The Prediction of Three Key Properties on Coalbed Methane Reservoir Using Artificial Intelligence
Bibliographic record
Abstract
This research focuses on creating the prediction tools for the three key properties in coalbed methane (CBM) reservoir; the properties are gas content, Langmuir parameters, and permeability. Basically, their roles are to describe the gas in place and also future dynamic behavior of CBM reservoir. These three key properties are tried to be predicted with open-hole data as the inputs.It uses artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) to generate the prediction tools. It is started from data preparation and processing, then pattern or function identifications, and finalized by validation and testing. Several training algorithms are applied for ANN such as adaptive gradient descent (ANN_GDX), Levenberg-Marquardt (ANN_LM), resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), and Bayesian regularization algorithm (ANN_BR). The first fives employ the early stopping technique for regularization, and the last one does Bayesian regularization. On the other hand, the ANFIS will use only early stopping technique.Based on this research, it is concluded that both ANN and ANFIS are able to identify the patterns or function between open-hole log data and the three key properties (TKP). Furthermore, it can be concluded that ANN_LM, ANFIS, and ANN_BR are the best selected algorithms which resulted the lowest error of TKP’s predictions.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".