From Field Experience in Extreme Cold Conditions to Health and Safety Internal Guidelines for Future Projects
Bibliographic record
Abstract
Abstract TOTAL Exploration & Production has been active in cold environmentssince 1970 (i.e. drilling in the Arctic Islands in Canada) and has beenoperating the Russian Kharyaga field since 1999. For its first experiences inthose challenging conditions, TOTAL applied its internal rules andspecifications that were not aimed at this kind of environments but to" classic" prospects, the main assets being in the Guinea Golf, in the NorthSea, the Middle East and in South East Asia. As the prospects and TOTAL'sportfolio have developed in areas where temperatures are below −15°C, such asKashagan field in Kazakhstan or Yamal in Russia, an " Extreme Cold" taskforcewas put together several years ago. The aim is to gather feedbacks from thepast and to centralize the Research & Development activities to look forinnovative solutions for the future and on-going projects. The workgroup isorganized around several panels, one of those being the Health and Safetyaspects for the operations in Extreme Cold conditions. From partnership throughJIPs, internal research and workshops with affiliates (mainly in Norway, Russiaor Canada), and projects teams, it has been decided to produce internalguidelines in order to define and harmonize the practices, acknowledging thateach field has its own meteorological constraints, and to gather the resultsfrom the multiple actions carried out by TOTAL headquarters or affiliates. Themethodology and the risk analysis performed to obtain a common technical basiswill be here presented. Introduction TOTAL Exploration & Production (E&P) has been active in coldenvironments since 1970 (i.e. drilling in the Arctic Islands in Canada) and hasbeen operating the Russian Kharyaga field since 1999. To properly develop suchassets and increase its presence in these conditions, TOTAL E&P has to useits current skills and develop methods to ensure that its operations arecarried out with the best up to date practice while ensuring the health andsafety of workers and limiting its environmental impact in sensitive regions. Extreme cold conditions raise specific hazards and issues and increase riskscompared with " conventional" installations located in a less hostileenvironment. These harsh conditions may directly or indirectly impactfacilities, industrial operations and/or personnel. Analysis of all availablemetocean data, hindcast and forecast, is crucial for such projects in order tosafely design the installations and to maintain them for as long as the projectis being developed (30 years in this case). Climate change data are alsocrucial. The identification of such risks is necessary from the conceptualstage, as is an understanding of the local environment. As TOTAL's portfolio in extreme cold conditions continues to develop, it isnecessary to ensure that the feedbacks are integrated and that the propertechnologies and skills are developed. As will be described later on, this ismainly ensured via the Company own standards (through a set of requirements, recommendations and guidelines) and databases accessible internally.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".