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Record W3113037968 · doi:10.2118/199294-pa

Unplugging Standalone Sand-Control Screens Using High-Power Shock Waves

2020· article· en· W3113037968 on OpenAlexaffabout
Ali Habibi, Charles E. Fensky, Mike Perri, Morteza Roostaei, Mahdi Mahmoudi, Vahidoddin Fattahpour, Hongbo Zeng, Mohtada Sadrzadeh

Bibliographic record

VenueSPE Drilling & Completion · 2020
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnergy-dispersive X-ray spectroscopyMaterials scienceScanning electron microscopeInductively coupled plasmaPetroleum engineeringEnvironmental scienceMineralogyComposite materialGeologyPlasma

Abstract

fetched live from OpenAlex

Summary Previous studies showed that different parameters influence the plugging of completion tools. These parameters include rock mineralogy, reservoir-fluid properties, and type of completion tools. Although different methods have been used for unplugging these tools, there is still debate regarding the performance of these methods on damage removal. In this study, we assessed the performance of high-power shock waves generated from an electrohydraulic-stimulation (EHS) tool on cleaning completion tools plugged during oil production. These devices were extracted from different wells in Canada, Europe, and the US. First, we quantified the extent of cleaning for the plugged slotted liners using the EHS tool at the laboratory scale. Next, we analyzed the mineral composition of the plugging materials removed after the treatment by conducting scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS), inductively coupled plasma mass spectroscopy (ICP-MS), colorimetric, and dry-combustion analyses. Finally, we reviewed the pulsing-stimulation-treatment results applied to several field case studies. The results of unplugging slotted liners at the laboratory scale showed that up to 28.5% of the plugged slots are cleaned after 120 pulses of shock waves. The mineral-characterization results showed that the main plugging materials are calcite, silicates, and iron-based components (corrosion products). The cleaning performance (CP) of the EHS tool increases by increasing the number of pulses and the output energy (OE) applied to the tool. The CP parameter is high at (i) high concentrations of carbonates, barium (Ba)-based components, and organic matter, and (ii) low concentrations of corrosion products and sulfates. The results of field case studies showed that the cleaning of the EHS tool is not limited to the sand-control devices and it can clean other tools that are less accessible for other techniques, such as subsurface safety valves. This paper provides a better understanding of the performance of shock waves on damage removal from plugged completion tools. The results could open new insight into the applications of shock waves for cleaning the completion tools.

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: none
Teacher disagreement score0.710
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 communication0.0000.000
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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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