Open AccessThe Use of Autonomous Marine Vehicles by the Hydrocarbon Industry: A Proof of Concept Mission
Author(s) -
Khalid Soofi,
Sudhir Pai
Publication year - 2018
Publication title -
marine technology society journal/marine technology society journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.23
H-Index - 43
eISSN - 1948-1209
pISSN - 0025-3324
DOI - 10.4031/mtsj.52.6.16
Subject(s) - petroleum seep , environmental science , marine engineering , proof of concept , current (fluid) , remote sensing , on board , meteorology , computer science , oceanography , geology , engineering , methane , geography , ecology , biology , operating system
Abstract A proof of concept and validation mission was conducted by ConocoPhillips to test the ability of autonomous marine vehicles (AMVs) to (1) collect and validate metocean data, (2) monitor loop and eddy currents, and (3) detect surface oil slicks with accompanying in-situ measurements. During this mission, four AMVs, two equipped with meteorological and oceanographic (METOC) sensors and two equipped with hydrocarbon (SEEP) sensors, were deployed. The METOC vehicles were equipped with Teledyne RDI Workhorse acoustic Doppler current profilers (ADCPs) and were launched to monitor and delineate eddy current features at near-surface depths in the vicinity of the ConocoPhillips asset, Magnolia platform in the Gulf of Mexico. The SEEP vehicles monitored areas identified with natural seep-related hydrocarbon surface expressions. With respect to mission objectives, the sensors were deployed in challenging oceanographic conditions, thus reducing demands on people and large vessels. Mission-based statistics per vehicle were gathered and tabulated capturing the total days at sea (59 days), distance traveled (881.45‐1526.49 nautical miles), and average speed (0.66‐1.08 knots) for validation of AMV as a platform. The mission showed a novel way of using technology that is safe and environmentally friendly and at a lower price point than traditional data acquisition methods. The AMV, hence, provides a substantial competitive advantage to acquiring data in a safe, reliable manner with greater operational efficiencies.