MétaCan
Menu
Back to cohort
Record W2045944590 · doi:10.2118/149089-ms

Multizone Well Testing with Downhole Tools in Extreme Sour-Gas Conditions

2011· article· en· W2045944590 on OpenAlexaff
Florian Hollaender, Alan Salsman, Fardin Ali Neyaei, Richard Singleton, Fuad Al Badi, Efstathios Rigatos, Ashraf Ali, Mohamed Anwar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsCompletion (oil and gas wells)Petroleum engineeringHydrostatic testWell test (oil and gas)Oil wellFlexibility (engineering)Computer scienceGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A recently drilled appraisal well in a multilayered carbonate reservoir was successfully tested using downhole tools in very hostile downhole conditions. While the reservoir pressure and temperature were not excessive, the combined concentrations of hydrogen sulfide (H2S) and carbon dioxide (CO2) were well above anything that has been tested in the past with similar tools. Four different zones were tested individually using a fit-for-purpose test design, which allowed for testing two zones per trip. The services included downhole tools for shut-in and well-control purposes, bottomhole samplers, memory gauges, through-tubing perforating and coiled-tubing intervention through the test string to acid stimulate, N2 lift, and set cement plugs between zones. The use of the downhole test string provided the means to carry out a complete well test with downhole shut-in and sampling controlled from the surface. The operational flexibility offered by the string allowed for all operations to be successfully performed even with lengthy exposures of the tools to sour fluids. This appraisal well is one of a series of wells drilled in this area but the first one in which very extensive data acquisition has been acquired thanks to a detailed well-test procedure and job planning. This test required a significant amount of prejob planning, in addition to attention to detail in the execution and onsite preparation and maintenance. Our paper describes the test requirements, the prejob planning, and the execution that led to the successful completion of this well test in very hostile conditions, the first of its kind in this part of the world.

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 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 score0.296
Threshold uncertainty score0.511

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 head0.094
GPT teacher head0.196
Teacher spread0.102 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicDrilling and Well EngineeringFrench-language works237,207