The Impact of the Covid-19 Pandemic on Iranian Oil and Gas Industry Planning: A Survey of Business Continuity Challenges
Bibliographic record
Abstract
Abstract The Covid-19 pandemic has severely affected various aspects of life, and its compounding and cascading impacts have been observed in most industries and firms. The oil and gas (O&G) industry was among the first to experience the impacts as the pandemic began due to the global economic recession and a sharp decline in demand for oil. The pandemic revealed major risk management and business continuity challenges and uncovered some of the vulnerabilities of the O&G industry and its major companies during a prolonged global disaster. Examining and understanding how the Covid-19 pandemic impacted the O&G sector in different countries, considering their unique circumstances, can provide important lessons for managing the current and future similar events. This study investigated various impacts of the Covid-19 pandemic on the O&G industry using Iran’s Pars Oil and Gas Company (POGC) as a case study. Data were collected through in-depth interviews with key managers of the company. Qualitative methods, specifically thematic analysis, were used to analyze the data. Findings of this study provide further insights into how the pandemic impacted the operations, risks, and business continuity of the POCG. The results show that the pandemic caused significant operational, financial, and legal impacts by disrupting routine maintenance, reducing the availability of human resources under the public health measures and mobility restrictions, increasing processing and delivery times, increasing costs and decreasing revenues, and delaying contractual obligations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".