Bibliographic record
Abstract
Technology Focus As the industry continues to absorb the lessons, and capitalize on the possibilities, offered by big data, professionals have kept in mind that exploration is not the only boundary of the digital frontier. Production monitoring and surveillance requires heightened degrees of precision and efficiency as operations are streamlined and projects are evaluated continuously. This month’s feature highlights a trio of papers focused on innovative technologies that have been implemented successfully in environments ranging from the deepwater Gulf of Mexico (GOM) to China’s Shengli field. Paper SPE 196188 describes third-generation production-logging-tool technology that uses miniaturization and digitalization to maximize the effect of digital sensors in deepwater fields. The tool possesses a rotating functionality that has yielded robust and accurate data in GOM case studies. A real-time virtual flowmeter system is described in paper SPE 196654. In offshore fields, the system has provided multiple benefits, from estimation of production flow rates in a range of conditions to determination of whether multiphase flowmeters were working correctly. The challenge presented by abnormal production decline in mature fields in China is the subject of paper SPE 197365, in which an early-warning system based upon a machine-learning technique has been used to reduce training time and guide further development strategy. These technologies, like all others you may encounter in JPT’s Technology Focus features, are the result of our members’ hard work and willingness to share their important discoveries and insights through SPE conferences. We hope that our readers benefit from these innovations and are inspired to discover their own, perhaps by writing and presenting an SPE conference paper. Recommended additional reading at OnePetro: www.onepetro.org. SPE 196514 - Banyu Urip Reservoir Daily Well Deliverability Monitoring by Stephanie Sapti Putri Widyasari, ExxonMobil, et al. SPE 198053 - Developing the Talent of Future Petroleum Engineers by Remotely Monitoring Well Operations by Bashayer Al-Fadhli, Kuwait Foreign Petroleum Exploration Company, et al. SPE 198685 - Prognostics Thermal Well Management: A Review on Wellbore Monitoring and the Application of Distributed Acoustic Sensing for Steam Breakthrough Detection by Mohammad Soroush, University of Alberta, et al.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.170 |
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".