Simulations of Field-Development Planning Help Improve Economics of Heavy-Oil Project
Bibliographic record
Abstract
Summary When Shell began an ultraheavy-oil development in Alberta, Canada, the field plan was based on manual pad positioning without consideration for surface hazards, trajectory constraints, or injector/producer slot requirements arising from the prefabricated-pad configuration. Shell teams were challenged to investigate alternative field-development plans to reduce the initial investment. The volume of wells, surface hazards, trajectory constraints, and pad slot configuration requirements for injector/producer ratios made this a formidable task. Shell leveraged the expertise of third-party consultants and powerful software to generate numerous development scenarios, which enabled the Shell staff to focus on the economics of each plan. An aerial image that delineated surface hazards, an elevation grid, and injector/producer targets were loaded into the 3D subsurface-visualization environment of the software to generate pad-placement simulations, including trajectory designs. The software followed the Shell planning constraints and avoided all surface hazards. The analyses were conducted for the highest, lowest, and median number of pads. From these three scenarios, a value analysis was plotted. This value analysis enabled the planning team to identify the optimal surface-to-reservoir configuration and to maximize the value of the field by delivering all of the reservoir targets, while minimizing the number of pads needed to drill. The output also yielded field cumulative values, including total and nominal well length, to support future economic analyses. The analysis determined that 87 pads maximized the value of the field when 42-slot configured pads were considered. Additional studies determined that when 50-slot configured pads were considered, more than 99% of the planned reservoir targets could be drilled from 72 major and four or fewer minor pads. In either case, a significant reduction from the original 98 pads was realized. Through the combined efforts of Shell engineers and third-party consultants, as well as the use of state-of-the-art software, this optimization was completed in only 8 worker-weeks. This paper describes the software and processes used to reduce Shell's estimated field-development costs and to minimize the environmental effect of the field by reducing the number of pads required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".