Modelling of Sub-Sea Gas Transmission Pipeline to Predict Insulation Failure
Ode Samson Chinedu1, Okoro Emeka Emmanuel2, *, Ekeinde Evelyn Bose1, Dosunmu Adewale1
Identifiers and Pagination:Year: 2018
First Page: 67
Last Page: 83
Publisher Id: TOPEJ-11-67
Article History:Received Date: 15/08/2017
Revision Received Date: 18/04/2018
Acceptance Date: 02/05/2018
Electronic publication date: 31/05/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Thermally insulated subsea production and transmission systems are becoming more common in deep-water/ offshore operations. Premature failures of the insulation materials for these gas transmission pipelines have had significant operational impacts. The ability to timely detect these failures within these systems has been a very difficult task for the oil and gas industries. Thus, periodic survey of the subsea transmission systems is the present practice. In addition, a new technology called optic-fibre Distributed Temperature Sensing system (DTS) is now being used to monitor subsea transmission pipeline temperatures; but this technology is rather very expensive.
However, this study proposed a model which will not only predict premature insulation failure in these transmission pipelines; but will also predict the section of the transmission line where the failure had occurred.
From this study, we deduced that in gas pipeline flow, exit temperature for the system increases exponentially with the distance of insulation failure and approaches the normal operation if the failure occurs towards the exit of the gas pipe. This model can also be used to check the readings of an optic-fibre distributed temperature sensors.
Result and Conclusion:
After developing this model using classical visual basic and excel package, the model was validated by cross plotting the normal temperature profiles of the model and field data; and R-factor of 0.967 was obtained. Analysis of the results obtained from the model showed that insulation failure in subsea gas transmission pipeline can be predicted on a real-time basis by mere reading of the arrival temperature of a gas transmission line.