A Framework for Enhancing Engineering Deliverables to Improve Construction Performance in Oil and Gas Projects
Bibliographic record
Abstract
Alberta’s oil industry is one of the largest constituents of Canada’s economy, and will remain a key determinant of the nation’s economic growth for the foreseeable future. Existing research conducted on the performance of Alberta’s oil industry capital projects reveals that construction cost overruns and schedule delays are among the leading contributors to capital expenditure in oil and gas projects. The significance of project cost and schedule growth has motivated industry and academia to initiate a great amount of research identifying the factors affecting construction performance in oil and gas construction projects. Problems in the project engineering phase, along with many other factors, have been identified as a root cause leading to cost and schedule slippage in construction within oil and gas projects. The current study aims at bridging the existing knowledge gap of: (a) what factors in engineering deliverables are actually contributing to poor cost and schedule performance, and (b) how those factors can be mitigated during the process of projects. This research has been conducted in two phases to address those objectives. A quantitative research approach was adopted in the first phase to detect the issues in engineering deliverables, and a qualitative method was used in the second phase to identify the root causes that contribute to those issues, and the measures to mitigate them. In the first phase, the research data were collected through a questionnaire survey, and were quantitatively analysed to rank the identified issues by their impact on construction performance. In the second phase, interviewing was the main instrument for collection of data, which were then analysed using qualitative research techniques. Three major groups of issues were identified as the top-rank contributors to poor construction performance: engineering design issues, engineering schedule issues, and design changes after IFC (Issued for Construction) revision. The qualitative study in the second phase of the research revealed communications as the root of what needs to be improved to enhance engineering deliverables. Built on the foundations of the findings in the two phases of the research, a framework was developed for enhancing engineering deliverables to improve construction performance. The outcomes of this study can be used by oil industry project officials at different levels, to prevent construction cost and schedule growth, through implementing the findings of the research in project process, procedures, and other activities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".