Identifying analogous reservoirs is important in planning the development of a new field. Usually, information available about a new area is limited or even nonexistent. Traditionally, the search for analogous reservoirs is carried out by experienced geoscientists. This search is subject to the availability of expertise, and the results heavily depend on the geology of the area. This paper presents a systematic and unbiased procedure to search for analogous reservoirs on the basis of information contained in a large validated database of engineering and geologic parameters. Each reservoir has its own "fingerprint" characterized by a set of properties, which commonly vary from one reservoir to another. The method uses multivariate statistical techniques to find a unique and reproducible list of reservoirs with fingerprints that are most similar to the selected target. The flexibility of the method allows for evaluation of different scenarios [e.g., static, dynamic, pressure/volume/temperature (PVT) behavior] by analog class.This method consists of four steps: data preprocessing, keyparameters selection, multivariate analysis, and similarity ranking. The first step involves analysis and preprocessing of the data.With key-parameter Selection, variables with largest impact on the case to be evaluated are identified. The third step, multivariate analysis, applies several multivariate techniques such as principal-component analysis (PCA) and cluster analysis. Finally, in the similarity-ranking step, we apply a similarity function to the group of previously selected "analogous reservoirs," generating a similarity ranking of analogous reservoirs.Casablanca oil field was used as a target reservoir to validate this new method. This reservoir is a mature carbonate field very well-known by Repsol, which had experts that identified four analogs. The new developed method was independently applied in this case to obtain 19 analogous reservoirs sorted by similarity criteria. The maximal similarity found was 85% for the Amposta Marino reservoir; this was one analogous reservoir independently identified by the business-unit team. Moreover, these four analogous reservoirs previously identified were within the first ten positions in similarity ranking. These results are encouraging because they ensure a reproducible response regardless of the user expertise.The singular feature of this new method is that it is based on a similarity function, which accounts for all the weighted key parameters (KPs) simultaneously. Commercial software often uses sequential filters. As a result, the procedure we present will support the predictive search of missing properties for the target reservoir, reducing the uncertainty for decision making.

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SUMMARYReducing uncertainties at appraisal stage of a reservoir is a key challenge to tackle, due to the limited number of exploration wells. The prediction of the facies architecture inside complex reservoirs is another challenging task, that cannot be assessed using classical geostatistics methods.The facies are used as the main guideline to populate the properties of static model, reducing error in facies assignation and propagation in the mesh is mandatory. In such a composite context, a tool able to constrain physically and geologically the sediments spatial distribution is a serious advantage.In this framework, 4D Forward Stratigraphic Modeling can be a useful tool to assess facies distribution. For the last 15 years this tool has been applied, commonly to exploration studies, and is now applied to reservoir scale studies.4D Forward stratigraphic modelling is a powerful tool to populate with an inner logic a three dimension geological model. The rules of physics, sedimentology and stratigraphy are honored and allowed the building of predictive environment of deposition and lithologies. At fine scale, Forward Stratigraphic Models could be used as a new method suitable for representing geologically reservoir heterogeneities in the static reservoir modelling, during the very challenging well-poor appraisal phase

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