Minimize Formation Damage in Water-Sensitive Unconventional Reservoirs by Using Energized Fracturing Fluid
Bibliographic record
Abstract
Abstract Slickwater has been widely used for hydraulic fracturing as it is inexpensive and able to carry proppants into the fracture. However, such fluid is unsuitable for water-sensitive formations, such as Montney. Water saturation around the fracture increases and clay swells when water leaks-off into matrix, both hindering natural gas flowing from matrix into fractures. N2 or CO2 energized water-based fracturing fluids have been widely used in water-sensitive formation as they can minimize fluid leak off during fracturing and achieve higher load fluid recovery during flow back. In this paper, multi-phase numerical simulations are applied to study the formation damage mitigation in Montney tight reservoir by using energized fracturing fluid. A simulation model is built and history matched with flow back and early production data of a typical Montney tight gas well. The behavior of multi-phase fluid leak off and flow back is studied, and the sensitivity of foam quality of fracturing fluid on load fluid recovery and well after stimulation productivity is analyzed. Statistical analysis is conducted on the stimulation and production data of over 5000 Montney wells to study the performance of energized fracturing in water-sensitive Montney formation. It is found that multi-phase fracturing fluid has less dynamic fluid leak off than that of a single phase fracturing fluid (i.e., water), and the major fluid leak off occurs during the static leak off period between the end of the stimulation processes and start of the flow back. Gas phase penetrates deeper and faster into the reservoir matrix in comparison with the liquid phase, which greatly contributes to flow back of fracturing fluid. Formation damage caused by fracturing fluid leak off can affect both early and long term production. In addition, N2-foam leads to the highest load fluid recovery in Montney formation, which is 1.6 times of that of CO2-foam. This work provides critical insights into understanding the performance of using energized fracturing fluid to mitigate formation damage in tight formations.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".