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.
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
8 December 2004
- Meeting paper published:
10 October 2004
- Revised manuscript received:
27 July 2005
- Manuscript approved:
10 March 2006
- Version of record:
20 February 2007
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