Oil and Gas Production in Saudi Arabia: Comparison with the US Petroleum Sector
Bibliographic record
Abstract
The article discusses the current status and development prospects of the Saudi Arabian petroleum sector that is based on Saudi Aramco national oil company. Authors analyze resource potential and upstream policy of the kingdom. They study such competitive advantages of the Saudi oil and gas sector as low lifting costs and minimal carbon intensity, as well as Saudi Aramco’s strategic focus on maximization of the long-term value of hydrocarbon reserves. The article emphasizes that the following success factors enable the Saudi petroleum sector to maintain for many decades its leadership in the global oil and gas production: strategic vision of the kingdom’s rulers, constructive relations with international oil and service companies, wise fiscal policy, reasonable “Saudization” practices, partial privatization of Saudi Aramco, implementation of Saudi Vision-2030, efficient extraction of non-conventional hydrocarbon reserves, development and application of the state-of-the-art information technologies, and maximization of the human potential. A special focus is made on the comparative analysis of Saudi Arabia’s and the US energy policies, and their radical differences are identified. A conclusion is made that diverging models of Saudi and US petroleum sector’s development are equally viable given their specific political, economic and social environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".