Rapid and Comprehensive Artificial Lift Systems Performance Analysis Through Data Analytics, Diagnostics and Solution Evaluation
Bibliographic record
Abstract
Abstract Artificial lift systems (ALS) should be thoroughly analyzed to minimize non-productive time, increase production and reserves, reduce cost and failure, and maximize capital efficiency. This work proposes a novel systematic for ALS analysis approach that implements data analytics for historical performance tracking and benchmarking, heuristic diagnostics for suboptimal identification, and reliable model-based solutions for opportunity evaluation. The proposed methodology follows the principles of integrated reservoir management and greatly reduces the time to manage ALS systems by combining expert knowledge, data analysis with model-based calculation in pursuit of extreme efficiency and high accuracy. Our efficient workflow builds from intelligent data pre-processing capabilities, which facilitate fast and structured analysis of complex datasets. The metrics and scoreboard are intuitive, comprehensive and unique, and are scarcely documented in technical literature. These evaluations from various aspects generate different paths for asset management with diversified views. By following a very systematic approach and incorporating industry standards and propriety analysis and metrics, this workflow leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster. Our solution makes the analysis unbiased, and integration of new datasets super easy. This could greatly benefit executive decision-making and operational practices in a field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".