Applications of Autonomous Underwater Vehicles in Offshore Petroleum Industry Environmental Effects Monitoring
Bibliographic record
Abstract
Abstract Environmental Effects Monitoring (EEM) is an important tool in assisting Environmental Risk Assessment (ERA). EEM in the offshore petroleum industry has been conducted world wide, but traditional approaches have struggled to keep apace as exploration and production activities move to frontier regions, such as increasingly deeper waters and Arctic regions. This paper proposes the use of autonomous underwater vehicles (AUVs) for environmental monitoring of offshore facilities as a means of improving and expanding the overall monitoring program. The paper provides a review of technical and procedural issues involved in this application of AUV technology, including the current status of offshore oil and gas EEM, a review of available AUVs, and a survey of developments in in-situ sensors. Introduction Offshore petroleum industry operations affect the marine environment in a variety of ways: high sound levels from seismic surveys that affect marine animals; exposure of marine organisms to drilling mud, produced water discharges, and accidentally spilled oils; and the physical alteration of habitat due to the construction of submarine structures. The potential risks to the environment posed by offshore oil and gas operations support the need for effective Environmental Effects Monitoring (EEM) around the project development areas. EEM is a central component of environmental protection and management strategies designed to minimize the consequences of anthropogenic activities (GESAMP, 1991). It is a very important tool in assisting Environmental Risk Assessment (ERA) which is seen from many studies that link the EEM and ERA together (Wells, 2000; Smit et al., 2005). EEM is required by regulations governing industry activities in the offshore, and by government agencies in relation to cumulative impact assessment studies (Cott, 2003). The United States started the use of environmental monitoring programs in 1973. The Mineral Management Services is currently responsible for managing oil and gas activities on the outer continental shelf (OCS). In the early stages of EEM programs, MMS monitored the effects of petroleum exploration activities on the George's Bank, Middle Atlantic OCS, and Gulf of Mexico. Early monitoring programs mainly focused on the effects of drilling wastes on benthic communities through a variety of sampling methods, such as camera transects, crab traps, bottom trawls, and box corers. The MMS also has monitored the effects of petroleum development and production activities in the Gulf of Mexico, Santa Maria and Western Santa Barbara Channels off California, and in the Alaska Beaufort Sea. Trace metals and hydrocarbons in the water column, sediments, pore waters, and biological tissues are collected and analyzed. A more detailed description of U.S. EEM programs can be found from Ahlfeld (2005). In Canada, both government agencies and operators have carried out EEM. For example, Petro-Canada collected sediment samples from 49 stations and water samples from 24 stations in an area located in the vicinity of the Terra Nova oil field during 2000 to 2001. Analyses of samples include hydrocarbon concentration, metal concentration, particle size, and the presence of sulphur, sulphide, and ammonia (DeBlois et al., 2005).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".