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 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 research paper is devoted to one of the engineering characteristics of the machine tool equipment, which largely determines the safe working conditions for machine operators and competitiveness that is the sound pressure levels at workplaces. The problem of reducing noise levels to sanitary standards at the workplaces of the machine operators is relevant for mechanical engineering and has an important scientific, engineering and socio-economic significance and it corresponds to the priority and advanced scientific directions of the Strategy for scientific and technological development of the Russian Federation (part 20 g “the possibility of an effective response of the Russian society to grand challenge with the interaction of man and technology ... ”It should be noted that in this direction a number of theoretical and practical works have been carried out to reduce the noise of the universal turning, milling, and abrasive wheel grinder machines.

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...

No abstract