Unplugging Standalone Sand-Control Screens Using High-Power Shock Waves
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".