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Record W2073608578 · doi:10.2118/169515-ms

A New Semi-Analytical Method for Analyzing Production Data from Constant Flowing Pressure Wells in Gas Condensate Reservoirs during Boundary-Dominated Flow

2014· article· en· W2073608578 on OpenAlexaff
M. Heidari Sureshjani, Hamid Behmanesh, Christopher R. Clarkson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringMaterial balanceWell test (oil and gas)Boundary value problemMechanicsFlow (mathematics)Constant (computer programming)Reservoir engineeringPermeability (electromagnetism)Real gasRelative permeabilityNatural gas fieldVolumetric flow rateReservoir simulationEnvironmental scienceGeologyNatural gasGeotechnical engineeringChemistryComputer scienceMathematicsEngineeringPhysicsProcess engineeringPetroleumMathematical analysisPorosity

Abstract

fetched live from OpenAlex

Abstract With the current focus on liquid-rich plays in North America, and the importance of gas condensate reservoirs globally, there is an increased importance placed on reservoir engineering methods to analyze such reservoirs. This paper provides a new semi-analytic boundary-dominated flow equation (BDFE) and also adapts the existing BDFE of dry gas reservoirs for production analysis of constant flowing pressure wells producing from gas condensate reservoirs. To analyze long-term production data, these equations are coupled with a modified material balance equation to give gas-in-place and average reservoir pressure obtained from plotting techniques which use iterative procedures. The required input data for analysis are gas flow rate, bottomhole pressure, CVD test data, and a portion of the immiscible gas relative permeability curve. We also introduce a new material balance time function for gas condensate reservoirs. To verify our analysis methods, compositional numerical simulation is used. We also test the practicality of our approach through analysis of field data.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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