A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

We present and test a new screening methodology to discriminate amongst alternative and competing Enhanced Oil Recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques have been successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests prior to field-wide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Since similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria we consider fluid (density and viscosity) and reservoir formation (porosity, permeability, depth and temperature) properties. Our approach is observation-driven and grounded on an exhaustive data-base which we compile upon considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through Principal Component Analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian clustering algorithm. Considering the cluster which comprises the EOR field under evaluation, an inter-cluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components, and (b) the fraction of variance associated with each principal component is taken as weight of the Euclidean distance we determine. As a test bed, we apply our approach on three fields operated by eni. These include light, medium and heavy-oil reservoirs, where Gas, Chemical and Thermal EOR projects have been respectively proposed. Our results are (a) conducive to the compilation of a broad and extensively usable data-base of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.