Bibliographic record
Abstract
Hydraulic fracturing is at a crossroads. Industry is more interested in this technology than ever before, as evidenced by the explosion of SPE hydraulic-fracturing papers over the last 30 years (from 38 in 1975 to more than 500 in 2005). But who is doing the R&D? Ideally, service companies, operators, consultants, universities, and government bodies would do this equally. The priorities of any one group would never dominate, and a healthy division would exist between technology pull (from industry) and technology push (from academia). In an effort to better understand who is driving new hydraulic-fracturing technology, a group of hydraulic-fracturing papers in SPE's eLibrary was studied by reviewing the affiliation of SPE primary authors for 1975 vs. 2005. (The search phrase "hydraulic fracturing" was used with an average of 40% of the papers reviewed in each year.) It was determined that technical leadership has changed with time, with a reduction in primary authorship by operators and universities/government bodies (from 40 to 25% and 37 to 25%, respectively) and a corresponding increase by service companies (from 20 to 39%). This trend also was observed in the broader oil industry (Lovendahl 2003) and in the scientific community on an international level (Rapoport 2006). The reasons for this shift in technology leadership are clear. Many operator R&D programs have declined or have been deemed noncore and completely shut down. Many government bodies are receiving less federal support and are forced to rely on increasingly limited industry cooperative funding (Lovendahl 2003). The result is that most R&D is performed by service companies. This trend could result in hydraulic-fracturing innovation having a bias toward small-step improvement, rather than revolutionary technologies. Keeping hydraulic-fracturing R&D active is a multifaceted challenge. Hydraulic fracturing is at the highest demand ever. R&D from the full range of industry groups and academia is essential for significant technical and commercial advancements to continue. Hydraulic Fracturing additional reading available at the SPE eLibrary: www.spe.org SPE 99428 "A New Approach to Hydraulic-Fracturing Modeling—Fully Coupled With Geomechanical and Reservoir Simulation" by L. Ji, SPE, U. of Calgary, et al. SPE 102326 "New Results Improve Fracture-Cleanup Characterization and Damage Mitigation" by J.A. Ayoub, SPE, Schlumberger, et al. SPE 106052 "Pinpoint Fracturing in Challenging Formations" by K.J. Beatty, SPE, Santos Ltd., et al. SPE 103591 "Use of Extremely High Time-Resolution Production Data To Characterize Hydraulic-Fracture Properties" by J.W. Crafton, SPE, Performance Sciences Inc., et al.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.236 | 0.181 |
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".