Secondary Porosity and Permeability of Coal Vs. Gas Composition and Pressure
Bibliographic record
Abstract
Abstract We have been investigating sequestration of atmospheric pollutants by injection into coal seams while at the same time enhancing hydrocarbon productivity by displacement of methane by the pollutants. During the course of our field measurements, we have been using single well injection, soak, and production tests to collect data required to understand and predict enhanced coalbed methane (ECBM) recovery potential and sequestration capacity. We found that changing the composition of the gas sorbed into the coal changes the porosity and permeability of the coal natural fracture system due to gas content changes, which cause matrix swelling or shrinkage due to relative adsorption of different gases. We collected sufficient information to develop a method for predicting the permeability and porosity of a coal bed as a function of the secondary porosity system (SPS) pressure and the gas content and composition of the primary porosity system (PPS). The method uses data from injection/falloff tests using water and/or a weaker adsorbing gas (WAG) than CH4 and a stronger adsorbing gas (SAG) than CH4. Estimates of effective permeability to gas and water obtained from these tests are used with an iterative computation procedure subject to constraints to solve for equivalent SPS porosity and absolute permeability at atmospheric pressure. Once calibrated, the model can be used to predict a coal bed's permeability and porosity as a function of injection pressure and injected fluid composition that in turn are used to predict injection performance. The model is applicable to production forecasts to account for SPS permeability and porosity changes as reservoir pressure declines with changes in gas composition. This paper describes the new model and discusses well test procedures to obtain the data required for model calibration. Also included are coal property estimates resulting from Alberta Medicine River (Manville) coal core and test data and an example model calibration.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".