Unplugging Standalone Sand Control Screens with High-power Shock Waves: An Experimental Study
Bibliographic record
Abstract
Abstract Previous studies showed that different parameters influence the plugging of completion tools. These parameters include (i) rock mineralogy, (ii) reservoir fluids properties, and (iii) type of completion tools. Although different methods have been used for unplugging these tools, there is still debate regarding performance of these methods on damage removal. In this study, we assessed the performance of high-power shockwaves generated from an electro-hydraulic 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 evaluated the extent of cleaning for the plugged completion tools using an EHS tool at the lab-scale. We examined the slots/screens before and after the treatment to show the performance of the EHS tool. Next, we analyzed the mineral composition and morphology of the plugging materials removed after the treatment by conducting X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-Ray Spectroscopy (EDS) analyses. Finally, we reviewed the pulsing stimulation treatment results applied to several field case studies. The results of unplugging sand control devices at the lab-scale showed that more than 50% of plugged slots/screens were cleaned after 45 pulses of shockwaves. The characterization results showed that the main plugging materials are calcite, silicate, and iron-based components (corrosion products). The results of field case studies showed an improved oil production rate after the pulsing stimulation treatment. This paper provides a better understanding of the performance of shockwaves on damage removal from plugged completion tools. The results could provide a complementary tool for production engineers to select a proper method for treating the plugged 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 | 0.001 |
| 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".