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Record W2076741437 · doi:10.2118/124203-pa

Simulations of Field-Development Planning Help Improve Economics of Heavy-Oil Project

2010· article· en· W2076741437 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSPE Economics & Management · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsTrajectoryInjectorSoftwareField (mathematics)Shell (structure)Net present valueComputer sciencePlan (archaeology)Operations researchEngineeringSimulationMechanical engineeringGeologyProduction (economics)MathematicsOperating systemPhysics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.263
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it