Bibliographic record
Abstract
Technology Focus Enhanced oil recovery (EOR) operations are designed to follow deterministic and detailed procedures, with planned actions for the people executing them and expected reactions from the system (reservoir) to which operations are being applied. However, a number of hurdles need to be overcome to reach a successful action/reaction, problem-free status in EOR operations. One challenge is that success depends on insightful subsurface knowledge. Nevertheless, as is well-known to people in the oil industry, information about key reservoir properties is scarce and limited to point measurements inside the wells and nondestructive remote data at much lower resolution. Thus, there is great uncertainty in the process of describing the reservoir, and EOR operations must be planned, developed, and managed in this uncertain environment. To reduce uncertainty, the strategy should be to focus at first on a robust EOR screening and design process, gathering as much reservoir data as possible. In the second stage, technically sound pilot studies can increase knowledge and reduce operating risk. Time and resources spent on those two topics have provided oil operators with improved EOR-management techniques, despite the underlying reservoir uncertainties, with fewer surprises and less stress and ambiguity. Like a gold-medal team in a synchronized swimming event, despite participants being outside their natural and well-known environments, training and teamwork are responsible for a unique, precise, and quite deterministic result. The papers featured this month will give you only a few examples of the vast amount of high-quality material that was published last year and will illustrate how EOR operations, though challenging, can be tackled on the basis of technological advances in data acquisition and careful analysis and interpretation of reservoir data during screening and pilot-study phases. I hope you will enjoy the reading and will search for additional papers in the OnePetro online library. Recommended additional reading at OnePetro: www.onepetro.org. SPE 164048 Novel Scale Remediation for Steam-Assisted-Gravity-Drainage Operations by Timothy Cheung, Shell Canada, et al. SPE 155123 Industrial Experience in Seawater Desulfination by Pierre Pedenaud, Total, et al. SPE 157996 Optimizing Water-Injection Rates for a Waterflooding Field by Feilong Liu, Chevron Energy Technology Company, et al. SPE 159620 A New Approach To Deliver Highly Concentrated Surfactants for Chemical Enhanced Oil Recovery by Julian Barnes, Shell Global Solutions
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".