New Models for Reserve Estimation and Non-Darcy Gas Flow in Shale Gas Reservoirs
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Bibliographic record
Abstract
Abstract Organic-rich shale gas reservoirs have various complexities related to the physics of gas storage and transport. Traditionally, the OGIP in shales has been calculated as the sum of the adsorbed gas and the free gas, using CBM reservoirs as an analog. However, as recently noted in the literature, the free gas volume must be corrected for presence of adsorbed gas, assuming all gas storage occurs in kerogen. Even with these corrections in place, shales are still complex reservoirs in terms of flow characteristics. The contribution of viscous, diffusive, and slip forces in nano-scale conduits cause the permeability calculated from Darcy's Law to be higher than the value for liquids. A new model is developed to address the effect of the adsorbed gas volume on the nanopore storage capacity. The relative fraction of adsorbed gas volume is treated as a sorbed-phase saturation. The initial free gas volume is then calculated by subtracting any non-free gas saturation from the effective void volume. We have extended this concept to a gas material balance equation through which the free gas volume is dynamically adjusted during depletion. The Simplified Local Density (SLD) adsorption model is used to evaluate sorbed-phase density and volume. To address the complexity in gas flow, permeability of the reservoir model is assumed to be a function of pressure in order to determine the impact of advection, slippage and diffusion mechanisms. The permeability is calculated via a multi-mechanism flow model. Finally, we utilized the dynamically-corrected permeability in parallel with dynamically-corrected porosity to simulate the primary recovery of a shale gas reservoir. The new models successfully describe the unique characteristics of shale reservoirs and correct the conventional methods for overestimation of reserves and underestimation of permeability. The format of the final material balance equation and flow model used here preserves the conventional reservoir engineering framework, but with some important modifications.
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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