Abstract An enabling application tool was developed for updating and maintaining Integrated Asset Models (IAM) for production optimization, surface network debottlenecking, and production allocation. The tool automates the routine tedious tasks required to update and maintain large-scale IAM models. Application of this tool in many BP Business Units (BU) consistently resulted in about a 90% reduction in model maintenance and management time, streamlined the IAM model application process, and improved production allocation accuracy. Deployment of this tool in the last few years has brought a step change to IAM model application across asset teams within BP.
Introduction IAM models bring together reservoir, well, and surface facility models to form an integrated system for reservoir and well optimization. This methodology ensures that the interactions between all components are correctly accounted for. The IAM approach has been discussed by Chow and Arnondin, and Zapata, et.al.
Once an IAM model is created and properly validated, it can be used in typical reservoir and well production optimization analyses such as:Identifying excessive pressure drops from the reservoir to the sales point
Evaluating the impacts on production by reducing the extra pressure losses in the system
Debottlenecking the surface network system
Optimizing lift gas distribution in a field with gas lift wells
Optimizing Electrical Submersible Pump (ESP) operating parameters for a field with ESP wells
Optimizing water injection with a fully coupled system consisting of production and injection subsystems
Evaluating the benefits of installing multiphase pumps or gas compressors
Using IAM models has become a common process in most of the BUs in BP. Asset teams have created and utilized fit-for-purpose IAM models in reservoir and well management, optimization, and surveillance. Figure 1 shows the IAM model for Pompano in the Gulf of Mexico (GOM). In this figure, semi-circles, triangles, and rectangles represent reservoirs, wells, and pipeline segments respectively. All the separators are also modeled. Some applications of this IAM model are described later in this paper in the section titled "Application of SIAM Tool in Pompano."
To realize the full benefits of the IAM models, it is critical to keep these models up-to-date along with changing reservoir and well conditions. If an IAM model is not frequently updated and properly maintained to reflect the new conditions, it will rapidly lose its value as it ceases to accurately predict well production rates in the system.
In a typical process to update an IAM model, existing well models are used to match the new well test data. If the models fail to predict the well test data, they are updated by rematching to the new test. The task to update these models is usually carried out manually by the model owners, and is labor intensive and time-consuming. In a growing trend, engineers have assumed more responsibilities and it has become a challenge to keep the models updated. The demand for keeping models up-to-date has driven the effort to automate the routine tasks; which therefore allows engineers to use their limited time resources more effectively in the analysis and solution of engineering problems to maximize production.
In recent years, the data required for updating, maintaining, and applying the IAM models have become more readily available as the data acquisition technology has greatly advanced. As Oberwinkler and Stundner point out, a new way of reservoir management is dawning on the horizon. Our industry is aggressively integrating real-time data into reservoir management workflow processes and turning the high frequency data into real value.
Sengul and Bekkousha outline a vision for application of real-time data in production optimization. They point out the key for success is seamless integration of data and minimizing human interface in data capture and application.