Heavy Oil: Development Challenges and Implementations of Technologies and Processes in an Investment and Carbon Constrained World
Bibliographic record
Abstract
This reference is for an abstract only. A full paper was not submitted for this conference. Abstract Heavy oil offers a large potential resource base for meeting the world's long-term energy needs, however these are more difficult barrels to recover and face a number of challenges. In the near term, the global economic crisis has led to a dramatic reduction in the hydrocarbon prices, which led to the slow down and / or reconsideration of the investment conditions and resulting in postponing few of the major complex and costly projects. There is also growing emphasis on reducing greenhouse gas emissions. On the other hand, it is it clear that long-term energy demand will continue to surge, due to a growing global population of over 3 billion and the rising standards of living emerging economies such as China and India. While there is still plenty of oil left in the ground, easy oil supplies are declining rapidly forcing the global petroleum industry to turn to unconventional oil deposits that are costlier to recover. The current estimate of extra-heavy oil and oil sands (IEA) is more than four trillion barrels-in-place with very large deposits in Canada, Venezuela, Russia, and the Caspian. Several GCC countries such as Kuwait and Saudi Arabia are exploring how to assess and unlock the huge and largely unexploited reservoirs of heavy crude. How much of the resource can be recovered will be dependent on emerging technology and processes. This paper will- Highlight the technical challenges in developing heavy and extra-heavy oil,- Focus on a range of technologies and processes that have been used to boost production in the past 40–50 years bringing examples from the Aera, California operational excellence in producing heavy and extra heavy oil, and- Outline the recent technology developments that are currently implemented and/or will be utilized in the next decades, such as In-situ Upgrading and SAGD projects in Canada and the TAGOGD in fractured carbonates in Oman. These field examples from Middle East and North and South America will be presented to demonstrate best practices and to provide insights on industry's future ability through technology to lower costs and increase the size of the resource base while increasing energy efficiency in operations, utilizing alternative solar energy for steam generation, and minimizing the overall impact on the environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".