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The paper describes the upscaling and reservoir simulation of a giant Middle East oil field, the geological modeling of which is described in a companion paper. 1 The main objective of the study was the simulation of the irregular water advance observed in some parts of the field as a consequence of peripheral water injection.Three scales of heterogeneity were identified in the characterization phase: the matrix, the stratiform Super-K intervals, and the fractures. To accommodate the different hydraulic properties of each heterogeneity system, a dual-media approach (dual porosity and dual permeability) was used.The assignment of the effective properties to the simulation grids (matrix and fracture grids) was performed independently for the three heterogeneity systems. In particular, the geostatistical facies model was upscaled with algebraic methods, while the stratiform Super-K layers and fracture properties were reproduced explicitly at the simulation gridblock scale through an original upscaling procedure.The history match was achieved in a short time by a small variation of the fractal dimension of the fracture distribution and without resorting to any local modification.Simulation results showed that the fracture system was the controlling factor in terms of water advance and breakthrough, while the impact of the stratiform Super-K layers proved to be of second order.In a later stage, the model was used to run production forecasts under different exploitation scenarios.The conclusions of this study indicate that for such porous and fractured reservoirs with stratiform Super-K occurrences, a detailed characterization of all the heterogeneity systems, coupled with a dual-media formulation, is necessary for accurate reservoir simulation and effective reservoir management.

As for some thick and highly fractured Iranian fields, the Cantarell complex located offshore Mexico presents features (decrease in the production GOR and bubble point pressure with time) that reveal the effect of convection. This effect on the past homogenization of the fluid properties is discussed and is supported by a thorough characterization of the thermodynamic properties of the actual reservoir fluids. In order to model convection, the reservoir simulator used for this study was adapted on purpose. Sensitivity runs were performed to demonstrate the necessity of accounting for convection for matching the past history of the Akal field, part of the Cantarell complex. Introduction Presentation of Cantarell Complex. The Cantarell complex is the most important oil field in Mexico, and the sixth largest in the world. In order to economically optimize its value, it has been decided to initiate a major recovery process by injecting nitrogen for pressure maintenance purposes. Cantarell field is a thick, highly fractured reservoir, therefore it is the kind of reservoir where convection phenomenon may occur. Convection is a complex process which is characterized by a vertical homogenization of fluid properties in the fractures. This may have an essential impact on production and injection profiles, in particular on the quantity of nitrogen in the effluents as well as nitrogen breakthrough times, thereby on the overall nitrogen injection efficiency. The Cantarell complex is located offshore approximately 85 km from Ciudad del Carmen. It includes four adjacent oil fields known as Akal, Chac, Kutz and Nohoch. Akal is the largest oil accumulation with more than 90% of the 35 billion bbl oil (Bbo) in place. The reservoir is an anticline producing from the fractured carbonates of the Cretaceous and upper Jurassic formations, which contains also many vugs and caves. The Upper Cretaceous is the most fractured and brecciated. Fracturing decreases with depth in the Middle and Lower Cretaceous. The average thickness of the whole reservoir is about 775 m and the depth of the top Cretaceous ranges between 1100 and 3600 mTVDSS. Below the Cretaceous sequence, the Upper Jurassic (Oxfordian, Kimmeridjian, Tithonian) is a stratigraphic reservoir with poor reservoir characteristics. Field production started in June 1979, reaching a peak of 1.157 MMbopd in April 1981, with 40 producing wells. A total of 184 wells were drilled in Cantarell, among which 173 wells in Akal. Cantarell crude is a 19 to 22° API Maya type, with an initial bubble point pressure close to 150 bar. Initially the reservoir pressure was above the bubble point pressure and was equal to 266 bar at 2300 mSS. Therefore there was no initial gas cap. The reservoir pressure rapidly reached the bubble point pressure and a secondary gas cap appeared in 1981. The Gas-Oil Contact was located at 1800 mSS in 1997. The corresponding cumulative production was around 5.5 billion stb. Presentation of Cantarell Complex. The Cantarell complex is the most important oil field in Mexico, and the sixth largest in the world. In order to economically optimize its value, it has been decided to initiate a major recovery process by injecting nitrogen for pressure maintenance purposes. Cantarell field is a thick, highly fractured reservoir, therefore it is the kind of reservoir where convection phenomenon may occur. Convection is a complex process which is characterized by a vertical homogenization of fluid properties in the fractures. This may have an essential impact on production and injection profiles, in particular on the quantity of nitrogen in the effluents as well as nitrogen breakthrough times, thereby on the overall nitrogen injection efficiency. The Cantarell complex is located offshore approximately 85 km from Ciudad del Carmen. It includes four adjacent oil fields known as Akal, Chac, Kutz and Nohoch. Akal is the largest oil accumulation with more than 90% of the 35 billion bbl oil (Bbo) in place. The reservoir is an anticline producing from the fractured carbonates of the Cretaceous and upper Jurassic formations, which contains also many vugs and caves. The Upper Cretaceous is the most fractured and brecciated. Fracturing decreases with depth in the Middle and Lower Cretaceous. The average thickness of the whole reservoir is about 775 m and the depth of the top Cretaceous ranges between 1100 and 3600 mTVDSS. Below the Cretaceous sequence, the Upper Jurassic (Oxfordian, Kimmeridjian, Tithonian) is a stratigraphic reservoir with poor reservoir characteristics. Field production started in June 1979, reaching a peak of 1.157 MMbopd in April 1981, with 40 producing wells. A total of 184 wells were drilled in Cantarell, among which 173 wells in Akal. Cantarell crude is a 19 to 22° API Maya type, with an initial bubble point pressure close to 150 bar. Initially the reservoir pressure was above the bubble point pressure and was equal to 266 bar at 2300 mSS. Therefore there was no initial gas cap. The reservoir pressure rapidly reached the bubble point pressure and a secondary gas cap appeared in 1981. The Gas-Oil Contact was located at 1800 mSS in 1997. The corresponding cumulative production was around 5.5 billion stb.

This paper presents a fully-integrated methodology for managing reservoir uncertainties during history matching, production forecasting and production scheme optimization. Based on the traditional experimental design methodology, this innovative approach, called the Joint Modeling Method, allows to model the production recovery as a function of both the deterministic uncertain parameters, such as petrophysical and production parameters, as well as non-continuous parameters such as geostatistical realizations and equiprobable matched models. In this new approach, the dispersion due to the non-continuous uncertainties is modeled in a rigorous statistical framework through the variance of the production recovery. The method was successfully applied on data derived from a North Sea real field case. The objective was to quantify the impact of the principle reservoir uncertainties on the cumulative oil production and to optimize future field development in a risk analysis approach. The uncertainties were mainly on petrophysical data, geostatistical facies distribution and aquifer strength. The study was performed in the following steps:sensitivity study. The most influential parameters were identified and the impact of geostatistical uncertainties was highlighted.history matching. The influential parameters were constrained to the available production data. In particular, the geostatistical model was locally modified using both FFTMA technique and the gradual deformation method.production scheme optimization. Experimental design and joint modeling were used to obtain probabilistic distributions of the optimized location of new wells in a non-producing zone.risk analysis: Finally, probabilistic incremental oil production was obtained using Monte-Carlo technique. Results show that this integrated methodology successfully enables to quantify the risk associated with the main reservoir uncertainties during the whole process of a reservoir engineering study (sensitivity, history match, production optimization and forecast). Introduction Thanks to growing measurement and computational facilities, reservoir modeling is getting more and more complex. Hence more and more prior uncertain parameters can be introduced in reservoir studies. The difficulty is then to identify the ones that are influential on production recovery and on economic field profitability and to quantify their impact. The "simplest" case where the reservoir engineer needs to deal with reservoir uncertainties is the appraisal case, when there is no production data to match. In that case, the uncertainty domain cannot be highly constrained and the impact on production forecasts may hence be very significant. To deal with those uncertainties, reservoir and production engineers commonly perform many reservoir simulations for different values of the uncertain parameters. This approach gives a qualitative idea of the influence of each uncertain parameter on the production response. However this method can quickly become very expensive when the number of uncertain parameters increases. Moreover, this is not a rigorous method since the impact of each uncertain parameter as well as the possible interactions between those uncertain parameters cannot be easily detected. Finally no direct quantitative relation between the responses and the uncertainties can be established.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe paper describes the upscaling and reservoir simulation of a giant Middle East oilfield, whose geological modeling is described in a companion paper (1). The main objective of the study was the simulation of the irregular water advance observed in some parts of the field, as a consequence of peripheral water injection.Three scales of heterogeneity were identified in the characterization phase, namely the matrix, the stratiform Super-K intervals and the fractures. To accommodate the different hydraulic properties of each heterogeneity system, a dual-media approach (dual porosity and dual permeability) was used.The assignment of the effective properties to the simulation grids (matrix and fracture grids) was performed independently for the three heterogeneity systems. In particular, the geostatistical facies model was upscaled using algebraic methods, while the stratiform Super-K layers and fractures properties were explicitly reproduced at the simulation gridblock scale, through an original upscaling procedure.The history match was achieved in a short time, by a small variation of the fractal dimension of the fracture distribution and without resorting to any local modification.Simulation results showed that the fracture system was the controlling factor in terms of water advance and breakthrough, while the impact of the stratiform Super-K layers proved to be of second order.In a later stage, the model was utilized to run production forecasts under different exploitation scenarios.Conclusions of this study indicate that for such porous and fractured reservoirs with stratiform Super-K occurrences, a detailed characterization of all the heterogeneity systems, coupled with a dual-media formulation, are necessary requisites for accurate reservoir simulation and effective reservoir management.

This paper presents an innovative integrated methodology for constraining 3-D stochastic reservoir models to well data and production history as well as a successful application to a real field case. The proposed approach allows to history match complex reservoir models in a consistent way by updating the entire simulation workflow. Advanced parameterization techniques are used to modify either the geostatistical model directly or the fluid flow simulation parameters in the same inversion loop. In a first step, the relevant inversion parameters are selected according to a sensitivity study based on the experimental design technique. In a second step, history matching is performed with the most significant parameters using an automated inversion procedure. In this step, the Gradual Deformation Method (GDM) is used to constrain the geostatistical model while respecting the global model properties. This technique may be combined with gradient based inversion methods in order to history match other deterministic parameters simultaneously. A successful application to a real field case located offshore Brazil, named PBR, is presented. The lithofacies reservoir model was built using geostatistical simulations with the non-stationary truncated Gaussian method. The fluid flow model includes about 40 wells and 15 years of production history. The entire simulation workflow was integrated in one history matching loop in order to update the geostatistical lithofacies model and the fluid flow model simultaneously. The mismatch between simulated and real production data was quantified by the calculation of an Objective Function (OF). A sensitivity study based on the experimental design technique was performed to determine the most influential parameters within the workflow and to capture the possible reasons of the mismatch. Based on these observations, an automatic history matching was performed with the key parameters. A set of deterministic parameters, including the facies permeabilities, vertical anisotropy ratios, correlation lengths, relative permeability end points and Corey exponents was used first to improve the global match. Then, the GDM was applied to modify the geostatistical model itself. The GDM proved efficient in history matching to modify the spatial facies distribution of the geostatistical model. Moreover, the repetition of this history matching procedure with several model realizations confirmed the reduction of uncertainty on production forecasts. Results on this real oil field case showed that this innovative approach, which combines both the experimental design technique and the GDM, enables detailed analysis of the mismatch and significantly increases the predictive quality of reservoir models, using a limited number of simulations. Introduction Stochastic methods are now widely used to build realistic fine scale geological models relevant for data integration when dealing with heterogeneous reservoirs. Such methods allow to constrain the geological model by various information such as a priori geological knowledge, well logs, seismic data, etc. The reservoir fluid flow model is generally built by upscaling this geological model and the history matching process is often performed on the reservoir model only. Therefore, the resulting matched models can lost their consistency with the geological information. Moreover, the geological model could not honor the dynamic data. The proposed approach focuses on history matching with geological information and is based on constrained geostatistical modeling techniques. History matching is conducted to update the entire simulation workflow, from the fine scale model construction to the fluid flow simulation. It ensures that the consistency is kept all along the modeling process as far as any parameter modification is automatically propagated in the workflow. This approach allows any deterministic parameter involved in the workflow to be optimized: reservoir parameters such as porosity, fault transmissibility and relative permeability end points as far as geostatistical parameters such as the ranges of the variogram. Stochastic parameters are treated by the GDM, which enables to constrain the geostatistical realizations to production data. Moreover, the capability to control automatically the workflow processing from the fine scale modeling to the fluid flow simulation is a strong advantage to perform comprehensive uncertainty analysis.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents an advanced risk analysis approach that has been applied on a Brazilian field case to manage and quantify the reservoir uncertainties during production forecasting and production scheme optimization. The oil field (named PBR) is located offshore Brazil. The PBR reservoir consists of a complex lithology, including mainly turbiditic sandstones interbedded by shales and marls. The workflow of the PBR reservoir modeling from the geostatistical lithofacies model, built using the non-stationary truncated gaussian method, to the flow model (about 24000 active cells) has been implemented in a fully integrated chain. The field was considered in this hypothetical study at its appraisal stage. Main challenge consisted in selecting an optimal water injection scheme to maximize oil production while minimizing water production and maintaining pressure over the 15 years of production, taking into account the overall major sources of uncertainties in the reservoir. Advanced risk analysis methods were used with the following objectives: -Determine the impact of the main uncertainties on the field production forecasts versus time and versus geographical locations of production and injection wells -Optimize the water injection phase to maintain pressure over time, while producing maximum oil and minimum water in a 15-years period.-Quantify the impact of main uncontrollable reservoir uncertainties on production forecasts and fluid distribution maps versus time for the optimal water injection program selected in order to identify potential risks with non-drained zones and pressure drops. At each step, either classical or optimal experimental designs were used to be able to perform an accurate analysis at a minimum cost in terms of reservoir simulation and interpretation. Results show that this innovative methodology can be successfully applied on complex real cases to quantify the risk associated with the main reservoir uncertainties during production forecasts and to optimize the water injection. In particular, this example illustrates how some innovative interpretation tools can be combined with experimental design methodology to perform advanced risk analysis for reservoir management.

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