Bibliographic record
Abstract
Abstract It has been observed over the years that shale gas production modeled with conventional simulators/models is much lower than the actually observed field data. Generally reservoir and/or stimulated reservoir volume (SRV) parameters are modified (without much physical support) to match the production data. Instead of modifying the reservoir parameters without physical support, we aim to investigate the shale closely and see if we are missing some vital part in the flow physics. Shale is a complex unconventional reservoir with a significant organic fraction. Traditionally, it is perceived that the gas is stored in pore space and adsorbed on pore surfaces. In this work, we postulate that significant amount of gas is also stored in the bulk of organic matter or kerogen. We show a conceptual model of one shale pore and model the flow behavior taking into account the free gas (stored in natural fractures and nanopores), adsorbed gas, and gas dissolved in kerogen. Therafter, we upscale the model to a laboratory scale sample. We propose a numerical model for the complex "quad" porosity system while also accounting for non Darcy flow in shale nanopores. We then calibrate the model against a laboratory experimental data. This laboratory scale model can be upscaled suitably for field scale simulation of shale reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".