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Two-Phase Liquid Production Allocation in Multilayer Producing Wells Using Temperature Measurements

2012· article· en· W1962194285 on OpenAlexvenueno aff
Ahmed Daoud

Bibliographic record

VenueAdvances in petroleum exploration and development · 2012
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringThermal conductionGeothermal gradientThermalInversion (geology)Temperature gradientMechanicsOil fieldVolumetric flow rateHeat transferFormation waterGeologyMaterials scienceThermodynamicsMeteorologyGeophysicsPhysics

Abstract

fetched live from OpenAlex

This work presents a methodology of allocating oil rate and associated water cut to each individual layer using temperature measurements and total surface production of oil and water. This paper consists of two parts. In part one; an analytical forward model is proposed for wellbore temperature response under two-phase production in a multilayer geometry, using a nodal representation of the well. This model accounts for the formation geothermal gradient, steady-state oil-water flow in the wellbore, friction loss and Joule-Thomson effect in the wellbore, contrast in the thermal and physical properties of oil and water, wellbore heat losses due to unsteady heat conduction in the earth, and the mixing of the fluid streams of contrasting temperature. The second part shows the application of the above solution by applying inversion techniques on temperature data coupled with forward model to allocate water and oil influx from producing layers. The inversion result is verified using a variety of commingled flow problems including a field case of a deviated well producing an oil-water mixture from two active completions. Inversion results seem to be robust within +/-15% provided the temperature contrast between the commingled layers is at least one order of magnitude greater than the resolution of the temperature measurements (e.g., 4°C contrast for 0.1°C resolution) Key words : Oil rate; Temperature measurement; Nodal representation; Inversion techniques

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)0.000
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 score0.388
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.055
GPT teacher head0.333
Teacher spread0.277 · 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
Published2012
Admission routes1
Has abstractyes

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