Digital Pipeline Network, Feasibility of Data Acquisition at Offshore Brownfield

Abstract

Recently digital pipeline system has been introduced, advocating the advantages on the simplification of pipeline monitoring, elimination of inspection activities as well as the accessibility of information especially on the wellness of the pipeline(s). For successful implementation of digital pipeline system, availability of Internet of things (IoT); which is the network of physical objects "things" that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet, is a must. The problems is IoT is rare or even non-existence for pipelines that have been there before 2005. Despite there are many oil and gas operators who have well advanced in control and monitoring their offshore facilities, they mostly monitor the only pressures and shutdown system. Other than that existing equipment in offshore facilities are often analogue and disconnected. A study on data retrieval feasibility to provide information to develop digital pipeline network for brown field facility arrived with very costly equipment installation. This is due the absent off communication system (DCS) and online measuring equipment (such as flow meter, pressure gauge and temperature sensor) at the required location of many offshore facility. This paper will discuss the feasibility and urgency of implementing of Digital Pipeline Network in offshore facilities most importantly at the existing and aging facilities. It will put across the importance of data retrieval, the required additional equipment and the facility for communication system as well as the assessment of installation hurdles at the aging facilities and the consequential cost requirement. Comprehensive data retrieval at brown field facilities can be achieved through rationalization between automated digital data online reading by installation of wireless IoT system (for equipment to measure pressure, flowrate and temperature) complimented with the existing communication system, manual data retrieval by site visit and network software analysis in order to reduce the material and installation cost while keeping the objectives intact. Pipeline OPEX will be subsequently reduced once AI based technology such as predictive and prescriptive assessment for effective pipeline monitoring and integrity management can be implemented and hence inspection and maintenance program for pipeline(s) can be optimized.