Bibliographic record
Abstract
Abstract Offshore small projects (< $ 20 MM gross) often account for 15-20 percent of Oil and Gas Company's annual capital budget. Poor small project execution increases project costs and adversely affects production. Project management skills and habits developed in small project execution, good or bad, feed large project performance. Using small projects as a training ground for large projects improves small project efficiency and develops project professionals with skills to save hundreds of millions of dollars on a large capital projects program. The small project arena provides a dependable and renewable source of project management professionals at a time when age demographics threaten long term project performance. The following paper describes a small project improvement initiative implemented to improve small project performance. The initiative focused on:evaluating current performancedeveloping common processes and tools andestablishing a global small project network to share best practices and facilitate global interaction. Introduction The offshore oil and gas industry spends billions of dollars annually on small projects. Small projects are defined here as projects with a total installed cost less than $ 20 MM gross. Small projects are often completed on operating facilities that can potentially affect large revenue streams. The cost of small projects is disproportionate compared to their potentially large impact to production and revenue. Taking the time and effort to train small project personnel pays two significant dividends:Small project performance improvesFuture large project leaders are trained and developed Small Project Improvement Initiative There are strong business and economic drivers to improve small project performance. The main drivers include: cost, schedule, results, operability, and safety. The following five-step program was developed to improve small project performance:Develop Standard Format for EvaluationConduct On-site InterviewsEvaluate Small Project PerformanceDevelop Action Plan to ImproveShare Learnings and Best Practices Develop Standard Format for Evaluation. The first step was to develop a standard format to evaluate small project performance on a global basis. A 150-question questionnaire or audit was developed covering the entire project cycle from how the project idea was originated through start-up and project closeout. The questionnaire covers the following areas:Project submission and screening processDecision criteria and selection processProject team formationFront end loading and planningUse of value improving practicesProject execution plan developmentContracting and procurement strategiesProject control systemsProject outcomes and closeoutLessons learned The questionnaire allows consistent evaluation and comparison of small project performance across multiple regions and countries. Conduct On-Site Interviews. Key business units were asked to identify 4-5 small projects for detailed review and to provide summary data on 6-8 other small projects. Ideally, these projects were completed in the last 6-18 months and represented their normal small project workload. A corporate specialist traveled to the business unit location to complete the detailed reviews. Normally, each detailed interview lasts
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".