A network modeling campaign for 15 surface gathering centers involving more than 1800 completion strings has helped to lay out different risks on the existing surface pipeline network facility and improved the screening of different business and action plans for the South East Kuwait (SEK) asset of Kuwait Oil Company. Well and network hydraulic models were created and calibrated to support engineers from field development, planning, and operations teams in evaluating the hydraulics of the production system for the identification of flow assurance problems and system optimization opportunities. Steady-state hydraulic models allowed the analysis of the integrated wells and surface network under multiple operational scenarios, providing an important input to improve the planning and decision-making process. The focus of this study was not only in obtaining an accurate representation of the physical dimension of well and surface network elements, but also in creating a tool that includes standard analytical workflows able to evaluate wells and surface network behavior, thus useful to provide insightful predictive capability and answering the business needs on maintaining oil production and controlling unwanted fluids such as water and gas. For this reason, the model needs to be flexible enough in covering different network operating conditions. With the hydraulic models, the evaluation and diagnosis of the asset for operational problems at well and network level will be faster and more effective, providing reliable solutions in the short- and long-terms. The hydraulic models enable engineers to investigate multiple scenarios to identify constraints and improve the operations performance and the planning process in SEK, with a focus on optimal operational parameters to establish effective wells drawdown, evaluation of artificial lifting requirements, optimal well segregation on gathering centers headers, identification of flow assurance problems and supporting production forecasts to ensure effective production management.

This paper discusses the development of full pore-to-process integrated asset models (IAM) for the Greater Burgan (GB) oilfield in Kuwait, the largest clastic oil field in the world. The IAM links the reservoir model with the multiple wells, pipelines, network models and process facilities models for improved forecasting and operational excellence in the South and East Kuwait asset of Kuwait Oil Company. The main objective behind the development of this integrated asset model is to enable enhanced asset management and to improve decision making, accounting for the complex interactions and synergies between reservoirs, production networks and process facilities in the hydrocarbon flow path all the way from the reservoir to the export points. The IAMs were developed using calibrated models built using next-generation simulators that enabled the running of forecast scenarios from the pore to process. The reservoir model was developed using a high-resolution reservoir simulator that enabled the simulation of this giant oilfield with more than 2000 wells in a few hours. The reservoir model was then coupled to the full-physics well-and-network models for 3 gathering centres of key interest which had also been previously calibrated to match wells and manifold rates and pressures. Finally, the network model was connected to the high-performance process facility model at the manifold headers. The reservoir-network coupling was done at the well level, each well coupled at the bottomhole with an updated IPR passed to the network and a resulting outflow constraint passed back to the reservoir every timestep to capture any effects of pressure regime established in the network. The network-process facility connection was established by using a feedforward push of the calculated mass flow rate, pressure, GOR, water cut, and temperature at the manifold, as updated boundary conditions to estimate the quantity and quality of fluids produced from the facility. The results of the integrated models showed moderate impact of the network on the performance of the reservoir over a 5-year forecast. Integration of the vast number of wells and network models with the crude processing facilities in a single IAM platform enables the evaluation of oil production improvement opportunities in terms of their long-term dynamic impact on the reservoir management. The IAM models will help to identify the bottlenecks in the system, optimize the production and achieve the aggressive oil target of the GB asset. This is the first set of fully first operational IAMs for Greater Burgan that includes all three key components – reservoir, network, and process facilities. The IAM gives access to control and define constraints in all the component models, making it an effective tool for further analysing development and optimization strategies for improved asset management of the largest clastic oilfield in the world.

KOC-SEK asset has the main goal of maintaining, increasing, and optimizing the production of the Greater Burgan, the largest clastic reservoir in the World. Discovered in 1938, Greater Burgan is a multilayer formation with large hydrocarbon volumes of different oil type (from heavy to light) for which a detailed characterization and exploitation strategy was required to develop a sustained asset production. With the asset facing multiple challenges to maximize recovery and sustain target production field rate, one of these challenges has been to optimize the segregation of a large number of wells into 15 gathering centres. This is to ensure de-bottlenecking and to address back-pressure issues of the production networks while flowing wells in their optimal operational envelope to avoid the rapid depletion of the reservoir and the increasing water production. Due to the vast number of wells, the intricate network, the limitations in the gathering system and the dynamic operations, maintaining and sustaining the reservoir performance requires continuous surveillance, rapid and robust understanding of the different variables at reservoir, well and network level to make timely decisions and maximize reservoir recovery. To support this, a standardized and integrated system based on data analytics and numerical models, has been developed in the context of Realize the Limit (RTL) of the Greater Burgan reservoir as a tool for the quick identification of potential oil gain opportunities, the evaluation of bottlenecks across all the production system, and track and measure the impact of different proposed operational and development scenarios to unlock potential production and support optimum production forecasting. The system has been developed in a digital framework, with inputs from a multi-disciplinary team to define the datasets, calculated parameters and visualization requirements related to reservoir properties, well completion status, operational parameters, and key indicators from subsurface and surface hydraulic models. These has been integrated to identify key performance issues involving several aspects of the reservoir's development and operational plans. Also, the process provides a high-level overview and a platform for all asset groups including management, field development and operations, where observing the same set of results can initiate collective decisions that improve wells, network and facility management and enhancement recommendations.