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Record W1988219565 · doi:10.2118/112863-ms

First Production Log Run in a Heavy-Oil Long-Horizontal Well Through a Y-Tool and Premium Screens

2008· article· en· W1988219565 on OpenAlexaff
Jacobo Montero, Namir Salazar, Tony Fondyga, Kerwin Reyes

Bibliographic record

VenueSPE/ICoTA Coiled Tubing and Well Intervention Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsWorkoverPetroleum engineeringProductivityCoiled tubingLoggingWell loggingProduction (economics)Environmental scienceReservoir engineeringOil productionOil fieldGeologyPetroleum

Abstract

fetched live from OpenAlex

Abstract The first production log ever run in a heavy oil, long horizontal well completed with premium screens in open hole and through a Y-tool was successfully executed by Petroregional del Lago S. A. (a joint venture between PDVSA and Shell) in the Urdaneta West Field (Lake Maracaibo, Western Venezuela). The purpose of the job was to identify the origin of water in a well that experienced water break-through from the first day of production. A specialized set of logging tools was run to detect both water and oil flow, as well as to determine any flow behaviors like cross-flow that would help understand the source of water in the formation, obtain sufficient data to prepare a water shut-off program, and establish basic productivity information from the well, being this also the first production log run in the heavy oil wells in the field, which require artificial lift to flow. The results of the production log indicate that a sand package is producing water from an unexpected zone, which will require a water shut-off workover. This paper describes the planning, operational and interpretation processes of the logging activity, and presents a number of lessons learnt and useful recommendations for similar activities in heavy oil wells.

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: Empirical
Teacher disagreement score0.341
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 communication0.0000.001
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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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