New Approach to Identify Analogue Reservoirs

Abstract

The identification of analogous reservoirs is an important step in planning the development of a new field, because the information available about the new areas is usually limited or even nonexistent. Traditionally the search for analogous reservoirs has been made by experienced geoscientists, but this practice is subject to availability of this experience and the results are heavily dominated by geology. In this paper we present a systematic and unbiased procedure to search for analogous reservoirs, based on information contained in a validated large database of reservoirs parameters, both engineering and geologic. Each reservoir has its own "fingerprint" characterized by the set of its own properties, which differ 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 variation of the similarity function (weights) and evaluation of different scenarios (static, dynamic, PVT behavior, etc.). Our method basically consists of four steps: Data Preprocessing, Key Parameters Selection, Multivariate Analysis, and Similarity Ranking. The first step consists of the analysis and preprocessing of the available database. In the second step, 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 Ranking step, we apply a similarity function to the group of previously selected "analogous reservoirs", generating a similarity ranking of analogous reservoirs. To validate this new method we use the Casablanca oil field as a target reservoir. Casablanca is a mature carbonate reservoir very well known by Repsol whose experts identified four analogues for this target. The new developed method was independently applied in this case to obtain 19 analogous reservoirs sorted by similarity criteria. The maximum similarity found was 85 % for the Amposta Marino reservoir, one of the independently identified analogous reservoirs given by the business unit team. Moreover, the four analogous reservoirs previously identified by the business unit team were between the first ten positions in the similarity ranking. These results are highly encouraging as it captures the know-how of the experts and ensures a reproducible response, regardless of the user expertise. The most relevant advantage of this new method is that it is based on a similarity function that takes into account all the weighted key parameters simultaneously, instead of sequential filters used by some commercial software. As a result, the procedure we present in this work will support the predictive search of missing properties for the target reservoir, reducing the uncertainty for decision making.

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

“…However, in our case, the difficulty lies in the fact that the data are not only continuous values that can be clustered, for example, through Euclidean distance, but also discrete values. Existing solutions involve, for example, reducing the initial space of heterogeneous data using principal component analysis (PCA) [30], and then clustering such data. However, the disadvantage of this approach is that by decreasing the dimension, we lose much information.…”

“…If we consider searching for analogues as a clustering problem in a multidimensional space, it becomes possible to consider various clustering algorithms to solve the problem. In previous studies [44,30], hierarchical clustering has been proposed to search for analogues reservoirs. Another critical task is analysing and predicting geological parameters, such as Lithology, Porosity, Depositional Environment.…”

Identification of Reservoir Analogues in the Presence of Uncertainty

“…In addition, one of the best known uses of analogous reservoirs is for reserves estimation (Harrel, 2004;Hodgin, 2006;Sidle, 2010). However, a bibliography review about the processes used in the Oil and Gas Industry to identify analogous reservoirs showed that this is an area with limited references (Martín Rodríguez et al, 2013).…”