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Record W4391329418 · doi:10.2118/217787-ms

An Interdisciplinary Economic Appraisal of Plug-And-Perf Versus Single-Point Entry Completions Systems Using Simulation

2024· article· en· W4391329418 on OpenAlexaff
B. Eidson, J. S. Macdonald, Ryan Carduner, Carina Theodore, S. Hervo

Bibliographic record

VenueSPE Hydraulic Fracturing Technology Conference and Exhibition · 2024
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsComputer sciencePoint (geometry)Economic appraisalPlug-inSpark plugMathematical optimizationOperations researchMathematicsMechanical engineeringEngineeringEconomicsOperating systemPublic economics

Abstract

fetched live from OpenAlex

Abstract Selecting which formation access and interstage isolation approach is economically superior is difficult to assess due to the complex interactions of these systems’ impact on drilling and completions cycle time and cost and well performance. An integrated view is necessary to assess the economics of single-point entry sleeve completions systems and plug-and-perf systems. The difficulty is exacerbated by the uncertainty of the percentage of effective plug-and-perf clusters. This paper documents the use of commercial simulation software to precisely estimate the daily cashflows for the life of a well pad (from rig move to decommissioning) using designs based on each system. The plug-and-perf cluster efficiency percentage is swept, and the percentage at which its economics breakeven with the single-point entry scenario is calculated. This is performed for multiple subsurface areas whose well performance vary differently from one another as effective cluster spacing changes. Using NPV/section, it was discovered plug-and-perf scenarios needed a cluster efficiency between 38-52% to breakeven with the corresponding single-point entry scenario. However, these results are highly dependent on an operator's contractual frameworks, well performance, and well production constraints.

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 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 score0.280
Threshold uncertainty score0.746

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 head0.035
GPT teacher head0.328
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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