TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractAn integrated asset modeling approach was used to evaluate gas compression and operating strategies for six gas fields in offshore Trinidad. Material balance and well models were linked to a pipeline network model. These IAM models were successfully used to evaluate the benefits of compression and looping of a pipeline segment. The modeling allowed the impact of interference of fields, different sand units within a field, wells, and facility constraints to be quantified. Compression could increase the gas recovery from six Trinidad fields by between 8.8% and 11.7 % of the original gas in place.
Summary 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. Introduction 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.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractAn 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.
The accuracy and precision of well rates are paramount in reservoir management, well performance surveillance, flow assurance, and any third party processing arrangements. Rate allocation is traditionally based on well rate tests and downtime. This method is usually time-consuming and thus performed relatively infrequently. This could be inadequate for proactive asset management especially with wells that may produce in transient state.This paper discusses methodology to improve well rate allocation quality and save engineering time. In practice, many fields face some or all of the following challenges that are related to well rate allocation: 1) reservoir communication, 2) well interference, 3) changing skin factors or other near wellbore boundaries, 4) uncertainties around reservoir fluid properties, and 5) difficulty in obtaining well rate tests for a variety of reasons. For many intelligent wells, it is a common practice to install permanent downhole gauges which are playing critical roles in field management. This paper describes a framework on how to capitalize on the real time data from well pressure and temperature sensors and use the data in Integrated Asset Modeling (IAM) to allocate the well rate with enhanced accuracy, increased frequency, and reduced processing time.The paper uses Atlantis in Gulf of Mexico as an example to demonstrate this process. The real-time data supported model based allocation process becomes virtual flow meters for the intelligent wells. For Atlantis, field-wide allocation accuracy has been improved from previously +/-10% error using the traditional allocation method based on well rate test and downtime method to current +/-3% error using the new method. This paper also shows model maintenance is a journey that needs strenuous attention especially after water injections commences and more production wells come online.
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