XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier
Bibliographic record
Abstract
holds a pivotal role in regulating fluid flow during hydraulic fracturing within shale oil extraction processes. However, concerns persist surrounding its reliability due to repeated attempts at opening the sleeve, resulting in process inefficiencies. While downhole cameras can verify sleeve states, their high cost poses a limitation. This study proposes an alternative approach, leveraging downhole data analysis for sleeve incident detection in lieu of cameras. This study introduces "XGSleeve," a novel machine-learning methodology. XGSleeve amalgamates hidden Markov model-based clustering with the XGBoost model, offering robust identification of sleeve incidents. This method serves as an operator-centric tool, addressing the domains of oil and gas, well completion, sliding sleeves, time series classification, signal processing, XGBoost, and hidden Markov models. The XGSleeve model exhibits a commendable 86% precision in detecting sleeve incidents. This outcome significantly curtails the need for multiple sleeve open-close attempts, thereby enhancing operational efficiency and safety. The successful implementation of the XGSleeve model rectifies existing limitations in sleeve incident detection, consequently fostering optimization, safety, and resilience within the oil and gas sector. This innovation further underscores the potential for data-driven decision-making in the industry. The XGSleeve model represents a groundbreaking advancement in sleeve incident detection, demonstrating the potential for broader integration of AI and machine learning in oil and gas operations. As technology advances, such methodologies are poised to optimize processes, minimize environmental impact, and promote sustainable practices. Ultimately, the adoption of XGSleeve contributes to the enduring growth and responsible management of global oil and gas resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".