Name: VINICIUS SCARDUA DELLACQUA
Publication date: 25/05/2017
Advisor:
Name |
Role |
|---|---|
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| JULIO CESAR SAMPAIO DUTRA | Co advisor * |
| LUCIA CATABRIGA | Internal Examiner * |
| WELLINGTON BETENCURTE DA SILVA | Advisor * |
Summary: Flow assurance of oil transportation has become a subject of study since the extraction of oil in ultra-deep waters wells hit. Amongst the challenges, the deposition of solids in the transport ducts, which appear due to the cooling of the system when a line stop occurs, causing a partial or total blockage in the ducts and consequently unexpected costs. Methods such as chemical inhibitors or the Nitrogen Generation System are used to prevent deposition of solids, but their dosage and efficiency control are difficult to measure. The Pipe-in-Pipe (PIP) system is a technology developed that combines the use of thermal insulation and active heating of the ducts for temperature control, preventing PIP from reaching the temperature of the solids formation. Thus, this dissertation proposes a temperature control system using a model-based predictive controller associated with the particulate filter (PF-MPC) to prevent the temperature drop in the PIP. This control scheme associates the reduction of the uncertainty of temperature measurement made by the PF with the optimum manipulation of the heat flux generated in the active heating necessary to avoid the cooling of the PIP. PF-MPC uses the mathematical model of the PIP to predict your temperature within a future prediction horizon, from a single-point measurement of the PIP, the calculation of the current instant control action in order to prevent the fall of temperature. The controller estimates the ideal heat flow that should be applied, reducing economic costs related to active heating energy consumption. The results show that the PF-MPC allows a good performance in temperature control, using low values of heat flow, maintaining its objective of reducing economic losses in the transport lines.
Key words: Pipe-in-Pipe, State Estimation, Particle Filters, Model Predictive Control.
