TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractHorizontal wells are becoming a very important component in the thermal recovery of heavy oil reservoirs. The success of a cyclic steam injection project depends strongly on the selection of key parameters, such as cycle length and amount of steam injected. The numerical simulation of horizontal wells, especially under non-isothermal conditions, is computationally demanding. When optimization is combined with numerical simulation, the computing time requirement may be prohibitive and it is not guaranteed that the optimal conditions will be found.In this research, a new methodology has been developed for optimizing the cyclic steam injection process for vertical and horizontal wells. The procedure integrates oil production characterization using numerical simulation, net present value maximization through a Quasi-Newton method, and model validation/tuning. The three-stage procedure provides the optimum number and/or duration of cycles, the optimal amounts of steam to be injected in each cycle and the optimal value of the overall economic indicator.The optimization algorithm was successfully validated with published results obtained from the discrete maximum principle. The methodology was then applied to determine the optimal conditions of cyclic steam injection for a horizontal well located in Bachaquero field, Venezuela.

A new methodology to determine the potential SAGD, "Steam Assisted Gravity Drainage", application for Venezuelan reservoirs, with °API gravity less than 20, is proposed as a first phase to select the most proper candidates. A systematic ranking of all the possible reservoir candidates using screening criteria supported by statistical methods is performed. Based on the evaluation of several analytical models a more representative analytical tool was generated to evaluate and predict the performance of SAGD process. Also, this analytical tool was used to define the critical parameters and the statistical analysis was used to estimate the relative influence of each one. Ranking method was based on a similarity approach. By defining a reference reservoir from published field tests and comparing statistical distances from it thirty-five candidates were pre-selected from a total of 1067 Venezuelan reservoirs. The best candidates should be evaluated with more details through numerical simulations. The resulting technical efficiency and economic balance will be taken as a decision criterion for a field application. This methodology will facilitate the decisions related to the application of SAGD process to increase the recovery factor in Venezuelan heavy oil reservoirs and upon its potential massive application.

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.

No 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.