This paper summarizes an integrated field, experiments and computer simulation research program conducted to support a review of the reserves and development plan for the Hamaca Area in the Orinoco Belt (OB), Venezuela. The impact of the foamy oil mechanism on the Hamaca heavy-extra heavy Oil reserves and the importance of understanding this behavior is presented in this paper. The study was conducted in reservoirs with the largest production history (within the OB). The experimental results showed in-situ formation of non-aqueous oil foam with high gas retention, improving oil mobility and leading, therefore, to high well productivity. An experimental recovery factor over 10% was obtained under primary conditions so it was possible to increase oil reserves by approximately 30% over the currently accepted volumes. Experimental results provided input parameters to perform preliminary simulation runs which would allow the modification of well spacing schemes, the generation of a high-probability production profile, and the optimization of artificial lift systems, incorporating larger capacity equipment. Introduction When the exploitation of Hamaca heavy oil reservoirs began, it was assumed that the primary production mechanisms were sand compaction, solution gas drive and thermal effects due to steam cyclic stimulation which improves oil mobility. However, an unexpectedly high cold production lead to research of the drive mechanisms to explain the special production performance. Similar behavior has been observed in the Lloydminster area of Canada. Despite considerable speculation, a number of authors have studied foamy oil behavior and some research has recently been done, yet the mechanism leading to this behavior still remains to be successfully explained. Reservoir properties that were proposed to explain the behavior in the OB and in Canada include unusually high sand permeability and/or critical gas saturation. In Canada, high critical gas saturation is now an accepted property of some reservoirs, the so called Foamy Oil reservoirs. However, a monitoring field program of sand compaction and land subsidence did not show any evidence that this mechanism has taken place in the Hamaca Area. None of these proposed properties and mechanisms are consistent with field observations. On the other hand, numerical models showed high uncertainty when trying to match the reservoir production pressure behavior. As a consequence, a research program was designed as part of an integrated reservoir study. It included a production behavior analysis, an experimental program for fluid/porous media characterization through conventional and nonconventional PVT tests, and solution gas drive experiments at research centers of Venezuela (INTEVEP), Canada (PRI-CMG) and USA (LAB). P. 639

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.

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.

We propose and evaluate a new methodology and tool for the identification of reservoir analogues of a target reservoir, given a database of known reservoirs. In particular, we focus on the common situation where some of the key properties of the target reservoir are unknown. This situation introduces uncertainty into the identification procedure. Therefore, an important step in our methodology is to characterize this uncertainty, so that it can later be considered when estimating production potential and capturing the associated risk.The proposed methodology is composed of a sequence of five steps: Data Preprocessing, Key Parameters Selection, Statistical Learning, Similarity Ranking and Uncertainty Characterization. The first step consists of the analysis and pre-processing of the available database. During Key Parameters Selection, properties with largest impact in the case to be evaluated are identified. In the third step, unknown key properties of the target reservoir are estimated using different statistical learning techniques. In the Ranking step, a similarity function is applied to the database, generating a similarity ranking. Finally, in the Uncertainty Characterization step nonparametric methods are used to estimate probability density/mass functions to characterize the uncertainty in the properties estimated through analogous reservoirs.We perform experiments to assess both the quality of property prediction and the quality of the selected analogues. We report results produced by five different machine learning algorithms using leave-one-out cross-validation. According to our results, Decision Trees was the most effective algorithm for categorical properties, while Regressive Support Vector Machines produced the best results for numerical properties. We also show detailed results for the Casablanca oil field as a target reservoir, which is a mature carbonate reservoir very well known by Repsol.Our methodology allows the identification of analogues for newly discovered reservoirs with limited information. An important additional outcome is the estimation of unknown properties such as recovery factor and production mechanism, and their associated uncertainties, which support the field development plant. IntroductionIn the Oil and Gas E&P Industry, analogous reservoirs are used in many ways to study reservoirs that lack critical information. In this paper, a "target reservoir" is any reservoir with a deficit of critical information, and "analogous reservoirs" are those with the most similar fingerprints to the target reservoir, based on the selected key parameters.

Borehole stability problems can dramatically increase the cost of drilling and completing wells. For example, sand failure and production may leadto costly well shut-in and workover, as well as equipment failure. Wellbore instability is particularly important when operating in deep unconsolidated formations or when horizontal wells are planned. Also, stability requirements increase when drilling into producing formations. Drilling problems such as stuck drillstring, caving, logging difficulties, well enlargement, inadvertent side tracking, and stuck casing can be costly to remediate. Traditionally, analytical methods are used to predict required mud weights. However, they are typically too conservative and are unsuitable for applications to horizontal wells in unconsolidated formations.In order to analyse horizontal drilling in an unconsolidated formation, a wellbore stability model has been developed. The model uses a robust finite element elasto-plastic code as a tool to perform effective stress analysis of the near-wellbore tensile and shear failure. The code is capable of handling extremely low confining stresses (near tensile regime) in unconsolidated formations. It is considered that plastic· yielding, occurring at relatively low deviatoric stresses under low confining stress conditions, is not a good indicator of wellbore failure in terms of loss of service. Therefore, a more realistic criterion based on the accumulated plastic strain is applied. The model has been used successfully to analyse the stability of a horizontal well with open-hole completion in unconsolidated oil sand. The results show reasonable agreement with field observations.