Bayesian optimization of capillary pressure data in hydraulic flow units using NMR
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) data provides a comprehensive picture of the petrophysical description of a reservoir through effective characterization of fluid-rock properties. However, estimating the correct capillary pressure curves from NMR T 2 data in particular has been challenging with varied fluid saturation as it requires hydrocarbon correction. The earlier methods, either do not incorporate the hydrocarbon correction or exhibit limitations in their implementation, negatively impacting reservoir characterization. Therefore, in this work, a new methodology has been presented that estimates the P c in the reservoir at hydraulic flow units (HFUs) by using features of NMR T 2 and cumulative desaturation rate ∑(dS nw /dT 2 ) through a newly developed workflow. Which incorporates the NMR hydrocarbon correction and encompasses the ensemble-committee machine model (ECMM) that has been purpose-formulated with the Bayesian optimized best-performing algorithms of machine, ensemble, and deep learning through a systematic approach. Results show that the ECMM workflow gives a much better mean squared error (MSE) than individual intelligent models while predicting P c . ECMM has also been utilized to analyze the capillary pressure variability at HFUs which reveals that higher variance in capillary pressure values among HFUs cause model to underperform in terms of MSE and vice versa. The new methodology introduces a robust and cost-effective machine-learning incorporated workflow to estimate continuous capillary pressure for reservoirs having varied lithologies for effective characterization.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".