Experimental Investigation into the Mechanisms of Liquid-Phase Damage in Shale Oil Reservoirs: A Case Study from the Leijia Area
Bibliographic record
Abstract
The fourth member of the Shahejie Formation in the Leijia area of the western depression of the Liaohe Oilfield represents a typical shale oil reservoir. However, post-hydraulic fracturing operations in this region are often hindered by significant discrepancies in well productivity, low fracturing fluid flowback efficiency, and an unclear understanding of reservoir damage mechanisms during fracturing. These challenges have become major bottlenecks restricting the efficient exploration and development of shale oil in this block. In this study, a series of laboratory-simulated experiments were conducted to investigate the primary mechanisms of formation damage induced by fracturing fluids in shale oil reservoirs. An experimental methodology for evaluating reservoir damage caused by fracturing fluids was developed accordingly. Results indicate that guar gum-based fracturing fluids exhibit good compatibility with formation-sensitive minerals, resulting in relatively minor damage. In contrast, capillary trapping of the aqueous phase leads to moderate damage, while polymer adsorption and retention cause low to moderate impairment. The damage associated with fracturing fluid invasion into fractures is found to be moderately high. Overall, the dominant damage mechanisms of guar gum fracturing fluids in the Shahejie Member 4 shale oil reservoir are identified as aqueous phase trapping and polymer adsorption. Based on the identified damage mechanisms, corresponding optimization strategies for fracturing fluid formulations are proposed. The findings of this research provide critical insights for improving shale oil development strategies in the Leijia area.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".