Forecasting IOR/EOR Potential Based on Reservoir Parameters

The oil and gas industry has accumulated significant experience in carrying out improved/enhanced oil recovery (IOR/EOR) projects. The outcome of different IOR/EOR methods applied to the fields world wide is available in open data sources as well as internal databases of oil companies. Open data typically include general information about oil field, average reservoir and fluid properties (reservoir parameters), and efficiency of an IOR/EOR method applied. Statistical analysis of such data may be applied to evaluate IOR/EOR potential of a particular field. Since such analysis requires small amount of information on a field, it is suitable for screening of large number of fields or evaluation of new discoveries or acquisitions. A new statistical approach to predict efficiency of different IOR/EOR methods for particular reservoir parameters has been developed and tested on actual filed data. The approach utilizes multi-dimensional statistical analysis based on data clustering. The K-means clustering is used to partition a filed case database into clusters based on reservoir parameters. A set of six representative parameters (porosity, permeability, depth and oil density, viscosity and temperature) has been chosen based on parameter correlation studies described in literature. Visualization of the cluster analysis results is performed via projection of six-dimensional vectors into two-dimensional space using the principal component method. IOR/EOR potential for a new field case is evaluated into two steps: (1) association of the case with a nearest cluster utilizing the discriminant analysis and (2) multi-dimensional interpolation of recovery factor for different methods within the cluster. A quality control is carried out at all stages of the statistical analysis to confirm its reliability. A list of potential methods with an estimation of recovery factor classified according to the confidence index (a measure of reliability) is outputted as a result of the analysis performed. Described algorithm was coded and tested on actual field case databases. The tests have shown good quality and reliability of the results obtained at all stages of the analysis. The testing has also revealed that reliable evaluation of IOR/EOR potential is possible for databases containing at least ten field cases with a particular method. Application of the new approach may serve as an IOR/EOR compass when potentially efficient methods have to be identified. A database containing actual field data and/or results of laboratory experiments and reservoir simulations may be used as an input for such analysis.