Bibliographic record
Abstract
This paper discusses many aspects of the application of horizontal wells for field development, starting with the basic question of why to opt to this technology. The different considerations to be taken into account when dealing with the technology are reviewed, including some inherent downsides like higher cost and operational complications. The main emphasis, however, is placed on practical reservoir engineering aspects, including well planning and performance, and the handling of horizontal wells in specific technical applications like well test analysis and numerical modeling. The theoretical basis and pertinent differences in physics and flow regimes around vertical and horizontal wells are discussed in the context, together with the inherent practical implications. Case histories and examples are presented for several successful applications worldwide that the author was involved in. In one case, a medium size field offshore Canada was developed with waterflood utilizing very few horizontal producers, with a set of horizontal and vertical water injectors. Detailed planning and intensive modeling, carried out by a team of engineers and geoscientists, led to a remarkably successful field development. In another case, few horizontals were used among many directional wells to develop a Mediterranean oil reservoir under aquifer and gas cap drives in an attempt to reduce coning problems, raising an opportunity to compare the long term performance of different geometry wells. A brief description is also presented to some advanced techniques and special cases of implementing horizontal wells such as thermal recovery (Steam-Assisted-Gravity-Drainage, or SAGD Process), multi-lateral wells, and multi-fractured wells, with discussion on these applications. Under favorable conditions, horizontal wells can also be used as an Improved Oil Recovery (IOR) tool in mature fields, an application that became very common in many super giant fields in the Arabian Gulf Area.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".