Physics-Based Approach for Shale Gas Numerical Simulation: Quintuple Porosity and Gas Diffusion from Solid Kerogen
Bibliographic record
Abstract
Abstract The objective of this study is to provide a detailed physics-based explanation of production behavior of shales through the construction of a fully implicit finite difference numerical model that includes five storage mechanisms as well as the gas dissolved in solid kerogen. Shale gas reservoirs are characterized by multiple porosities such as inorganic matrix porosity, organic matrix porosity, microfracture and slot porosity, hydraulic fracture porosity created during the stimulation of the shale reservoir, and adsorbed porosity. In addition to this complex system, natural gas is trapped and stored in shales as free, adsorbed and dissolved gas. Simulation of shale gas reservoirs is conducted by the introduction of a quintuple porosity approach in which the aforementioned storage mechanisms, the dominant nano-scale structure of shales and the presence of viscous flow, slip flow and Knudsen diffusion are honored in order to rigorously represent the physics associated with gas flow in these types of reservoirs. Simulated results are presented as cross-plots of P/Z vs. Gp, which are used to show the effects of the quintuple storage formulation on the behavior of shale gas reservoirs. The gas contributions due to free gas, adsorbed gas and dissolved gas are highlighted as part of the numerical modeling results. The numerical model is also compared against real data from Devonian gas shales. The characteristic signature of actual data is reproduced by the numerical simulator developed in this work. This paper presents a comprehensive methodology, which efficiently handles all the storage mechanisms present in shale gas reservoirs in such a way that better estimates of original gas in place (OGIP) and recoveries can be performed. This work also helps to reduce the uncertainty related to shale gas production forecasts since the current available simulators do not efficiently represent the physics of these unconventional reservoirs. Therefore, it is crucial to consider all the factors addressed in this paper. Ignoring one of these elements will lead to underestimation of gas production from shale gas reservoirs. It is concluded that the simulation approach proposed in this study provides a more accurate reservoir model for shale gas representation and a better understanding of the flow mechanisms occurring in these reservoirs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".