Bibliographic record
Abstract
Technology Focus Shallow-water offshore production began before 1900 and continues to be important today. Technology to maximize economic production from shallow-water fields can be adapted from onshore or deepwater technologies. Improvements in monitoring-system capabilities (and costs) are direct contributors to optimizing well and facilities operations. Several case studies illustrate the benefits of applying existing technology to increase production in mature operations. The first case study is of a gas/condensate field. Typical liquid-handling strategies were first applied to mitigate production decline, including converting the test separator to a low-pressure separator. Regular well testing is required for allocation and reservoir management. Previously, low-pressure wells were shut in during the well tests, resulting in production losses. Clamp-on sonar meters allow well testing every 2 months without shutting in production. Data from the sonar metering and other production-surveillance techniques have been used to optimize well cycling. The second case study describes implementation of through-tubing technology for sand control at a normally unmanned platform. The platform is located in shallow water with onerous sea states. Significant sand production began from an unconsolidated oil zone when the original gas well was converted to commingled production. This paper describes the selection and installation of through-tubing sand control and subsequent selection and installation of through-tubing gas lift. Critical success factors for well rejuvenation at this marginal field include managing marine issues and crane limits. The last case study discusses installation of a downhole electric heater in an offshore heavy-oil well. Heating heavy-oil reservoirs is uncommon offshore. This successful heater application uses a three-phase system with a cold section to protect the electrical submersible pump (ESP) used to lift the oil. Distributed-temperature sensing monitors temperature profiles in the well. Cable connections—power for the heater and the ESP—are critical for successful operation. JPT Recommended additional reading at OnePetro: www.onepetro.org. IPTC 16858 Downhole Electrical Heating for Heavy-Oil Enhanced Recovery: A Successful Application in Offshore Congo by F. Bottazzi, Eni, et al. OTC 23948 Full-Scale Testing of Distributed-Temperature Sensing in Flexible Risers and Flowlines by Nick Weppenaar, NOV Flexibles, et al. OTC 23968 Large-Diameter-Riser Laboratory Gas Lift Tests by G. Zabaras, Shell, 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 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".