Bibliographic record
Abstract
Technology Focus More than half of all existing wells are estimated to require sand control or sand management throughout their lifetime, including unconsolidated sandstone in conventional reservoirs or flowback in unconventional reservoirs. The majority of recent major hydrocarbon discoveries, from Africa (Mozambique, Angola, and Tanzania), transcontinental countries (Egypt), North America (US and Canada), to Far East Asia (Malaysia), are offshore with high-permeability soft formation sands. Approximately half of them are gas-bearing reservoirs. High-flow-rate gas wells are particularly susceptible to sand production. High-velocity or turbulent fluid flow generates large drag forces, dislodging unconsolidated sand particles. The free-flowing particles can erode downhole and surface equipment, including well-control barriers. In a worst-case scenario, this can lead to dangerous uncontrolled production. To ensure successful sand management, a multidisciplinary engagement is necessary. The teams should be able to predict sanding tendencies, detect the sanding locations, select appropriate downhole sand-management and -control devices, and implement the best operating practices for the life of the well. Because of the current downturn, operators are shifting their efforts to the revitalization of existing wells in order to squeeze more production from depleted reservoirs. The same holistic sand-management tactic should be applied to remedial sand control. In summary, production from sand-prone reservoirs is a daunting task, with formidable challenges. Sand management and control remain as an old problem but with new challenges because of the suppressed oil and gas prices. Cost-saving and value-adding solutions are vital now more than ever. For more information, read the featured papers, recommended additional reading, and other publications at OnePetro. Recommended additional reading at OnePetro: www.onepetro.org. SPE 181596 Defining Sand Control in an Uncharted Frontier: A Case Study on the Zawtika Field Development in Myanmar by Graham Grant, PTTEP International, et al. SPE 181360 Case History: Integrated Approach to Sand Management and Completion Evaluation for Sand Producer in a Mature Field, North Sea by M. Ruslan, Dong Oil and Gas, et al. SPE 182511 New Criteria for Slotted-Liner Design for Heavy-Oil Thermal Production by Mahdi Mahmoudi, 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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".