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.

The Wara Sandstones formation is one of the main reservoirs of Greater Burgan field in Kuwait, producing under primary depletion since the late 1940s. A major water flood has recently begun and prior to this, a large-scale pilot (Early Wara Pressure Maintenance Project – EWPMP), has been initiated. As part of the scope of this study, representative geological models have been built to improve reservoir characterization to capture reservoir heterogeneities in the EWPMP area, which is crucial in building a dependable simulation model. An innovative workflow combining geological (cores), petrophysical (RCAL, Rock-Types) and dynamic data (pressures), has been developed to generate a range of geological models, that will be later on screened and selected for dynamic simulation. For a better representation of the sedimentological settings, five cored wells have been reviewed, to establish the main markers used for the geological modeling and to define core-based depositional environments. Six Rock-Types, calibrated on cores, and integrating RCA porosity-permeability data have identified in 56 wells to model the reservoir. The object-based modeling (OBM) approach combines aspect ratios and depositional trends to constrain the petrophysical properties distribution. The Wara Formation has been deposited in tidally influenced fluvio-deltaic to estuarine environments. Six depositional environments have been defined on cores, dominated landward by bay head fluvial delta that laterally passes into tidal estuarine mouth bars and sandy estuarine bay. They have been extended to 111 wells in the area based on log signatures and patterns. Based on analogs from similar ancient and modern deposits, aspect ratios for tidal bodies and sand body shapes were used in addition to the wells control to constrain the distribution of depofacies. Variations in sand body's size and shapes were used to generate poorly connected, fairly and highly connected sand bodies, giving a range of uncertainty to the models. The final sand body distributions have been validated using pressure data to match some pressure breaks related to shale barriers in the reservoir. Once the geological framework has been built and validated, Rock-Types and petrophysical properties distributions were generated in the pre-defined geological framework, using a sequential indicator simulation approach. The OBM approach allowed generating a range of models that reflects the geological settings and that better capture the reservoir heterogeneities and connectivity (assessed through the body geometry). The resulting generated petrophysical properties are then more geologically related. Modeling complex reservoir heterogeneities in clastic environments is a challenge in the oil industry. An accurate sand body distribution is crucial for a good understanding and representation of the reservoir behavior in both static and dynamic models. The proposed innovative object-based modeling workflow that combines geological, dynamic and petrophysical data, used in this study may be a good alternative for geological models of similar depositional environments, to assess the complexity of such particular reservoirs.

A major numerical modelling project was performed with the objectives of develop a more robust model for development planning studies. The second was to gain a better understanding of "reservoir dynamics", in particular aquifer influx, the lateral pressure distribution and gross fluid movement within the major reservoirs of the Wara–Burgan sequence and the flow between them. The challenges and implemented solutions of history matching a reservoir model of a huge, complex field with multiple production zones, many wells and large volumes of production and surveillance data are described in the context of a recently completed study of Wara–Burgan reservoir in the giant Greater Burgan field. Additional challenges due to possible mechanical problems in wells and uncertainties in production and injection data, as usually experienced in mature fields, are also discussed. The work started with reviews of basic engineering data, previous simulation studies and the regional geology. It was the first project for this field in which modern assisted history matching (AHM) techniques were applied. The main enablers for this were increased computational resources and the availability of new generation high-performance reservoir simulators. AHM techniques were used to help better define "high level" features of aquifer properties, pressure communication and gross fluid movement within and between the main reservoir units. A large emphasis was given to matching the pressure data from RFTs and cased-hole saturation estimates. A combination of AHM and more traditional calibration methods have enabled improved models of the Wara-Burgan reservoir to be developed. These models account for the gross aspects of pressure and fluid movements in and between major reservoir units and provide a reasonable match of the performance at field, reservoir unit and gathering center (GC) levels. The use of AHM techniques and special plots to assess the quality of the pressure match have enabled better characterization on permeability levels and allowed lateral pressure gradients to be better represented. As the match was refined, issues with well histories become more apparent and the approach to dealing with these problems is discussed. Matching the apparent remaining oil distribution was facilitated by extensive tools that allowed easy comparison of simulation with both saturation estimates at wells from cased-hole logs and to interpreted saturation maps. The available workflows, simulation tools and computing environment also allowed models with different levels of refinement (4 to 28 million cells) to be used to address concerns about numerical resolution and upscaling. Base "Do-Nothing" prediction cases were also performed. These gave some insight into how sensitive prediction results would be to model calibration assumptions. Development of the current representative numerical model for the main (Wara – Burgan) reservoir of the giant Greater Burgan field has allowed the major features of pressure communication, fluid movement and current pressure and fluid distributions all to be captured in a geologically plausible setting. The approach to using very large volumes of data, including log data, in the AHM work and the novel tools used to assist visualization of match quality will be discussed.

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