The Greater Burgan field is the world’s largest sandstone oil field. It has been producing since 1946 under primary depletion from natural water drive. Sub-surface modeling is an integral part of reservoir management and Kuwait Oil Company (KOC) has been investing significant amount of resources in this technology to support field development planning and depletion strategy. In 2001, the first comprehensive Greater Burgan full-field geological model was built with 65 million cells encompassing all the major reservoirs. Subsequently, a reservoir simulation study with a 1.6 million cells dynamic model was conducted in 2003 utilizing parallel simulation technology. In the last decade, active field development plans have resulted in major surface facility upgrades and more than 300 new wells drilled. The existing sub-surface models no longer sufficed to meet technical requirements and as a result, an unprecedented Greater Burgan sub-surface modeling project was commenced in 2009. This is a 4-year project consisting of structural, static and dynamic modeling. It started with Sequence Stratigraphy Study followed by Geo-modeling. The latter was completed in August 2011 and subsequently paved way to Dynamic Modeling phase of the study. This paper discusses up-scaling of the high resolution geological model and the specific problems that the study team had to overcome in the process. State-of-the-art technologies were applied to the construction of the biggest-ever geological model (900 million cells) of the Greater Burgan field. The high resolution of the static model was necessitated by not only the sheer size of the field, but also, by the complex depositional environment defining the internal architecture of the reservoir and the resultant heterogeneity in the system. Sedimentological and stratigraphic data were used extensively to describe the internal architecture of the reservoir, capturing the level of heterogeneity observed in the field. A primary use of this high resolution model was to create a basis for the flow simulation model used in reservoir management. Although computing technology has advanced significantly, conducting flow simulations on such a fine scale model demands prohibitive amount of computation and becomes impractical when a time constraint is imposed on the project. Therefore, model up-scaling is essential to conduct simulations in a reasonable run time. Preservation of volumetric quantities and flow features were the two key considerations for the successful up-scaling. While volumetric conservation can be achieved by following a strict procedure, preserving flow features across the various reservoirs imposes a great challenge. This paper addresses actual challenges encountered during the up-scaling process. The discussion focuses on the following topics: Choice of the model size considering both computational time and accuracy of simulation results. Need for multi-scale approach with three simulation models: Fine, Coarse, and Very Coarse - each to be used to answer specific questions of the study;Right balance between areal and vertical grid coarsening that ensures adequate model physics and preservation of geological features;Mechanistic modeling to support decisions made in the process of up-scaling;Preservation of flow features in various reservoirs, difference between massive and more heterogeneous reservoirs;Transferring water saturation between fine and coarse models: testing various approaches to find one that produces the best volumetric match.

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The Greater Burgan field in Kuwait is the largest clastic oil field in the world. Its sheer size, complex geology, intricate surface facility network, over 2,200 well completions and 65-years of production history associated with uncertainty present formidable challenges in reservoir simulation. In the last two decades, many flow simulation models, part-field and fullfield, were developed as reservoir management tools to study depletion plan strategies and reservoir recovery options. The new 2011 Burgan reservoir simulation effort was not just another simulation project. Indeed, it was a major undertaking in terms of technical and human resource. The model size, innovative technology, supporting resources, integrated workflows and meticulous planning applied to this project were unprecedented in the history of the Greater Burgan field development. This paper describes work done to prepare a representative numerical model which could be utilized to optimize the remaining life of the reservoir complex. Right from the onset, representative numerical modeling concerns were identified. These led to a systematic collaboration framework being built in place between the static and dynamic modeling teams. Calibration of the model to the historical observations was executed at three levels, Global, Regional and Wells -the Cascade Approach. The cascade approach was designed to enable a concerted model calibration effort in accordance with the recurrent data quality. For instance, while the total field production history attains a high degree of accuracy, the data at the regional Gathering Center (GC) is of a lower level of certainty, but far more reliable than the data at an individual well. Commercial modeling software have been utilized extensively to produce several utilities such as water encroachment maps, Repeat Formation Tester (RFT) matching tools and aquifer definition and adjustment workflows. Subsequently, synergy in the integrated use of these tools produced a robust model calibration process on all three levels in the cascade approach.The main goal of the project -development of a predictive simulation model, always remained at the fore of the project team's mind during the model calibration. Check-point prediction models were defined and constructed at regular intervals during the model calibration phase. This approach allowed qualitative assessment on the evolution towards a representative numerical model. Furthermore, it allowed synchronizing simulation workflows and expedited project deliverables. The overall result was a sound full-field reservoir simulation model that achieved a good match of production, pressure and saturation histories, leading to reliable forecasting of oil recovery under different development scenarios.

The Greater Burgan field in Kuwait is the largest clastic oil field in the world. Its sheer size, complex geology, intricate surface facility network, 5,000 well-completions and 68-years of production history represent formidable challenges in reservoir simulation. In the last two decades, many flow simulation models, part-field and full-field, were developed as reservoir management tools to study depletion plan strategies and reservoir recovery. The new 2013 Burgan flow simulation was a major undertaking in terms of effort and financial cost. The model size, innovative technology, supporting resources, integrated workflow and meticulous planning applied to this project were unprecedented.As the Burgan field has matured over time, the reservoir pressure has declined in certain areas, with associated reduced productivity. The reduction of wells' productivity, combined with the increasing water production, has necessitated improved oil recovery (IOR) initiatives in order to support the Kuwait Oil Company (KOC) 2030 strategy, sustaining oil production and ensuring high recovery from Burgan reservoirs. This paper describes the development of a dynamic model to design pressure maintenance projects for optimal reservoir management and IOR strategies. The prediction model was built on a history matched model on three levels, Global (Field), Regional (Reservoirs / Gathering Centers) and Wells. These three levels depict the concerted history matching effort in accordance with the recurrent data quality. Details of geologic and dynamic modeling have been documented and presented in previous Burgan SPE papers and are not repeated in this paper.The primary objectives of the Burgan prediction model are meeting the production target profiles with optimal field development plans (FDP) and to maximize oil recovery. Two of the most promising projects are currently in different phases of development, Wara Pressure Maintenance Project (WPMP) and Burgan Sand Upper (BGSU-PMP). In this paper, only the WPMP is discussed in detail as the waterflood project is now entering operation stage after 10 years of planning and construction. BGSU-PMP is part of the Burgan FDP but is not focused within the scope of this paper.Sub-surface modeling in the giant Greater Burgan field complex is not just enormous, it is also arduous and challenging. The accomplishment by the team was momentous despite a less-than-expected result. Nonetheless, lessons learnt offered valuable information for future improvement. It has been a long and difficult journey from geological model to dynamic model over the last five years. Yet, in pursuing IOR and EOR, the journey has just begun.

Summary Nine multimillion-cell geostatistical earth models of the Marrat reservoir in Magwa field, Kuwait, were upscaled for streamline (SL) screening and finite-difference (FD) flow simulation. The scaleup strategy consisted of (1) maintaining square areal blocks over the oil column, (2) upscaling to the largest areal-block size (200 x 200 m) compatible with 125-acre well spacing, (3) upscaling to less than 1 million gridblocks for SL screening, and (4) upscaling to less than 250,000 gridblocks for FD flow simulation. Chevron's in-house scaleup software program, SCP, was used for scaleup. SCP employs a single-phase flow-based process for upscaling nonuniform 3D grids. Several iterations of scaleup were made to optimize the result. Sensitivity tests suggest that a uniform scaled-up grid overestimates breakthrough time compared to the fine model, and the post-breakthrough fractional flow also remains higher than in the fine model. However, preserving high-flow-rate layers in a nonuniform scaled-up model was key to matching the front-tracking behavior of the fine model. The scaled-up model was coarsened in areas of low average layer flow because less refinement is needed in these areas to still match the flow behavior of the fine model. The final ratio of pre- to post-scaleup grid sizes was 6:1 for SL and 21:1 for FD simulation. Several checks were made to verify the accuracy of scaleup. These include comparison of pre- and post-scaleup fractional-flow curves in terms of breakthrough time and post-breakthrough curve shape, cross-sectional permeabilities, global porosity histograms, porosity/permeability clouds, visual comparison of heterogeneity, and earth-model and scaled-up volumetrics. The scaled-up models were screened using the 3D SL technique. The results helped in bracketing the flow behavior of different earth models and evaluating the model that better tracks the historical performance data. By initiating the full-field history-matching process with the geologic model that most closely matched the field performance in the screening stage, the amount of history matching was minimized, and the time and effort required were reduced. The application of unrealistic changes to the geologic model to match production history was also avoided. The study suggests that single realizations of "best-guess" geostatistical models are not guaranteed to offer the best history match and performance prediction. Multiple earth models must be built to capture the range of heterogeneity and assess its impact on reservoir flow behavior. Introduction The widespread use of geostatistics during the last decade has offered us both opportunities and challenges. It has been possible to capture vertical and areal heterogeneities measured by well logs and inferred by the depositional environments in a very fine scale with 0.1- to 0.3-m vertical and 20- to 100-m areal resolution (Hobbet et al. 2000; Dashti et al. 2002; Aly et al. 1999; Haldorsen and Damsleth 1990; Haldorsen and Damsleth 1993). It also has been possible to generate a large number of realizations to assess the uncertainty in reservoir descriptions and performance predictions (Sharif and MacDonald 2001). These multiple realizations variously account for uncertainties in structure, stratigraphy, and petrophysical properties. Although impressive, the fine-scale geological models usually run into several millions of cells, and current computing technology limits us from simulating such multimillion-cell models on practical time scales. This requires a translation of the detailed grids to a coarser, computationally manageable level without compromising the gross flow behavior of the original fine-scale model and the anticipated reservoir performance. This translation is commonly referred to as upscaling (Christie 1996; Durlofsky et al. 1996; Chawathe and Taggart 2001; Ates et al. 2003). The other challenge is to quantify the uncertainty while keeping the number of realizations manageable. This requires identifying uncertainties with the greatest potential impact and arriving at an optimal combination to capture the extremes. Further, these models require a screening and ranking process to assess their relative ability to track historical field performance and to help minimize the number of models that can be considered for comprehensive flow simulations (Milliken et al. 2001; Samier et al. 2002; Chakravarty et al. 2000; Lolomari et al. 2000; Albertão et al. 2001; Baker et al. 2001; Ates et al. 2003). In some situations, often a single realization of the best-guess geostatistical model is carried forward for conventional flow simulation and uncertainties are quantified with parametric techniques such as Monte Carlo evaluations (Hobbet et al. 2000; Dashti et al. 2002). Using the case study of this Middle Eastern carbonate reservoir, the paper describes the upscaling, uncertainty management, and SL screening process used to arrive at a single reference model that optimally combines the uncertainties and provides the best history match and performance forecast from full-field flow simulation. Fig. 1 presents the details of the workflow used.