset was data mined for determinants of recovery efficiency. Understanding of the factors that are driving variance in reservoir performance is essential for benchmarking current performance and for the screening of new opportunities. The following insights in the origin of variance in reservoir performance could be extracted from this analysis. Global trends for recovery factor with drive mechanism, reservoir type, geological age, lithology and depositional environment were extracted through subgroup analysis. Other property trends, such as porosity with depth and geological age, were found to be basin specific. The internal structure of the database and correlations was revealed through principal component analysis. Relative importance of the predictor variables was determined using automatic multivariate linear regression. It was found that the predominant variables include: API gravity, permeability and reservoir temperature. Additional data was identified through combination of literature review, dimensional- and statistical analysis. The following variables are suggested: dip angle, flow rate, fractional water cut, and pressure drop. Furthermore continuous scales for heterogeneity and fracture intensity, especially for carbonate reservoirs are suggested. To express the confidence level for each reservoir in the database, categorical variables for maturity and data quality are proposed. This research forms the basis for future data mining of the dataset and further improvement of the TQ EUR TOOL in which the data is stored. In a wider context this report presents a high level overview of observations on reservoir performance based on actual reservoirs worldwide rather than laboratory data or theory.