An Accelerated Immersive Capability Development in Unconventional Shale Resource
Bibliographic record
Abstract
Abstract The unconventional revolution in North America has attracted large investments from national oil companies (NOCs) and international oil companies (IOCs) eager to gain knowledge and add profitable unconventional resources to their asset portfolios. A key element of realizing this goal is the rapid and effective development of in-house technical personnel to support the effort. In 2008, a major NOC in Asia began its unconventional quest with an initial entry into a coal seam gas (CSG) opportunity in Australia, followed by investments in shale gas plays in the prolific Canadian North Montney area. To develop the much needed in-house capability, the NOC partnered with a major service provider to develop and deliver training programs to prepare its geoscientists and engineers to work within the unconventional division. Previous efforts by the NOC to develop in-house unconventional capabilities were hampered by a lack of data specific to their joint-venture shale assets. The newly established Unconventional Division had recently acquired an interest in Canadian Unconventional assets, and challenged the joint team to design a capability development program that would utilize this data, deliver practical experience, and build critical staff knowledge, in less than 6 months. The NOC agreed to provide data from their Canadian asset to enable the development of an accelerated, immersive program. The redesigned 10-week program used a source rock reservoir development workflow that incorporated a geoscience and engineering software suite to enable participants to review the field data and evaluate optimization opportunities. The goal-oriented tasks were to evaluate the project areas and develop the in-place gas resource to reach plateau production. The program was conducted entirely in Kuala Lumpur at the service provider's offices between September 8 and November 20, 2014, with development plan results presented to the NOC's management in Kuala Lumpur and Calgary. This paper presents the program development, challenges encountered through the preparation and deployment, final outcomes, and lessons learned.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".