In this paper, we present a field example where a streamline simulator was used to rank multi-million cell geostatistical reservoir descriptions and to find the optimum level of vertical upscaling for finite-difference simulation. During geostatitstical reservoir characterization, it is a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions and performance predictions. However, only a small fraction of these models can be considered for comprehensive flow simulations because of the high computational costs. A viable alternative is to rank these multiple ‘plausible’ reservoir models based on an appropriate performance criterion that adequately reflects the interaction between heterogeneity and the reservoir flow mechanisms. In this study, we explore the use of ranking based on streamline time of flight connectivity derived from a streamline simulator. The time of flight reflects fluid front propagation at various times and its connectivity at a given time provides us with a direct measure of volumetric sweep efficiency for arbitrary heterogeneity and well configuration. The volumetric sweep efficiency is the simplest measure that reflects the interaction between heterogeneity and the flow field. It is a dynamic measure that can be easily updated to account for changing injection/production conditions. We show that the proposed connectivity criterion can also be used to evaluate the effects of vertical upscaling in the dynamic performance and to determine the optimal level of upscaling for numerical simulation purposes. Our field study involves a Middle Eastern carbonate reservoir under a moderate to strong aquifer influx. The reservoir is on primary depletion and has no injectors. Multiple geostatistical reservoir descriptions were generated using a hierarchical approach whereby the larger level of uncertainty is defined first followed by smaller levels. The aquifer is modeled with a constant pressure boundary and for each time update, the location of the boundary was modified to account for the water encroachment. Using the field-wide sweep efficiency as a performance measure, the realizations were ranked, and used for flow simulation to assess risks associated with various development strategies. Subsequently, three selected realizations were upscaled for the purpose of comprehensive history matching and performance prediction. Background With the wide-spread use of geostatistics, it has now become a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions and performance predictions. Most commonly, these multiple realizations account for spatial variations in petrophysical properties within the reservoir and thus, represent a very limited aspect of uncertainty. For reliable risk assessment, we need to generate realizations that capture a much wider domain of uncertainty such as structural, stratigraphic, as well as petrophysical variations. From a practical point of view, we want to quantify the uncertainty and at the same time keep the number of realizations manageable. In this study, we will adopt an approach that is based on hierarchical principles. Thus, the uncertainty having the most potential impact is identified first. For example, with limited well control, the structural uncertainty derived from the seismic interpretations can have the most impact on the flow performance. Or, for faulted reservoirs, the uncertainty with respect to locations of faults can have the most impact. Then, the next level of uncertainty is identified and so on. The last level of uncertainty is the multiple geostatistical realizations of reservoir properties for a given set of input parameters. The petrophysical uncertainties generally tend to have a much lower impact on the reservoir performance compared to factors affecting large-scale fluid movements.

TX 75083-3836, U.S.A., fax 01-972-952-9435.

Geological knowledge is an important ingredient in a successful reservoir characterization process. Geoscientists and engineers have used variogram extensively as the tool to quantify the spatial relationship of various attributes, e.g., facies/rock type, porosity, and permeability. Proper variogram modeling is a key factor to obtain a geologically-sound reservoir characterization model. This paper discusses the difficulty that is commonly encountered by many practitioners in modeling the variogram and proposes a way to incorporate geological knowledge as the soft information to improve variogram model. Common difficulty in variogram modeling is the calculation of horizontal variogram. The averaging technique that uses combination of geological knowledge and analogy in geophysical literature about frequency data analysis is implemented to solve the difficulty in calculating horizontal variogram. This technique has produced results that are agreeable with geology of the reservoir. The art of incorporating the geological knowledge in variogram modeling lies in the fact that geological knowledge is a qualitative measure whereas variogram is a quantitative measure. The methodology to combine these two measures presented in this paper is as follows. First, interpreting various geological aspects of the reservoir in detail. These include, but not limited to, the interpretations of geological environment, sequence stratigraphy, pore-space characteristics, iso-chores, iso-porosity and iso-permeability maps. From these interpretations, a summary table, that includes the major continuity direction, lateral extension and anisotropy index of each attribute, is prepared. Second, calculating experimental variogram using the Averaging Technique. Third, modeling the experimental variogram considering the information obtained from the first step. The procedure presented above has been implemented as a routine procedure in several reservoir characterization studies for both carbonate and sandstone reservoirs in the Middle East and in the USA. For illustration purposes, comparison of the realization results, taken from carbonate field study, between the model with variogram derived purely from the hard data, i.e., well log data, and the variogram derived from both hard and soft data, i.e., geological knowledge, is presented. It is concluded that the incorporation of geological knowledge has improved the confidence level of the results and should always be part of any reservoir characterization study. Introduction Geoscience data sets are distinguished from other types of data sets in one important aspect: they exhibit spatial relationship.1 In simple terms, neighboring values are related to each other. This relationship gets stronger as the distance between two neighbors becomes smaller. In most instances, beyond a certain distance the neighboring values become uncorrelated. This type of qualitative information needs to be defined in a suitable form so that it can be used to estimate values at unsampled locations. The most common statistics used to describe spatial relationship is variogram. It is the most widely used tool to investigate and model spatial variability of various reservoir attributes.2 The success in modeling the spatial relation, via the variogram, will provide higher chance of a successful reservoir characterization study. The main role of variogram is to reflect our understanding of the geometry and continuity of reservoir properties, which can have an important effect on the predicted flow behavior and reservoir management decisions. It is the measure of "geological variability" versus distance; it increases, as samples become more dissimilar. Therefore, it is clear that geological knowledge in reservoir charactersization is incorporated through variogram model. Therefore, thorough interpretation of geological knowledge should be done prior to modeling the variogram relationship. The interpretation that is of interest to the variogram modeling is anything that can lead to the information of lateral extent and major/minor continuity directions.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIt is a common observation that well test permeability values do not match with thickness weighted core permeability averages. This is not a surprise because of the differences in the measurement scales where, unlike well test measurements, core samples represent a very small portion of the reservoir around the well bore. In addition, the presence of fractures and/or high permeability channels will further enhance the difference between the two sources of data. Therefore, reservoir descriptions based on core measurements alone cannot honor well test results. They need to be modified properly without violating the underlying geological and geostatistical information.In this paper, we present a methodology to properly enhance permeability fields that also accounts for fracture distribution in the reservoir. The basic idea is that radial upscaling around a wellbore within a given investigation radius should match the permeability obtained from well tests. The enhancement is caused by two factors: microfractures, which cannot be explicitly represented in the reservoir description, and macro-fractures, which can be interpreted using 3-D seismic data. To account for these two different types of fractures, we calculate two different enhancement factors, one for the base level (microfractures) and one for the higher level (macro-fractures). The base level, after appropriate interpolation, is applied across the entire reservoir, whereas the higher level is applied only to locations where macro-fractures are interpreted from 3-D seismic data.The technique was successfully applied to a Middle Eastern carbonate reservoir. A significant correlation is observed between the enhancement required to match the well test data and the fracture density (macro-fractures obtained from 3-D seismic data) within a given investigation area. A correlation function is then obtained between the enhancement factor and the fracture density for a given grid block, which in turn is used to apply enhancement to interwell locations. Thus, the resulting permeability field did not only honor the well test results but also the fracture distribution and the underlying geological and geostatistical descriptions. In a later stage, a tensorial approach was used to upscale permeability to account for the anisotropy in permeability distribution. Using this approach, a proper anisotropy of permeability distribution, matching the fracture orientation, has been obtained.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents the result of fully 3D integrated reservoir description and flow simulation study of a giant oil field in Middle East using the state of the art technology. The overall goal is to develop a representative reservoir model to form the basis for reservoir management and longterm development planning. This is done by generating alternate reservoir descriptions, based on stochastic models, to quantify uncertainties in the future performance. The data that were integrated include well cores and logs, geological interpretation (stratigraphy, rock type, depositional model), seismic (structure, curvature analysis and inversion-derived porosity), well test, SCAL, production data and fracture distribution.The 3D multiple realizations were generated by considering rock type and petrophysical properties at well location, obtained from well logs and cores, and simultaneously constrained by seismic derived porosity. The simulations of properties were generated using simultaneous sequential Gaussian simulation where the seismic constraint was introduced via Bayesian Updating procedure. Special consideration was given to the spatial modeling of data where soft information was derived both from hard data and depositional environment. Fracture distribution, derived from seismic curvature analysis, was used in the integration process to match the core-based derived permeability with well test permeability. This distribution was used to obtain permeability anisotropy distribution using newly developed tensorial approach.A total of forty-eight realizations were generated considering four major types of uncertainties: structure, spatial model, petrophysical properties and simulation path. The results have been used as the basis for fluid in place (STOIIP) calculation using Monte Carlo simulation technique. These realizations are then ranked based on the sweep efficiency, obtained from multiphase streamline simulations, and the STOIIP. Three realizations, representing medium, low and high realizations, were selected and upscaled. An optimum vertical upscaling level was determined using streamline simulator and developing quantitative criterion. This ensures that the representative heterogeneity of the reservoir was maintained during the upscaling process.Comprehensive history matching was done for the three selected realizations for the entire nineteen years of production history using objective criterion so that the quality of the three matches is similar. The observed data matched include water cuts and measured pressures. The parameters used to match the history are restricted to the parameters that have not been accounted for in the static model. Using probabilistic concepts, uncertainties in future performance were quantified for various scenarios.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents a practical approach in modeling a naturally fractured reservoir. The approach was used for a field study of a giant carbonate reservoir in the Middle East. The method is shown to be practical and comprehensive and yet has produced good results. It consists of a fully integrated effort from geological, geophysical and engineering disciplines. The overall goal of the study is to develop a representative reservoir model to form the basis for reservoir management and long-term development planning.The approach consists of the following procedures:• Generation of multiple realizations of matrix property using geostatistical techniques. The standard cosimulation procedure was implemented to ensure the consistency among reservoir properties, namely rock type, porosity and permeability. • Generation of multiple realizations of 3D fracture property by reconciling seismic, well logs and dynamic data. These were obtained from curvature analysis and seismic facies map validated by borehole image and dynamic data. The fracture network was described in the reservoir as lineaments (fracture swarms) showing two major fracture trends. • Calibration of the model permeability with well testderived permeability considering fracture distribution. A newly developed technique was implemented to ensure that the fine scale model (i.e., geological model) honors well test as well as production data before it was subjected to the flow simulation. The technique also generates permeability anisotropy to account for fracture orientations.• Ranking of multiple realizations using streamline simulation to select three representative realizations (low, medium and high models).• Upscaling of reservoir properties, including vertical upscaling level optimization using streamline simulation. • History matching and future performance prediction of the three selected realizations as a single media model. The use of single media model was based on the observation of relatively high matrix permeability in the major producing zone. However, for comparison purposes, a dual media model was also developed.• Uncertainty analysis of the future dynamic performance using a probabilistic approach. The procedure described above has been implemented successfully in a field study. The use of a calibration process in the geological model reduces the number of parameters that need to be adjusted during history matching. Consequently, history matching may concentrate on the uncertainty in parameters that have not been specifically accounted for in the geological modeling stage, such as relative permeability and aquifer size/strength.

This paper presents an innovative approach to integrate fracture, well test and production data into the static description of a reservoir model as an input to the flow simulation. The approach has been successfully implemented into a field study of a giant naturally fractured carbonate reservoir in the Middle East. This study was part of a full field integrated reservoir characterization and flow simulation project. The main input available for this work includes matrix properties, fracture network, well test and production data. Stochastic models of matrix properties were generated using geostatistical methodology based on well logs, core, seismic data and geological interpretation. Fracture network was described in the reservoir as lineaments (fracture swarms) showing two major fracture trends. The network and its properties, i.e., fracture porosity and permeability, were generated by reconciling seismic, well logs, and dynamic data (well test and PLT). The challenge of the study is to integrate all the input in an efficient and practical way to produce a consistent model between static and dynamic data. As a result, it is expected to reduce the history matching effort. This challenge was solved by an innovative iterative procedure between the static and dynamic models. The static part consists of the calibration of model permeability to match the well test permeability. It is done by comparing their flow potentials, kh. In this analysis the dominant factor in controlling production at each well, either matrix or fracture, was determined. Based on the dominant factor, matrix or fracture permeability was modified accordingly. This way the changes in permeability are kept inline with the geological understanding of the field. The dynamic part was carried out through a full field flow simulation to integrate production data. The flow simulation at this stage was used to match production capacity, i.e. to determine whether the given permeability (matrix and fracture) distribution is enough to produce the fluid at the specified pressure during the producing period of the well. The iteration is stopped once a reasonable production capacity match is obtained. In general, a good match was achieved within 3–4 iterations. The generated reservoir description is expected to substantially reduce the effort required to obtain a good history match. Introduction This paper presents the approach, implementation and results of fracture integration process into a reservoir model. The study is part of a fully integrated reservoir characterization and flow simulation study of an oilfield in the Middle East. A comprehensive integrated reservoir characterization was conducted by considering all available data, namely well logs and cores, geological interpretation, seismic (structures and inversion derived porosity), fracture network, and pressure build up tests. The approach used in the study was a stochastic approach where multiple reservoir descriptions were generated to quantify the uncertainty in the future performance.1,2 Reservoir properties for each realization were generated using a geostatistical technique that produces properties, i.e., porosity, permeability and water saturation, consistent with the underlying rock type description. The description was based on core and log data. Additionally, porosity, which affects the permeability description, was also constrained to the seismic derived porosity. The permeability distribution generated by this method was referred to as the core-derived permeability in this paper. Since core-measurement commonly represents the matrix property of the rock, the core-derived permeability mentioned above was also referred to as matrix permeability.

In this study, two-phase production history data is incorporated into reservoir characterization by inverse modeling. As a first step, an analytical method has been developed to compute sensitivity coefficients for two-phase flow conditions. These equations are independent of flow simulators, and can be incorporated to any finite difference or streamline simulators. They only require one simulation run to compute the sensitivities for all time steps. In this study, we have chosen streamline simulation, which is order of magnitude faster than finite difference simulations. In the second step, a dual loop iteration technique is employed which uses conjugate gradient relaxation in order to bypass the explicit construction of the sensitivity coefficient matrix and the inversion of the product of sensitivity coefficient matrix and its transpose. Thus, a substantial reduction in the computational cost for solving the inverse problem is achieved, including the help of streamline simulation. Finally, this technique was validated using synthetic as well as field cases. The field case study was conducted on a sandstone reservoir. P. 151