Bibliographic record
Abstract
Technology Focus According to the US Energy Information Administration, West Texas Intermediate (WTI) annual average oil prices have fluctuated between $93.2 and $43.3/bbl between 2014 and 2019. The price was $76.4/bbl (WTI spot price) at the beginning of October 2018, dropping approximately 41% to $45.4/bbl on 1 January 2019, reached a peak of $66.3/bbl on 23• April 2019, and then depressed to today’s (15 August 2019) $55.1/bbl. The lower and unstable oil prices since 2015 have continuously pushed operators and service companies to reduce costs and improve capital efficiency in all aspects of business, including sand management. This is reflective of the industry’s attention on fundamental and proven sand-control practices. This year, many published works have focused on standalone screens as the most cost-effective solution when compared with other more-expensive approaches such as gravel packing or frac packing. Screens are reliable for sand control when properly designed and used in suitable sand-prone environments. The latest technologies in this area have featured developments focusing on mitigating plugging and erosional damage. While sand control in production wells catches much attention, water-injection wells also are subject to sand-control problems triggered by water hammer, crossflow, and backflow. Newly developed screens for water-injection wells have been demonstrated with promising results in the latest field trials in preventing formation sand from flowing back and, therefore, maximizing injectivity. In this issue, I have selected a variety of papers with more emphasis on cost-effective sand-management practices and operations. Please read the following three paper synopses with the recommended additional readings for more information, and do not forget to attend the upcoming SPE Annual Technical Conference and Exhibition scheduled for 30 September through 2 October in Calgary. Recommended additional reading at OnePetro: www.onepetro.org. SPE 192090 Sand Prediction for a Cost-Effective Marginal Greenfield Development by Siti Aishah Mohd Hatta, Petronas, et al. SPE 193697 Risk Assessment in Sand-Control Selection: Introducing a Traffic Light System in Standalone-Screen Selection by Mahdi Mahmoudi, RGL Reservoir Management, et al. SPE 193698 Successful Installation of Standalone Screen in Challenging Environment in Umm Niqa Field by Amr Zeidan, Kuwait Oil Company, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.082 |
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".