An innovative workflow for selecting appraisal area in low permeability greenfield development under uncertainties

Abstract

There are uncertainties in both inherent geological properties and IOR/EOR performance parameters of low permeability greenfield reservoirs. Therefore, efforts to reduce uncertainties in the appraisal phase are necessary for the development and production phases. An adequate selection of the appraisal area in the hydrocarbon field is an imperative factor since the results of the appraisal well drilling and IOR/EOR pilot tests will be utilized for the development of the entire field. The major challenge in selecting an appraisal area is the lack of an integrated and systematic approach. In this study, we present a novel systematic and quantitative approach consisting of a better representative of reservoir features. To do this, some sort of cluster analysis over multiple realization is utilized. The underlying field of study is firstly segmented into appraisal candidate areas. Afterwards, the corresponding covariance matrix and recovery factor arrays are calculated for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered by fuzzy clustering method to choose the dominant cluster. Subsequently, the distances between each area and the center of dominant clusters could be used as a criterion for decision making. Moreover, the coefficient of variation for the oil volume changes is another criterion. Finally, the Shannon entropy weighting along with Reference Ideal Method (RIM)-Multi Attribute Range Evaluations (MARE) are applied. They are used as a multi-criteria decision-making method to calculate the Probabilistic Appraisal Opportunity Index (PAOI) in each area. The proposed method was applied for an appraisal study on an oil field in southwest Iran and, the optimum appraisal area is thereby determined based on the PAOI.