Summary The screening of large number of reservoirs for the application of EOR processes has been generally done through "rules of thumb" which often times fail to identify the most suitable reservoirs, due to their binary characteristics, which do not take into account synergistic effects on process performance. Therefore, a new screening process performance. Therefore, a new screening method is developed in this work to rank reservoirs for carbon dioxide flooding which attempts to solve this shortcoming. The method is based on a parametric study, carried out systematically to determine the effect of reservoir properties on reservoir response to the gas injection. The study was done using a fully compositional simulator, a black oil model with a mixing parameter, and a semi-analytical predictive model. Results obtained with the three simulators are presented and compared in this paper. Reservoir parameters examined were temperature, pressure, porosity, permeability, dip, API gravity, oil saturation, net oil sand thickness, minimum miscibility pressure, saturation pressure, remaining oil in place, and reservoir pressure, remaining oil in place, and reservoir depth. The optimum set of parameters which gave the best average oil production rate for a base case was obtained from the simulation studies. The base case consisted of the injection of 2000 MSCF/D of carbon dioxide in a inverted five spot, 40 acres pattern. The decrease in oil production rate with departures of the production rate with departures of the characteristic parameters from the optimum values were also determined to quantify the importance or weight of each property. Actual reservoirs were ranked by an arbitrary heuristic function, called the exponentially varying function, whose value depended exponentially on the weighted differences between the properties, characteristic of the reservoir, and the optimum values obtained from the simulation studies. Results obtained with the three simulators compare quite well and indicate that on the average, the best reservoir for carbon dioxide injection should have an oil gravity of 36 degrees API, a temperature of 150 degrees F, a permeability of 300 mD, an oil saturation at the start of the injection of 60 %, a reservoir pressure at the time of injection of around 200 psi over minimum miscibility pressure, a porosity of 20 %, a net sand thickness of 40 ft and a reservoir dip of 20 deg. Of the above parameters, those whose changes around the optimum influence the most process performance are API gravity, oil process performance are API gravity, oil saturation and reservoir pressure. Therefore, the reservoirs with these three parameters closer to the optimum values are the best candidates for CO injection. This is taken adequately by the exponentially varying function defined in the paper. The procedure was applied to rank about six hundred reservoirs in the greater Anaco and Oficina areas of Eastern Venezuela in order to identify the most suitables for a pilot test. The reservoir ranked 30th by the method hereby described was chosen to implement the pilot. This procedure could be easily extended to other EOR processes once the necessary simulations are carried out.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents a validation of an internally consistent, physically based model for relative permeability based on an extension of the Carman-Kozeny (CK) equation. The modified CK (MCK) expression is a function of surface areas of fluidfluid and fluid-rock interfaces, as well as fluid saturations and tortuosity. The model uses interfacial and surface areas determined from capillary pressure measurements and, by this means, can incorporate variable wettability and hysteresis as well as assuring consistency of petrophysical properties.To validate the MCK approach, the model is fit to experiments where both capillary pressure and relative permeability are measured simultaneously during flow. The MCK model is further fit to literature-reported water-oil experimental data. Besides the MCK model, each data set is fit with a modification of the commonly used Brooks-Corey (MBC) model to compare the performances of the two.The surface areas derived from capillary pressure relationship used in the MCK model provide a good description of the experimental relative permeabilities measured under the same conditions. The investigated MCK model fits experimental data almost as well as the MBC model. Furthermore, the MCK model is physically based and appears to agree with the wetting characteristics of the investigated porous media when these are known.

The economical performance of an oilfield operation is uncertain and highly influenced by strategic and operational decisions variables such as well placement, scheduling and control. Based on numerically intensive reservoir simula- tors, the evaluation of an extensive list of possible decisions across all possible realizations becomes computationally intractable and additional mathematical techniques are required. A common approach to dealing with this problem is the Response Surface Methodology (RSM) coupled with Design of Experiments (DoE) and sampling techniques. Existing approaches to construct surrogates depend on specific statistical/risk measures such as expected value and standard deviation. For example, in order to construct a surrogate for the standard deviation of NPV, one would compute the standard deviation associated with the simulation results over the selected geological realizations for each candidate production strategy and then fit a mathematical model to it. In this case, the idiosyncratic response of each geological realization with respect to the production strategy is lost, which may lead to a bad risk assessment and, consequently an inappropriate decision making process. In this paper, we propose Stochastic Response Surface Methodology (SRSM) to enhance the decision-making process over the determination of oil & gas production strate- gies while properly taking into consideration geological uncertainty. The SRSM does not depend on any pre-defined risk measure providing the necessary flexibility to evaluate the intrinsic risk-return trade-off associated with the economical performance of the reservoir. Our approach is based on the construction of surrogates for each geolog- ical realization selected by sampling procedure. We argue that constructing a different surrogate for each selected realization captures the idiosyncratic behavior of each representative geological setting and provides the flexibility of choosing any set of risk measures after the surrogate construction has been done. Based on the Brugge field, an SPE benchmark case study, we provide a numerical example to illustrate our methodology.

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.

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.