Development and Applications of Sustaining Integrated Asset Modeling Tool
- Tony T. Liao (BP Kuwait Ltd.) | Gloria E. Lazaro (BP America) | Alison Marie Vergari (BP America) | Donn Robert Schmohr (BP Exploration Alaska Inc.) | Nicholas John Waligura (BP America) | Michael H. Stein (BP EPTG Houston)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- February 2007
- Document Type
- Journal Paper
- 13 - 19
- 2007. Society of Petroleum Engineers
- 3.1.2 Electric Submersible Pumps, 2.3.4 Real-time Optimization, 3.1.6 Gas Lift, 5.4.2 Gas Injection Methods, 3.1 Artificial Lift Systems, 4.6 Natural Gas, 3.2.8 Well Performance Modeling and Tubular Optimization, 4.1.6 Compressors, Engines and Turbines, 4.3.4 Scale, 4.1.4 Gas Processing, 5.3.2 Multiphase Flow, 5.6.9 Production Forecasting, 4.1.2 Separation and Treating, 7.1.7 Intergated Asset Management, 4.1.5 Processing Equipment, 3.3 Well & Reservoir Surveillance and Monitoring, 4.4.2 SCADA, 4.2 Pipelines, Flowlines and Risers, 3 Production and Well Operations, 5.6.4 Drillstem/Well Testing
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- 652 since 2007
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Integrated Asset Modeling (IAM) is a process that combines reservoir, well, and surface-facility models to create a complete system for reservoir and well optimization. This methodology ensures that the interactions among all components are correctly simulated. To realize the full benefit of IAM models, it is critical that changing reservoir and well conditions are entered to keep the models up to date and valid. If an IAM model is not frequently and properly maintained to reflect new conditions, it will rapidly lose its value as it ceases to accurately predict well production rates and pressure drops in the system.
An application tool was developed to provide easy updating and maintenance of IAM models for production optimization, surface-network debottlenecking, and production allocation. This tool automates the routine tasks required to update and maintain large-scale IAM models. The unique feature of this tool is its ability to calculate well production rates in almost real time by feeding well operating parameters obtained from the SCADA system into updated well-performance models. These production rates can be used to allocate total volumes measured at gathering centers back to individual wells. In addition, engineers can keep track of well matching parameters, such as productivity indices or skins, in the process of automatically maintaining IAM models using the sustaining integrated asset modeling (SIAM) tool. Trends in these parameters can then be analyzed to diagnose potential well problems and select workover candidates. Application of this tool in business units (BUs) has consistently resulted in a 90% reduction in model maintenance and management time, a streamlined process to maintain and update IAM models, and an improvement in production-allocation accuracy. These improvements have constituted a step change in IAM model application effectiveness across asset teams within BP.
In a typical IAM model-update process, existing well models are used to match new production-well test data. If a model fails to predict the observed production-well test rate, it is updated by rematching to a new test. The task of updating well models is usually completed manually by the model owner and is highly labor-intensive. In accordance with a growing industry trend, engineers have assumed more responsibility in this area, and it has become a challenge to keep models updated. This challenge has driven an effort to automate routine tasks so that engineers can spend more time analyzing engineering problems in order to optimize well production and debottleneck the gathering network.
In recent years, the data required for updating, maintaining, and applying IAM models have become more readily available as data-acquisition technology has advanced. As Oberwinkler and Stundner (2005) point out, a new era of reservoir management is dawning. Our industry is aggressively integrating real-time data into reservoir-management workflow processes and turning high-frequency data into real value. Sengul and Bekkousha (2002) outline a vision for application of real-time data to production optimization. They point out that the key to success is seamless integration of data and minimization of human intervention in data capture and application. This paper presents a work process that automates many time-consuming and labor-intensive tasks, streamlines data flow, and uses high-frequency SCADA data to perform well-performance analyses. Specific application cases are presented to illustrate the process. The development and application of the SIAM tool represents a step forward from real-time data acquisition to production optimization and reservoir surveillance.
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