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.
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.
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, 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.The quest began in 2009 with the construction of a Structural and Stratigraphic model, followed by Static modeling in 2010 and Dynamic modeling in 2011. Early dynamic model startup allowed integration between the static and dynamic modeling teams which resulted in a geological model suitable for reservoir simulation.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 second part of the project was to develop a predictive simulation model to be used as a reservoir management tool to forecast and evaluate reservoir development options for ultimate recovery. 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 f...
The Burgan-Wara formations from Southeast Kuwait (Greater Burgan field) constitute the largest known siliciclastic oil reservoir on earth. A specific workflow combining geological, geophysical and reservoir engineering techniques was developed to build a fully integrated and representative geomodel of the field.The sedimentology and stratigraphy were here reviewed in terms of depositional environment and lateral stratigraphic correlation. The variability inherent to the depositional style leads to a complex reservoir scheme. Lower Burgan is dominated by stacked braided channels representing homogeneous, high quality reservoirs. Higher in the stratigraphy, lateral facies variability and heterogeneities are observed in tidal dominated units. Mud dominated units occur within the Upper Burgan and Lower Wara formations and provide good sealing capacities. Small, laterally strongly variable and heterogeneous fluvio-tidal dominated units are representative of the Upper Wara Formation. A 3D geological model of more than 900 million cells was built, based on the new structural and stratigraphic framework interpretations to capture the complexity of the Burgan Field reservoirs. Rock-types were defined, based on more than 900 well logs and core petrophysical properties measurements. The seismic reservoir characterization, focused on inversion techniques and calibrated with the newly defined rock-types provided crucial information on sandstone proportions distribution, especially in areas with lower well control.The first attempt to simulate, at high resolution, the largest siliciclastic oil field in the world provides a comprehensive way to understand the field heterogeneities and behavior. The updated geological model, based on new interpretations allowed characterizing the major reservoir heterogeneities and has significant impact on the reservoir management of this giant field.
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