Bibliographic record
Abstract
This year’s history matching and forecasting selections, made by reviewer Gopi Nalla of DeGolyer and MacNaughton, reflect the importance of accurate and innovative methodology in the approach toward development of unconventional or challenging plays, from tight oil to highly heterogeneous gas fields to coalbed methane. The authors of paper URTEC 208352 evaluate and compare the performance of rate-normalization and pressure-deconvolution techniques for both synthetic and tight-oil examples. While the synopsis is devoted mostly to the authors’ work in applying these techniques to synthetic cases, much of the complete paper is devoted to tight-oil examples. Ultimately, the authors recommend the pressure-deconvolution approach generally. In paper SPE 207933, the authors apply an integrated approach of using reservoir pressure/gas compressibility (P/Z) calculations to obtain a field gas initially in place (FGIIP) estimation that is then incorporated into an integrated asset model. The technique is applied to a giant onshore gas field. The authors conclude that the new FGIIP estimation can be applied as a reference to re-review the static modeling legacy and to narrow static modeling uncertainties, leading to reliable forecasting and more-efficient field development. An application of the iterative ensemble Kalman smoother to a scenario involving horizontal coalbed-methane wells for a low-permeability field in Australia is the subject of paper URTEC 208291. A forecast study was conducted to validate the history-matched ensemble, with the results showing a good match of 12 months of the new production data not used in history matching, highlighting the robust prediction capabilities of the presented approach. SPE papers continue to a be a vital resource for industry professionals; arguably, such work is more important than ever as the industry and the world adjusts to new modes of collaboration and realities in both the office and the field. I invite you to read the full text of these papers on OnePetro and to find further recent works that advance the literature of this specialized but critical Tech Focus topic. Recommended additional reading at OnePetro: www.onepetro.org. URTEC 208361 - Effect of Relative Permeability on Modeling of Shale Oil and Gas Production by Hamid Behmanesh, University of Calgary, et al. SPE 204835 - Successful Case Study of Machine-Learning Application To Streamline and Improve History-Matching Process for Complex Gas-Condensate Reservoirs in Hai Thach Field, Offshore Vietnam by Son Hoang, Bien Dong Petroleum Operating Company, et al. SPE 207855 - Unleashing the Potential of Relative Permeability Using Artificial Intelligence by Abdur Rahman Shah, Schlumberger, 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.121 |
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".