New Approach to Identify Analogous Reservoirs
- Hilario Martín Rodríguez (Repsol) | Elena Escobar (Repsol) | Sonia Embid (Repsol) | Noelia Rodriguez Morillas (Repsol) | Mohamed Hegazy (Repsol) | Larry W. Lake (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Economics & Management
- Publication Date
- October 2014
- Document Type
- Journal Paper
- 173 - 184
- 2014.Society of Petroleum Engineers
- 5.8.7 Carbonate Reservoir
- data mining, analogue, analogous, reservoir, analog
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- 689 since 2007
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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, key parameters 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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