North Slope Heavy-Oil Sand-Control Strategy: Detailed Case Study of Sand Production Predictions and Field Measurements for Alaskan Heavy-Oil Multilateral Field Developments
Bibliographic record
Abstract
Abstract The North Slope of Alaska has billions of barrels of heavy oil residing in largely undeveloped reservoirs. Despite this large volume of heavy oil in place, the majority of reserves development on the slope to date has been focused on light crude. However over the past 20 years Arco, BP, Conoco and now ConocoPhillips have begun to develop the North Slope’s vast heavy oil resource base. Recently a sand/solids control study was undertaken by ConocoPhillips and BP in order to determine the most economic strategy for solids control and well design in future heavy oil developments. The study was integrated across companies, organizations and discipline boundaries in order to include completion, rock mechanics, laboratory research, drilling, reservoir, geological, operations, facilities and field personnel. With this diverse team, actual solids production and solids predictions were investigated from a number of different perspectives. Solids production predictions were made based on core measurements, log analysis, simulators that predict formation failure and sand production rate, laboratory core flow tests, 2 years of field shakeout data, and multiple field measurements of solids production. Probabilistic predictions were then generated based on these investigations rather than deterministic "best guesses" for the economic analysis. These different methods for predicting solids production will be discussed and illustrated in this case study. The study and ensuing strategy determined that sand management or using non-sand exclusion slotted liners and sand tolerant facilities was the highest value development scenario over the life cycle of the North Slope Heavy Oil Developments.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".