IMPLEMENTING ARTIFICIAL NEURAL NETWORK FOR PREDICTING CAPILLARY PRESSURE IN RESERVOIR ROCKS
Bibliographic record
Abstract
Capillary pressure is one of the main parameters which is widely used to characterize and describe reservoir rock properties. Although some methods have been proposed to determine capillary pressure in reservoir rock, these methods may not be able to determine the capillary pressure accurately. In this study, an artificial neural network (ANN) algorithm was developed to estimate the capillary pressure in a hydrocarbon reservoir in the Middle East. A complete data set of several core samples includes porosity (Φ), normalized porosity (Φz), permeability (k), rock quality index (RQI),flow zone indicator (FZI); water saturation and drainage capillary pressure curves were applied to develop the ANN model. The ANN model which was designed in this study contains two separate parts. The first part categorized the reservoir rock into discrete groups with similar ranges of porosity and permeability, and the second part estimated the capillary pressure for each group. The results of this study revealed that ANN is an appropriate method to estimate the capillary pressure in reservoir rocks, particularly those which have heterogeneity in rock properties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".