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Record W2059625208 · doi:10.2118/169240-ms

Hydrocarbon Flow Assurance: Low Rate and Pressure Gas Field Experience

2014· article· en· W2059625208 on OpenAlexfundno aff
S. Olayemi, Manuel L. Cano, Sergio López Sánchez, C. O'Shea, Francis Letendre, P. Solc, Marie Verney, Márcia Marie Maru

Bibliographic record

VenueSPE Latin America and Caribbean Petroleum Engineering Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersShell Canada
KeywordsPetroleum engineeringVolumetric flow ratePressure dropWet gasHydraulicsInflowFlow (mathematics)Artificial liftGeologyEnvironmental scienceMechanicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract In the early life of most gas wells, there is sufficient reservoir pressure and flow rate to assure reliable evacuation of associated fluids from the reservoir/wellbore to the surface facility. In unconventional plays and in the case of Deep Basin gas field, this natural flow phase is usually short with a characteristic hyperbolic decline. When gas flow reaches critical velocity i.e. the minimum required velocity to lift out the liquids, pressure drop as a result of the hydrostatic head of liquid that is being left behind in the wellbore, increases till the well eventually stops flowing. Reduced rates and ultimate recovery due to liquid loading has significant impact on the economics of tight gas developments. The methods (timer cycling, foam, plunger, velocity string) presented in this paper are relevant to many low rate and low pressure gas wells. These methods have been used singly or in combination to optimally utilize the reservoir's energy for long term flow assurance. Cost, rate and estimated ultimate recovery of the deliquification decision have been key driving factors in the pursuit of effective hydrocarbon flow. Challenges and lessons learned thus far for deliquification decisions in Deep Basin are discussed, including the criteria/requirements for each method, inflow performance, wellbore hydraulics, water dynamics (formation and condensed water), scale deposition, associated secondary benefits/complications and field data showing impact.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.507
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.213
Teacher spread0.206 · 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.

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

Citations3
Published2014
Admission routes1
Has abstractyes

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