Abstract:

The observation of operating parameters for the single-phase or multi-phase flows of oil and gas pipelines always plays an important role and prerequisite in daily operation. The inlet and outlet parameters of the pipeline such as pressure and temperature will greatly affect the efficiency of the production and transportation process of fields. These parameters are very important. It must be required to observe carefully and strictly. In fact, pressure and temperature signal gauges will always be set up at the inlet and outlet of the pipeline for continuously variable transmission of the signal to the central control room, then the operator can observe regularly. Alarms will be triggered when the signal goes out of the setting operation area. However, the operational parameters transmitted from this gauge are only available after the actual fluid flow passes through the pipeline. This will limit when the operator needs to apply calculation methods to pre-predict the flow regime, and operating parameters of the fluid in the pipeline when fluid flows has not passed through the pipeline. From that approach, the paper presents the result of research on the application of machine learning algorithms (ML) to build models to predict operating conditions of three-phase flows in the pipeline at Hai Thach- Moc Tinh field based on input parameters such as the open of wellhead flow control valve of wells located in wellhead platform WHP-MT1, WHP-HT1, and the commercial gas volume. The results of the research show that the ML calculation method gives the result which is only +/-2 barg/20C difference compared to the field data obtained from pressure (HT1-PT-0911) and temperature (HT1-TI-0911) signals set up at the outlet of the transportation pipeline at the Hai Thach-Moc Tinh field.