Framework For The Environmental Impact Factor For Drilling Discharges - A Proposed Tool For Risk Reduction, Management And Regulation Of Drilling Discharges
Bibliographic record
Abstract
Abstract Through a joint industry project, initiated by the oil industry, a risk based assessment model was developed, enabling quantification of environmental risk from discharges from drilling operations. This tool, applicable for decision support, establishing cost-effective measures for reducing potential harmful drilling discharges to the marine environment, is a further development of the Environmental Impact Factor (EIF) approach used for the management of discharges of produced water on the Norwegian Continental Shelf. The developed EIF for drilling discharges (EIFDD) assesses potential environmental impacts from the discharge of cuttings and drilling fluids. Potential impacts from particulates (e.g. barite) and chemicals in the water column and sediments are assessed. Additionally, potential physical stress from deposition of drilling mud and cuttings on the sea floor are evaluated. Principles for environmental risk assessment as described by the European Commission's Technical Guidance Document (TGD) and Species Sensitivity Distributions were incorporated. One of the main challenges was the application of risk assessment principles to non-toxic stressors, originally designed to evaluate toxic stress,. The EIFDD evaluates environmental risks from oxygen depletion, change in grain size and burial in marine sediments. In order to derive environmental threshold effect values for all stressors an extensive literature study was carried out. Besides that information resulting from decades of environmental monitoring around offshore platforms on the Norwegian Continental Shelf was used for both determination of thresholds and validation purposes. The conceptual framework of this state-of-the-art environmental risk management tool is presented, including the risk assessment principles, and methods to derive exposure and threshold effect values. The use of the EIFDD is illustrated with an example calculation.
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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) | 0.000 | 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".