Where in the World are Canadian Oil and Gas Companies? 2017
Bibliographic record
Abstract
Studying the year-over-year global presence of Canadian oil and gas companies in 218 countries provides a comprehensive picture of industry trends and patterns of exploration, production and services abroad. The Where in the World (WIW) project began tracking these trends in 2011. This paper examines data collected in 2017, which reveals a reduction in the level of Canadian companies’ activities overseas between 2015 and 2017.In 2017, 118 Canadian oil and gas companies operated in 73 countries worldwide. 2017 highlighted the broad decline of Canadian oil and gas companies’ activities, in terms of the total number of Canadian companies operating internationally, the number of countries that the companies are active in, and the total Canadian produced levels of oil, natural gas liquids (NGL) and natural gas.There were 79 Canadian exploration and production (E&P) companies that were actively engaged in operations in 55 countries in 2017. This was a decrease of 29 companies and 22 countries from the 2015 Where in the World study. Canadian E&P companies produced a total of 721,488 barrels of oil equivalent a day (boe/d) of oil, NGL and natural gas in 30 of the 55 countries where Canadian activity occurred, a decrease of 36 per cent in comparison to 2015. In total, Canadian international production of hydrocarbons in 2017 was 0.497 per cent of the total production of international hydrocarbons.North America continues to be the primary destination for both Canadian E&P and service companies’ international activities and operations. Of the 118 Canadian oil and gas companies that operated internationally, 67 operated in the United States.In 2017, 39 service companies spread their expertise and technology to a total of 43 countries, 11 fewer countries than in 2015, and three fewer in comparison to 2011, the year the Where in the World study began. It is noteworthy to highlight that the major reduction in the presence of service companies occurred in Africa, which accounted for approximately 21 per cent of this decrease. Nine of the 11 countries, or 82 per cent of the countries, where service companies no longer operate were located in Africa. Overall, 2017 will be distinguished for the notable and continued decrease of Canadian oil and gas companies compared to 2011, when 255 companies were active in 106 countries, and 2015, when 161 companies were active in 96 countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".