Application of Continuous Improvement Methods to the Petroleum Upstream Business
Bibliographic record
Abstract
Abstract Imperial Oil has a strong history and culture of continuous improvement (CI). Historically the benefits seen from CI were less in the upstream business than in the downstream and other areas with resulting skepticism on the method validity for the resources business. In recent years, Imperial Oil has tested a more aggressive application of CI methods in upstream operations with positive results. The technical methods for CI are broadly known. The approach taken at Imperial Oil Resources (IOR) was to develop the management techniques for CI initiatives. A project philosophy was used and processes were established for initiating, staffing, tracking and closing each initiative. Dedicated CI personnel were added and trained. Expectations were set for the delivery of results. Hindsight reviews have been established to verify the success of each effort. The toolset used was expanded beyond the published Lean and Six Sigma methodologies. Methods were added, such as those used for improving the profitability of a target business segment and for assessing reliability issues that have been proven in other business functions. An examination of the challenges encountered by CI specialists indicated the need for additional training in leading and influencing others. Project teams have also been assembled with experts external to Imperial Oil's upstream business using company staff from other business lines. Skills have been developed in engaging suppliers, contractors and regulatory agencies in IOR's projects. Positive results have been realized in a broad array of areas. Maintenance improvements, general operating cost reductions, energy efficiencies, environmental impact and volume improvements have been achieved in existing conventional and unconventional operations at surface facilities, plants, well servicing and support functions. In the second year of operation, the CI program at IOR implemented projects that will provide over 30 million dollars per year in ongoing financial benefits. In addition significant benefits were realized from the application of these methods to reduce operating costs in a planned oil sands mining project. Active projects are expanding efforts into other areas such as drilling.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".