Analysis of Well Production Data Using Functional Data Analysis
Bibliographic record
Abstract
Summary This study employs novel ensemble-based statistical techniques for type-well analysis to quickly analyze the production data from multiple wells in a reservoir. The method is based on functional principal component analysis (FPCA) that accounts for measurement noise and the sparsity of the production timeseries. In particular, the sparse FPCA method is used, which can extract the underlying random process from an ensemble of sparse production timeseries to predict the smooth trend of the well production data. The production from wells with short histories is also extrapolated using the stochastic information extracted from the wells with longer production. This approach is applied to analyze production data from 500 wells in an unconventional tight oil reservoir. In addition, the multivariate FPCA is utilized herein, for the first time, to jointly project the simulated surface condensate and gas rates from an unconventional gas condensate model by accounting for the correlations between the production phases. This approach is utilized to effectively replace the full numerical simulator by alternatively using an efficient timeseries proxy that can generate the full simulation output for any given set of input variables almost instantaneously.
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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 | 0.008 |
| 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".