Shale oil flow simulation considering laminar characteristics and desorption of ad-/absorbed oil from kerogen
Bibliographic record
Abstract
Laminated continental shale oil reservoirs have the potential for commercial development. In this paper, a new simulation method for interlayer and intra-layer coupled flow in laminated shale reservoirs is established. This method simulates the structural characteristics of shale-sandstone longitudinal interlayer distribution by dual-porositysystem, and combines with chemical reaction model to characterize the desorption process of ad-/absorbed oil from kerogen in shale layers. Then, the intra-layer and interlayer interfacial flow mechanism in the depletion process is investigated, and the contribution of interfacial flow and desorption is analyzed. The results indicate that the sandstone layer is the main oil-producing layer, accounting for over 90% of the total oil production. However, the interlayer flow and kerogen desorption in the shale layers make significant contributions, resulting in an enhancement of 13.41% and 42.64% in the total oil production, respectively. Additionally, the desorption of ad-/absorbed oil from kerogen enhances the energy of both the shale and sandstone layers, significantly increasing their production. Moreover, higher pressure drawdown, total organic carbon (TOC) content, desorption rate, and horizontal permeability of sandstone layers are advantageous for the exploitation of shale oil.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".