Physics-Based Fluid Flow Modeling of Liquids-Rich Shale Reservoirs Using a 3D 3-Phase Multi-Porosity Numerical Simulation Model
Bibliographic record
Abstract
Abstract Production from liquids-rich shale reservoirs in the United States and Canada has increased significantly during the past few years. However, a rigorous understanding of shale rocks and fluid flow through them is still limited and remains a challenge. Thus, the objective of this research is developing a 3D physics- based model for simulating fluid flow through these types of multi-porosity rocks. This is important given the recent spread of these types of reservoirs throughout the world. Simulation of liquids-rich shale reservoirs is carried out with the construction of an original fully- implicit 3D multi-phase semi-compositional finite difference numerical formulation, which uses a multiple porosity approach as well as diffusion from solid kerogen. The multi-porosity system includes (1) adsorbed porosity, (2) organic porosity, (3) inorganic porosity, (4) natural fracture porosity, and (5) hydraulic fracture porosity. The numerical model is developed with capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic pore walls, viscous and non- Darcy flow mechanisms (slip flow and Knudsen diffusion), and stress-dependent properties of natural and hydraulic fractures. Examples of simulated results are presented as cross-plots of pressure, production rates and cumulative production vs. time. These plots are utilized to show the contributions of free gas, adsorbed gas and dissolved gas on fluid production from liquids-rich shale reservoirs. Results indicate that both desorption and gas diffusion positively affect shales' performance. Simulation results demonstrate that not taking into account desorption and diffusion from solid kerogen leads to underestimating production from liquids-rich shale reservoirs. Furthermore, the simulation study shows that long periods of time are required for the effects of these two mechanisms to be manifested. This helps to explain why shales have been produced over long periods of time (several decades) like in the case of Devonian wells located in the Appalachian basin. The type of 3D simulation model for multi-porosity liquids-rich shale reservoirs developed in this paper is not currently available in the literature. The approach implemented in this work provides a novel and important foundation for simulating complex shale 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".