Bibliographic record
Abstract
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191514, “Safe Choice—Operationalizing Human Performance Science in Decision-Making,” by Carla Santamaria, Jim K. Flood, Paul C. Schuberth, SPE, Jorge J. Morell, Jaime R. Hinojosa, and Justin Haddock, ExxonMobil, and Hugh O’Donnell, Eric Sandelands, Mel Cowan, and Alan Higgins, Ingenium, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. The complete paper discusses ExxonMobil’s focus on the next phase of its safety journey through advancement of individual awareness and decision making to help drive breakthroughs in the operator’s vision of safety. This methodology empowers and enables safe decision making at all levels of an organization by providing new knowledge and techniques, and links these to current behavioral-based safety practices. In particular, a program called Safe Choice, first developed for the ExxonMobil Hebron Project integration, hookup, and commissioning construction (IHUC) site in Newfoundland, Canada, is detailed. The paper also covers the implementation-blueprint process the operator has used to enable each business unit to adapt and further the codevelopment process for local needs while maintaining core•principles. Introduction For many years, the energy industry has understood both the moral and business imperatives for providing effective safety-management systems to keep employees, contractors, the communities in which they operate, and the environment safe. In pursuing its safety program entitled “Nobody Gets Hurt,” Exxon Mobil seeks performance breakthroughs that will eliminate not only fatalities and higher-consequence injuries and outcomes, but all hurts, including those of lower severity. The company has focused its efforts on operations-integrity management systems, process safety, transformational leadership, safety culture, and human factors that affect its systems and processes. Considerable study of human factors has led to a number of innovations, including classification of incidents on the basis of both hurt and potential-hurt levels. A detailed study of upstream events in 2015 found that although decisions made in the moment affected some incidents, many had resulted from decisions made sometimes days, months, or even years earlier. From this analysis, the company identified some higher-priority areas of focus for its safety-management systems, and, most importantly, identified a need for enhancing decision-making knowledge and awareness as a step toward conscious, safer decision making. Understanding the human factors behind decision making was critical to unlocking a breakthrough in performance. Recent efforts have focused on factors that influence individual risk tolerance, with more emphasis on helping identify what those factors are but less on how and why those influences work. The concept of risk tolerance has not always resonated with all workforce groups. However, framing risk tolerance as a series of decisions by individuals has made more sense across the organization. The company’s focus thus has shifted to providing a better understanding of the influences upon, and effects of, choice.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".