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Record W2508493040 · doi:10.2118/181552-ms

First Autonomous Inflow Control Valve AICV Well Completion Deployed in a Field Under an EOR Water & CO2 Injection Scheme

2016· article· en· W2508493040 on OpenAlexaff
Ransis Kais, Vidar Mathiesen, Haavard Aakre, Glenn Woiceshyn, A. F. Elarabi, Ricardo Hernandez

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2016
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsInflowCompletion (oil and gas wells)ChokePetroleum engineeringWell controlEnhanced oil recoveryWellheadOil fieldFlow (mathematics)Flow control (data)Injection wellOil wellEngineeringEnvironmental scienceMarine engineeringGeologyMechanical engineeringElectrical engineeringDrilling

Abstract

fetched live from OpenAlex

Abstract Enhanced oil recovery (EOR) schemes utilizing CO2 and water injection often experience significant problems and challenges with short circuiting of CO2 gas and water between injectors and producers, thereby leaving significant oil behind. Presented herein is the description and results of a field trial of new downhole flow control technology designed to provide autonomous inflow control of produced fluids from each zone in multi-zone wells. The new technology deployed involves integrating an autonomous inflow control valve (AICV) and a conventional (passive, non-autonomous) inflow control device (ICD) into a unique 3-position "sliding sleeve", shifted by coil tubing, to allow performance comparison between the two different inflow control devices as well as multiple flow control settings of each type. The AICV was designed (and lab tested) to selectively choke back or shut off flow of free CO2 gas and also high watercut (>99%), thereby significantly improving reservoir sweep and yielding higher oil production. The AICV opens or closes autonomously depending on sensed properties of wellbore fluids. Prior to installing the advanced completion, the multi-zone (vertical) trial well was extensively characterized using PLT and other log data, which was then inputted into a commercially available computer model to help design the AICV and ICD settings. The field trial was designed to evaluate the use of flow control in the EOR scheme over a wide range of flow rates and also to compare the two different flow control technologies at different settings within the same wellbore and reservoir condititions. This paper presents the results of the world's first field trial of the AICV in a Water and CO2 injection scheme, and the world's first comparison with conventional ICD technology in the same well. In addition to lab tests, the early field trial results of the advanced flow control completion are compared with historical production and PLT data where the zones were comingled (without any downhole flow control). A performance comparison of AICV versus conventional ICD, along with conclusions, implications for other wells in the same field and other fields, and lessons learned, are all presented herein.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations16
Published2016
Admission routes1
Has abstractyes

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