The Practical Applications Of Coupled Models From Reservoir To Sales Point For Fields With Highly Variable CO2 Content

摘要

This paper presents an integrated production model construction workflow with actual field application from four fields with multiple stacked reservoirs and with varying carbon dioxide (CO2) content (up to 60%) located in the Malaysia-Thailand Joint Development Area (MTJDA). The subsurface-to-surface linked coupled models are history matched to field performance and reconciled to the surface gas utilization (fuel, flare, drop-out, etc.) under specific operating conditions to meet sales specifications. The capability of the subsurface-to-surface linked coupled models is further enhanced with the modeling of the CO2 membrane unit to aid in development planning. This integration is expected to help to maximize the economic value from the assets by accurately predicting the reservoir performance with respect to the surface network constraints. The objectives of this project are to: 1. Produce more reliable sales forecasts 2. Generate representative reserves recovery estimates 3. Equally compare different projects in different fields 4. Estimate the timing of future developments to meet existing Gas Sales Agreements The construction workflow can be described in four steps: 1. Project Definition 2. Test of Concept 3. Quality Control 4. Forecast. Project work scope calls for a coupled model that is able to generate a fully-automated combined field deliverability forecast at specified sales CO2 content that takes into account the CO2 removal unit, intra-field pipeline network, and every developable reservoir within the area. This custom built simulation model is capable of generating incremental production profiles from the integrated dynamic simulation models (as opposed to tank models) to the sales point and its configuration allows a "plug and play" option to allow additional models to be integrated easily. This is an example of effective use of current simulation techniques to robustly and efficiently address the challenges of developing complex assets with highly variable CO content.