Technology Development Framework: Moving From Qualitative to Quantitative Decision-Making
Bibliographic record
Abstract
Abstract Technology development in the energy sector typically suffers from an ad hoc approach to making decisions about the key questions of "What is the next de-risking step?" and "At what scale?". This lack of structure in decision making occurs despite the technology development process usually being within a stage gated protocol. This often leads to significant waste of time and resources and plenty of "surprises" about the extent of additional testing required. The approach proposed in this manuscript is relatively simple to use, scalable, and transferable across industry sectors. Consistent application of the methodology has the potential to bring a quantitative rigor that can lead to more effective and efficient technology development and improve both intellectual and monetary capital allocation. Additionally, the framework provided facilitates a more structured and faster learning curve for people moving into technology development roles. Starting with a high-level understanding of the technology and business case, the methodology identifies possible commercial deployments and associates key technical and commercial risks that can be barriers to commercialization. Then, using a "Risk Burndown" approach based around an initial assessment of Probability of Success (POS), different Paths to Deployment (PTD) are developed. A Value of Information (VOI) approach is used to evaluate key metrics. This leads to a diverse set of de-risking options with associated trade-offs for informed decision making. The methodology has been developed and deployed since 2017 across a portfolio of both incremental improvement and game-changing subsurface technologies at Suncor Energy, a large integrated Canadian energy company. Over 25 technologies have been progressed using elements of this workflow with 7 of the more complex and expensive projects employing the full quantitative VOI approach. These analyses have been used to inform decision making on over CAD 400 million of proposed de-risking activity spend.
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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 | 0.093 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".