Enabling High Performance Multi-Lateral Wells: Current Technologies and Gaps
Bibliographic record
Abstract
Abstract This paper presents the current state of the industry in multilateral wells (ML) and presents the key findings from a broad literature review and two knowledge sharing workshops hosted by the University of Calgary and the Clean Resource Innovation Network (CRIN) in late 2021 and early 2022. The technology to deliver ML wells is mature and has been demonstrated across thousands of wellbores around the world. Evaluation, characterization, and control of flows in ML wells is also possible using available technology, however, it is expensive and may be cost prohibitive. Modelling and optimization of flows from lateral legs may be done using existing reservoir simulators. However, some regulatory hurdles may remain, including accounting for production from different zones and licensing of legs. What generally limits the more widespread adoption of ML wells is the perception of elevated risk from decision makers, lack of familiarity with ML technologies, and the perceived costs of ML junctions and associated equipment and operations. By highlighting the technologies and successes, this paper aims to heighten awareness of ML technologies and further widen the adoption and implementation of ML wells.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".