Abstract Deep subsea gas pipelines are in place or being built in places like the Baltic, the Mediterranean, the Black Sea, and Southeast Asia. At full depth, the height of the water column can produce pressure that exceeds the gas pressure inside the pipeline, particularly toward the delivery end; in those regions, a loss of containment will result in water ingress into the pipeline instead of gas egress. We discuss the detection of water ingress in such pipelines. We present the results of multiphase simulation of ingress at several locations and sizes in two flow regimes. Time scale of the ingress depends strongly on location, which is equivalent to the difference between gas pressure in the pipeline and ambient external pressure from the water column. Larger sizes have greater tendency toward cyclic behavior in the simulator. The expectation that the accumulation of water in the pipeline would displace gas, producing a downstream flow surge, is shown to be naive. Instead, accumulating water decreases the gas flow cross-section, throttling down gas throughput and reducing delivery flow. At equilibrium, water ingress will act as a regulator, causing pressure at the point of ingress to be equal to ambient external pressure. Output of the multiphase simulation is converted to instrument measurements, then simulated in a conventional leak detection RTTM. Ingress is detected as a conventional leak prior to any water reaching the downstream delivery. Location information is partially inverted compared to conventional leaks, but properly corrected can be used to distinguish gas egress from water ingress.
Abstract There are occasions when one might want to use results from one simulator as input to another simulator, for example, when preparing data for a leak sensitivity study where data from an offline simulator is used to synthesize measurement data for a real time model. Alternatively, one might want to compare results between different simulators, as with the PSIG test cases. Clearly, ensuring the pipeline parameters and fluid parameters are the same in both simulators is an essential step in this process. However, nuances and differences in modelling philosophies can lead to differences in the results that can be difficult to explain. In this paper we present an incremental approach to ensuring that results between simulators match to within desired tolerances and, where they do not match, provide insight to where the source of the differences lie. The key parameters used to measure the agreement, or otherwise, between the simulators are explained and the effect of the different modeling parameters investigated. To demonstrate, the approach is applied to an offline single-phase simulator, an offline multiphase simulator, a spreadsheet model and a Real Time Transient Model. In the example presented the various steps in the process are described and differences in modelling approaches are highlighted. Finally, we demonstrate that, for the example used, excellent agreement between the different simulators can be achieved. Motivation and Overview The reasons for wanting to ensure that different simulators agree are many and varied. For example, we might want to generate data using one simulator for consumption by another simulator, such as when undertaking a leak sensitivity study. Another example would be independent verification of a flow assurance study.