Benchmarking A Concept ─ Data-driven Commercial Valuation Of A Hypersonic Impact Drilling Concept
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Commercial valuation of a technology in Proof-of-Concept stage is often based on limited case study data, and then extrapolated to a hypothesized total market demand for that technology. The methodology presented in this paper uses a bottoms-up, data-driven, well-by-well valuation using a 60,000+ well industry benchmarking data set. The methodology was developed to support the valuation of a new technology concept using hypersonic impact drilling, then at API-17N Technology Readiness Level 1. Any new technology has a low definition of operational performance and technical capability by virtue of being in concept stage. The well dataset used for valuation analysis is relatively high-level, resulting in a significant number of assumptions and limitations. Nonetheless, the combination of a granular technology model with a large actual dataset provides insights into sensitivities and uncertainties which are unobtainable with a broad-brush, high-level approach. Based on the information available in the database, the methodology constructs a synthetic time-depth curve for drill and case operations after removal of non-productive time. Synthetic time and cost for each section are calculated for both the actual well and the technology model allowing section-by-section ‘bench-marking’ of the technology. The combined savings from technology-positive sections gives the size of the prize or commercial margin available to be shared between Operator and Supplier. We present a case study in which we modelled the initial new technology deployment concept, showing this concept to have an operational sweet spot with value rapidly decreasing away from it. An alternative, downhole deployment concept resulted in a multiple times wider applicability and a multi-billion-dollar un-risked value proposition indicative of a potentially game changing technology. Based on this new insight, the technology developer was able to pivot early on, probably avoiding costly dead-end development and market disappointment, and increase industry and investor confidence and investment. The methodology can be used to gain actionable insights at multiple levels:To obtain a mature market valuation, mature technology parameters such as reliability, directional drilling capability and all applicable hole sizes and depths are invoked.To aid the technology development and design decisions, sensitivity analyses can be performed on design parameters.To guide development requirements, a Minimum Viable Product analysis provides insight to the minimum technical requirements necessary and the de-risking work required before a technology can gain acceptance in the marketplace.To explore early applications and potential sponsoring projects, clusters of potential high-value and/or early-applications can be identified.The results from this valuation model provide insights into the potential of wells at or beyond the fringes of the database, i.e. complex wells that require extraordinarily long net times to drill.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it