Modeling Permeability in Tight Gas Sands Using Intelligent and Innovative Data Mining Techniques
Bibliographic record
Abstract
Abstract Evaluation of gas potential in low permeability reservoirs (< 0.1 md) generally referred to as Tight Gas reservoirs is not very straight forward as in conventional reservoirs. This study is focused on modeling permeability in the Travis Peak Formation where there are many challenges. One may encounter low resistivity pay due to clay coated grains and alternating thin laminae of finer and coarser sandstones with distinctly different pore geometries, natural fractures, bituminous zones and multilateral fluvial channel sandstones in broad lenses. Core data is sparsely available. Most importantly, there are no structural features that may construe trapping mechanisms. In view of these challenges, a permeability model was developed primarily for the Travis Peak Formation of Robertson and Leon counties where it has produced 96 BCF of gas and 0.54 MMbbl of oil. A permeability model was developed by integrating core and log data using the Adaptive Neuro Fuzzy Logic Inference System (ANFIS) that combines the functionality of neural network and fuzzy logic techniques. A combination of conventional logs such as lithology (GR, SP), porosity (RHOB, NPHI), deep and shallow resistivity logs were integrated with core data (porosity, permeability). The results showed excellent agreement with measured core permeability values. The results of the conventional porosity-permeability transform are also presented for comparison purposes. Since the data has been used from the Travis Peak Formation of East Texas Basin comprising several counties, it is expected that the permeability model should be applicable anywhere in the basin.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".