Seismic attribute-based characterization of coalbed methane reservoirs: An example from the Fruitland Formation, San Juan basin, New Mexico
Bibliographic record
Abstract
Abstract The Fruitland Formation of the San Juan basin is the largest producer of coalbed methane in the world. Production patterns vary from one well to another throughout the basin, reflecting factors such as coal thickness and fracture and cleat density. In this study, we integrated conventional P-wave three-dimensional (3-D) seismic and well data to investigate geological controls on production from a thick, continuous coal seam in the lower part of the Fruitland Formation. Our objective was to show the potential of using 3-D seismic data to predict coal thickness, as well as the distribution and orientation of subtle structures that may be associated with enhanced permeability zones. To do this, we first derived a seismic attribute-based model that predicts coal thickness. We then used curvature attributes derived from seismic horizons to detect subtle structural features that might be associated with zones of enhanced permeability. Production data show that the best producing wells are associated with seismically definable structural features and thick coal. Although other factors (e.g., completion practices and coal type) affect coalbed methane production, our results suggest that conventional 3-D seismic data, integrated with wire-line logs and production data, are useful for characterizing coalbed methane reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".