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Record W1998399676 · doi:10.2118/0510-0052-jpt

Analysis of Inflow-Control Devices

2010· article· en· W1998399676 on OpenAlexaboutno aff
Dennis Denney

Bibliographic record

VenueJournal of Petroleum Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPressure dropInflowPetroleum engineeringDrop (telecommunication)Volumetric flow rateOil wellSubmarine pipelineEnvironmental sciencePressure controlNozzleCompletion (oil and gas wells)GeologyMechanicsEngineeringGeotechnical engineeringMechanical engineeringOceanographyPhysics

Abstract

fetched live from OpenAlex

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 122824, ’Analysis of Inflow-Control Devices,’ by Bernt S. Aadnoy, SPE, University of Stavanger, and Geir Hareland, SPE, University of Calgary, prepared for the 2009 SPE Offshore Europe Oil & Gas Conference & Exhibition, Aberdeen, 8-11 September. Inflow-control devices (ICDs) were developed to avoid water-coning problems in long horizontal wells. The model for the ICD consists of pressure-drop equations from the reservoir, through the screen, the flow conduit, the ICD nozzle, and into the production tubing, along with pressure drop through the lower-completion system. Evaluating the model shows that turbulent flow through the ICD dominates the pressure drop, indicating density-controlled flow. The flow rate will vary with depletion. A new ICD concept can maintain constant flow regardless of the degree of field depletion. Introduction Long horizontal production tubing results in a considerable pressure drop over its length. Oil produced at the far end (the toe) must overcome this pressure drop. Oil produced at the near end (the heel) is not affected by this pressure drop. Therefore, assuming homogeneous reservoir characteristics, the flow rate is higher at the heel than at the toe. Because the area close to the heel will produce more liquid hydrocarbons, the result is coning of the water/oil contact (WOC) or the gas/oil contact (GOC), as shown in Fig. 1. When the WOC or GOC reaches the wellbore, the process will accelerate. Over time, as water production increases, oil production decreases and produced-water disposal can become a problem. Most of the oil near the toe will not be produced, and new wells will be required to drain it. The recovery aspect is of utmost importance for these types of wells. To control and equalize the horizontal-well drawdown, ICDs are installed along the production tubing. By applying flow restrictions, coning can be reduced or controlled. Ideally, the entire WOC or GOC should be parallel to the production tubing as shown in Fig. 2. If the waterfront enters the tubing over the entire length at the depleted stage, ultimate recovery has taken place. Hydraulic Model In a typical ICD tool, screens are wound over base pipes. Axial rods provide standoff of the screen from the base-pipe surface and provide a conduit for reservoir fluid to flow toward the ICD. The ICD is installed at one end of the base pipe. Reservoir oil flows through the screens into a pathway (conduit) along the base pipe. Going through the pathway shown in Fig. 3, the oil enters a chamber before going through orifices. After passing through the orifices, the oil flows through several large holes inside the casing. The orifices control the flow rate. The coupled flow model includes the pressure drop from the reservoir through the ICD and into the base pipe. The flow and pressure analysis detailed in the full-length paper is based on a typical ICD application in the North Sea. The flow path is coupled by a series of pressure losses and broken into the following components. Outside screen Conduit below the screen Chamber Orifices Holes through the casing

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.247
Teacher spread0.242 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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