Bibliographic record
Abstract
Discover a Career - Apache’s George E. King talks about well completions engineering. Well completions engineering is very likely the junction point of every other technical discipline in the oil industry. Feed-ins from geosciences and drilling must be balanced with predictions from reservoir engineering and requirements from production to deliver a fit-for-purpose well design. All this must be contained in a package that retains sufficient flexibility to handle life-of-well changes while retaining well integrity beyond the designed life of the well. Expected well life may range from a few years in deep water to more than 70 years in tight gas applications. Conditions range from the pressure and temperature fluctuations of ultradeep water to those of long horizontals and 20- to 50-multistage-fracture stimulations in shale oil and gas completions. The extremes of pressure and temperature are continually increasing, and environmental requirements are a moving target. Because of the engineering complexity involved, completions engineering is where a large amount of new technology enters the industry. If you are interested in engineering challenges, then take a closer look at well completions engineering.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".