Support-Vector Regression for Permeability Prediction in a Heterogeneous Reservoir: A Comparative Study
Bibliographic record
Abstract
Summary Permeability is a key parameter in reservoir-engineering computation, and the relationship between rock petrophysical properties and permeability is often complex and difficult to understand by using conventional statistical methods. Neural-network-based methods can be employed to develop more-accurate permeability correlations, but the correlations from these methods have limited generalizability and the global correlations are usually less accurate compared to local correlations. In this research, the objective is to build a permeability model with promising generalization performance. Recently, support-vector machines (SVMs) based on statistical-learning theory have been proposed as a new intelligence technique for both prediction and classification tasks. The formulation of SVMs embodies the structural-risk-minimization (SRM) principle, which has been shown to be superior to the traditional empirical-risk-minimization (ERM) principle employed by conventional neural networks. This new formulation deals with kernel functions, allows projection to higher planes, and solves more-complex nonlinear problems. SRM minimizes an upper bound on the expected risk, as opposed to ERM, which minimizes the error on the training data. It is this difference that equips SVMs with a greater ability to generalize, which is the goal in reservoir-characterization statistical learning. This novel support-vector-regression (SVR) algorithm was first introduced in well-logs intelligent analysis. Here, a permeability-prediction model using SVR from well logs in a heterogeneous sandstone reservoir is developed. Also, an attempt has been made to review the basic ideas underlying support-vector machines for function estimation. To demonstrate the potential of the proposed SVM's regression technique in prediction permeability, a study was performed to compare its performance with multilayer perceptron neural network, generalized neural network, and radial-basis-function neural networks. Accuracy and robustness were investigated, and statistical-error analysis reveals that the SVM approach is superior to the other methods for generalizing previously unseen permeability data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".