Innovative Data-Driven Permeability Prediction in a Heterogeneous Reservoir
Bibliographic record
Abstract
Abstract In complex reservoirs where heterogeneity of properties and errors associated with sampling yield uncertain measurements of geological properties, e.g. porosity and permeability, conventional techniques such as empirical and nonlinear regression methods attempt to estimate values of these properties with low or no error. Conversely, artificial intelligence asserts that the error contains useful information that complements conventional techniques. For example, fuzzy logic methods to predict permeability at uncored wells provide uncertainty measures based on secondary variables such gamma ray, neutron porosity, sonic porosity, bulk density, formation resistivity, and core-based permeability. Fuzzy logic provides tools for uncertainty modeling and improved permeability estimation. Here, a two-stage fuzzy ranking algorithm is integrated in the fuzzy predictive model to improve generalization capability and transparency of the model through selecting inputs best suited to predict permeability. Fuzzy curve and surface analysis is used to rapidly and automatically identify information-rich well logs and filter out data dependencies. Subtractive clustering algorithm generates membership functions and isolates clusters of data. The Takagi-Sugeno-Kang fuzzy rule-based system (TSK) is constructed from a subset of logs and core measurements. The antecedent fuzzy sets are obtained by projecting the mean and variance onto input data axes. The consequent functions consist of a set of linear equations. To tune antecedent and consequent membership function parameters, an Adaptive Network based fuzzy inference systems (ANFIS) is used. The proposed methodology is initially validates using the standard Box & Jenkins’ gas furnace data to predict C02 concentration and compared to a multilinear regression technique. Generalization capability has increased through using the most significant inputs during modeling gas furnace data. The methodology was also demonstrated by predicting permeability from well log data of a heterogeneous sandstone reservoir located in the Middle East. The fuzzy model was compared to conventional linear and multilinear regression models to show its applicability and superiority in heterogeneous systems. Unlike conventional methods, the proposed fuzzy technique does not require any prior knowledge of the reservoir and relationships between permeability and input variables. In addition, the technique accounts for uncertainty that exists in log data. The result is an efficient interpretation expert system that can be continuously conditioned as new data becomes available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".