Strategies for Oil and Gas Asset Retirement Sustainability in Alberta, Canada
Bibliographic record
Abstract
Oil and gas companies in Alberta, Canada lose millions of dollars per year due to ineffective management of retired assets. Ineffective management of inactive oil and gas assets in Alberta has led to over 80,000 inactive wells, highlighting the practice of prolonged deferment of asset end-of-life costs. Using the corporate sustainability model and asset management concept model as frameworks, this multiple case study was conducted to explore the strategies that asset managers in small- and medium-sized oil and gas companies used to manage retired assets effectively to increase organizational sustainability. The population for the study included 3 business leaders of small- and medium-sized oil and gas companies in Alberta who implemented effective strategies to manage their retired assets. Data were collected through semistructured interviews with the leaders and review of artifacts including firm documents and websites. Data were compiled, disassembled into fragments, reassembled into a sequence of groups, clarified, and interpreted for meaning. Methodological triangulation and member checking validated the interpretations. Data analysis resulted in 7 themes: responsible leadership commitment, adoption and communication of corporate social responsibility philosophy, regulatory compliance, asset management software tools, dedicated inactive assets and reclamation champion/team, annual budget/long-term planning, and performance measurement/reporting. The findings may contribute to positive social change by providing insights for small- and medium-sized oil and gas business leaders on strategies for managing inactive assets and for fostering an environmental culture among employees that has beneficial impacts on their families and communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".