Factors Influencing Design Changes in Oil and Gas Projects
Bibliographic record
Abstract
Although many project time delays, cost overruns and quality defects—particularly in the oil sector—are attributed to design changes, the associated issues have not yet been thoroughly investigated. This study aims to provide a knowledge-based foundation for the evaluation and prediction of the impact of design changes on project performance, by identifying and analysing the main contributing factors. Using findings from a thorough literature survey, semi-structured interviews, and knowledge-mining from completed projects in the oil industry, a total of 28 factors were consolidated and grouped into four major categories. These factors were then ranked on their relative importance, based on data from a questionnaire survey distributed to a wide range of professions in the industry. An analysis of the results found that the most influential factors contributing to design changes are project scope definition, level of schedule overlapping, and project team's work experience, respectively. Ultimately, a predictive model has been proposed for determining the impact of design changes on project performance. The outcomes of this study form part of the authors' on-going research focusing on the analysis of the casual relationships between diverse factors to develop the predictive model.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".