Application of Multi-Lateral Wells for Production and Enhanced Oil Recovery – Case Studies From Canada
Bibliographic record
Abstract
Abstract The use of multi-lateral wells started in the mid-1990s in particular in Canada, and they have since been used in many countries. However, few papers on multi-lateral wells focus on their production performances, thus what could be expected from such wells in terms of production and recovery factor is not clear and this paper will attempt to address that gap. Taking advantage of public data, the production performances of various multi-lateral wells in Western Canada have been studied. In the cases reviewed in this paper, these wells always target a single formation; they have been used in a variety of fields and reservoirs, mostly for primary production but also for polymer flooding in some cases. Multiple examples will be provided, mostly in heavy oil reservoirs, and production performances will be compared to nearby horizontal and vertical wells whenever possible. From the more classical dual and tri-lateral to more complex architectures with 7 or 8 laterals, and the more exotic, with laterals drilled from laterals, the paper will present the architecture and performances of these complex wells and of some fields that have been developed almost exclusively with multi-lateral wells. Interestingly, multi-lateral wells have not been used much for secondary or tertiary recovery, probably due to the difficulty of controlling water production after breakthrough. However, field results suggest that this may not be such a difficult proposition. One of the most remarkable wells producing a 1,250 cp oil under polymer flood has achieved a cumulative production of over 3MM bbl, which puts it among the top producers in Canada. Although multi-lateral wells have been in use for over 25 years, very few papers have been devoted to the description of their production performances. This paper will bring some clarity on these aspects. It is hoped that this paper will encourage operators to reconsider the use of multi-lateral wells in their fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".