Optimizing Maximum Reservoir Contact Wells: Application to Saudi Arabian Reservoirs
Bibliographic record
Abstract
Abstract The objective of multilateral-well technology is to improve well productivity by maximizing reservoir contact, resulting in field development with fewer wells. Long horizontal wells (up to eight km) are drilled, but the greatest opportunity, as well as the greatest technological challenge, lies with MRC wells. A Maximum Reservoir Contact (MRC) well, by definition, is a multilateral horizontal well with more than five km of total contact with the reservoir rock. Planning of these wells requires extensive modeling studies to optimize total length, placement and configuration of branches, and the use of "smart-well" options: selective layer/branch shutoff devices, balanced production to limit flow of fluid through a particular branch, crossflow control, and downhole separators to handle high watercuts. This paper describes how these objectives were met and the well design was optimized. In this work, a number of sector models were used to evaluate different MRC completions based on their total production, well placement and design, stage of depletion, pressure interference between laterals, and impact of water encroachment by downhole control. Applying this reservoir simulator to the development of the northern area of the Greater Ghawar field, we were able to accomplish the following:Optimize well placement.Optimize numbers and lengths of laterals.Evaluate benefit of "smart" well completions. Detailed modeling of the proposed MRC wells provided a better understanding of reservoir dynamics that will enable Reservoir Management to make faster and informed decisions. Besides this, the study helped us significantly reduce turnaround time for MRC well evaluation. Introduction The successful implementation of horizontal drilling over the last two decades has led to the development of multilateral-well technology. The number of multilateral-well completions has increased substantially in the last several years due to advances in directional drilling and completion systems. Many field applications have been reported in the literature including compartmentalized reservoirs in UK and Malaysia, stacked dual, triple, and fishbone laterals in Venezuela, and diverse applications in onshore and offshore USA and North Sea, Thailand and Brunei, Canada, Brazil, Italy, Nigeria, and the Middle East.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".