Integrated Asset Management (IAM) solutions provides an excellent platform for combining data from multiple sources (subsurface to markets). The level of detail incorporated in such models determines the usefulness of such tools in reservoir management. The integrated modelling tools can be as simple as combining decline profiles to very detailed 3D numerical simulation models coupled to thermodynamic pipelines and facility network with risk management logic represented externally. This study makes an effort to address the issue of differentiating flow assurance models from forecasting tools. The advantages of using IAM tools with optimal detail for questions faced by an engineer are discussed through a real field application.
IAM solutions are becoming the norm in the industry to assist engineers and planners formulate sound investment decisions. Modern IAM tools have been documented to increase annual revenues in excess of 100 million USD. In this paper, we discuss the inputs required to build such IAM tools and the types of analysis typically conducted in such tools.
The role of flow assurance is inevitable in field development but increasingly we see the misuse of the technology under different circumstances. Here a case study is conducted on a real field Integrated Asset Model comprising of more than 20 gas/gas condensate reservoirs in Oceania. A typical network modelling tool is utilized to construct the entire field model with fields represented through pressure based type curves/ material balance tanks connecting platforms to facilities through pipelines.
The IAM model is used to conduct flow assurance study, field development optimization, short-term and long term forecasting. In each case, the effect of pipeline flow formulations, temperature tracking, velocity constraints etc., are discussed and its implications on model runtime and accuracy is presented. The understanding is important to make IAM more efficient and effective to carry out the different scenario analysis.
The results show the flow assurance model comprised of detailed representation of flow behavior is useful in field development planning but they tend to be very slow due to large amount of details required. The forecasting model with necessary inputs tends to produce similar results and proves to be effective in conducting large number of scenario analysis.
Recommendations are made to an engineer when looking to employ a detailed IAM model aimed at flow assurance as compared to forecasting studies. Guidelines are presented to planners/engineers on awareness of computational overhead when considering an IAM study.