Bibliographic record
Abstract
Nearly 100 participants in an SPE Applied Technology Workshop (ATW) in Moscow discussed the application of horizontal wells (HWs) and multilateral wells (MWs) in Russia, where horizontal drilling is widely practiced. Case studies were presented from western Siberia, Sakhalin, Canada, the Middle East, and the North Sea to stimulate discussion of where it is best to use HWs and MWs and, alternatively, where other forms of production enhancement such as hydraulic fracturing might be more appropriate. The second part of the workshop focused on execution—drilling, completion, and life-of-field monitoring. Status of Well Technology Sibneft Chief Engineer Iskander Diyashev opened the discussion, describing how the company has enhanced production in its Noyabrsk western Siberia operation with 50% of the increase coming from HWs (even though these wells make up only 4% of the total well stock). TRACS-Consult Petroleum Engineering Consultant Robert Holtslag gave an overview of HWs in Russia where there are ≈2,500 of a worldwide total of ≈75,000. The Russian wells have productivity improvement (PI) factors in the range of 1.2–3. Holtslag pointed out that a major challenge is to increase the lower end of this range so that more HWs have robust economics. Schlumberger Middle East Chief Reservoir Engineer Fikri Kuchuk examined HW and MW performance, emphasizing the shortfall in performance as a result of inadequate cleanup, water sumps in the wellbore, and uneven pressure distribution in the near-wellbore formation that may reduce the PI factor to 30–50% of its potential value. He stressed the importance of drilling horizontal sections, which avoids sumps and highs where water and gas can accumulate. Sibneft Reservoir Engineer Elena Khairulina presented implementation of modeling HW performance using many examples from Noyabrsk. Case Histories and Screening Sibneft Reservoir Engineer Larisa Gaponova described the optimization of Sugmutsky field development by use of HWs. The peak oil production almost doubled with a much smaller number of wells. She compared a 500-m-long HW with a vertical well (VW) with a vertical hydraulic fracture. The initial PI factor was ≈4.4, stabilizing at a current value of 3, while the initial water cut was two times less than that from a typical VW and stabilized at 3.4 times less. Sibneft Stimulation and Completion Engineer Andrey Brovchuk explained hydraulic fracturing of openhole HWs in western Siberia, showing that a post-frac improvement of 150% was achieved and that 3.5% of production was achieved from 14 HWs with hydraulic fractures. Economides Consulting Partner Andronikus Demarchos described a transversely fractured HW in Algeria where four fractures were created with 70-m spacing along an HW. Each fracture used 100 to 200 tons of proppant. Results will become known when bridge plugs, needed in order to create multiple fractures, are drilled out.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".