Investigation of permeability, formation factor, and porosity relationships for Mesaverde tight gas sandstones using random network models
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Bibliographic record
Abstract
Replicating the pore topology/structure of tight gas reservoirs is essential to model fluid flow through such porous media. Constitutive relationships between the macroscopic properties of the medium can often help with such modeling efforts. Permeability and formation factor are rock properties providing useful information for assessing the potential of hydrocarbon recovery. Pore topology/structure and pore–throat radius distributions are the major factors having influence on permeability and formation factor estimation. A stochastic random generation algorithm is employed to study the effect of pore structure and geometries on the relationships of formation factor–permeability and permeability–porosity on physically realistic 3D random networks. These relationships are derived by constructing two sets of physically equivalent pore networks of tight porous media and are validated using experimental measurements of Mesaverde tight gas sandstones. The first set of networks were based on Berea sand network properties, which are then reduced and derived using a Weibull truncated equation to produce physically sound tight porous media. The second equivalent networks are constructed according to experimentally derived throat size distributions obtained from ambient mercury injection capillary pressure for 17 selected core samples from three Mesaverde tight gas sandstones basins in the U.S. Imperial college Pore-Scale Modeling software is used to model the single liquid flow properties through constructed equivalent networks. The estimated porosity, absolute liquid permeability and formation factor of the constructed physically equivalent networks are in good agreement with measured data obtained by Byrnes et al. (Analysis of critical permeability, capillary and electrical properties for Mesaverde tight gas sandstones from Western US basins: final scientific. Technical report submitted to DOE and NETL 355, 2009 ). However, a variation between estimated absolute permeability to liquid and measured routine gas permeability is accounted in core samples that have measured permeability smaller than 0.1 mD. Networks based on Berea sand properties show qualitative agreement between modeled data points and experiment data. However, modeled data are off by two orders of magnitude and all fall more or less on the same line.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it