Incident Prevention Planning for a Sour Gas Well-Field Operation
Bibliographic record
Abstract
Abstract Operations in a sour gas well field can create events that may lead tocomplaints from the local community. These events include sour gas releases, mercaptan discharges and flare operations. Conoco Canada Ltd. decided to useproven problem-solving and decisionmaking tools to create an incidentprevention plan for its seven well sites and related facilities in the Vulcan/Long Coulee area of Alberta. Process maps were used to characterize each operation and identify areas thatpotentially could lead to an incident. These operational points wererank-ordered to determine the most likely points at which incidents couldoccur. A team of experienced Conoco personnel representing operations;engineering; field services; and health, safety and environment was assembledto apply Systems Approach tools to finding alternatives that would help toprevent incidents. This led to the preparation of formal action plans that would be implementedthroughout Conoco's Vulcan/Long Coulee operations. Conoco's Vulcan/Long Coulee Incident Prevention Plan consists of the following information: Conoco is committed to continuous improvement of these operations with thegoal of preventing incidents. This paper will detail how this approach toincident prevention planning was implemented and what it would take to applythis approach in other areas. Introduction By providing companies with a set of analytical tools, the Systems Approachallows decision makers to achieve continuous environmental improvement. Using Systems Approach tools, S.E.A.L. International (S.E.A.L.) helped Conoco Canada Ltd. (Conoco) to identify effective ways of reducing gas emissions atits Vulcan/Long Coulee operations. Key strategies that contributed to theseaccomplishments included an increase in communication within the Conocoorganization and a higher level of involvement on the part of operators. Background Members of the public had complained about the odour in the vicinity of Conoco's wells in the Vulcan/Long Coulee area of Alberta, which is a sour gasfield environment. Because Conoco is strongly committed to adhering to itsenvironmental policy in addition to regulatory requirements, it wanted to adopta more proactive process for addressing emissions from its wells and relatedoperations in Vulcan/Long Coulee. It was felt that the Systems Approach wouldhelp Conoco to identify priorities for finding and implementing solutions tothis important problem. Furthermore, it was felt that the Systems Approachwould help Conoco to demonstrate to local residents the concrete actions it wastaking to protect their well being and safety. It was thought this wouldfacilitate communication with local residents before and after futureincidents, should they occur. Conoco was introduced to the Systems Approach tools in August 2001, when S.E.A.L. consultants used the tools to develop a Benchmark Assessment for Conoco's Vulcan/Long Coulee operations1. In October 2001, the SHEAR Advisor of Conoco's Vulcan operations attended a training course promoted by S.E.A.L. andled by Dr. Robert Pojasek, the developer of the Systems Approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".