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Record W2133881005 · doi:10.2118/87307-pa

Phase Behavior of a Gas-Condensate/Water System

2003· article· en· W2133881005 on OpenAlexaff
Sunil Kokal, Mohammad Al-Dokhi, S.G. Sayegh

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsBrineMethaneMass transferCarbon dioxideChemistryAqueous solutionHydrocarbonCarbonateFormation waterPhase (matter)ScalingPetroleum engineeringChemical engineeringThermodynamicsChromatographyGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Summary Gas-condensate reservoirs are an essential part of Saudi Arabia's hydrocarbon resources. A good understanding of the effect of water on the phase-behavior properties of these hydrocarbons is essential for accurately forecasting the performance of the reservoirs with numerical simulators. In addition, the scaling and corrosion tendencies of the produced brine are strongly influenced by mass transfer with the hydrocarbon phase. This paper presents unique experimental phase-behavior data for a typical Saudi Arabian gas-condensate three-phase (aqueous/ condensate/gas) system. The objective of this work is to quantify the effect of the aqueous phase on gas-condensate fluid properties. The results show that appreciable amounts of carbon dioxide and methane are partitioned from the gas-condensate phase into the aqueous phase. Another important observation is the mass transfer of water into the condensate phase. The mass transfer between the condensate and aqueous phases results in a slight decrease in the gas/condensate ratio (GCR). The carbon dioxide in solution makes the brine acidic and can dissolve carbonate minerals from the formation (e.g., calcium carbonate). In addition, the acidic or sour brine will be quite corrosive. The experimental results are compared with equation-of-state (EOS) and other correlations published in the literature.

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)0.002
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: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.044
GPT teacher head0.326
Teacher spread0.281 · 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

Citations18
Published2003
Admission routes1
Has abstractyes

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