Numerical investigation of refracturing with/without temporarily plugging diverters in tight reservoirs
Bibliographic record
Abstract
Refracturing is an important technique to tap the potential of reservoirs and boost production in depleted oil and gas fields. However, fracture propagation during refracturing, including both conventional refracturing and temporary-plugging refracturing remains poorly understood, especially for cases with non-uniform distribution of formation pressure due to long-term oil production and water injection. Therefore, taking pilot tests of refracturing with sidetracking horizontal wells in tight reservoirs in the Changqing Oilfield, China as an example, we establish a three-dimensional numerical model of conventional refracturing and a numerical model of temporary-plugging refracturing based on the discrete lattice method. Non-uniform distributions of formation pressure are imported in these models. We discuss the effects of key operating parameters such as injection rate, cluster spacing, and number of clusters on the propagation of multi-cluster fractures for conventional refracturing. For temporary-plugging refracturing, we examine the impacts of controlling factors such as the timing and number of temporary plugging on fracture propagation. In addition, we analyze a field case of temporary-plugging refracturing using well P3 in the Changqing Oilfield. The results show that fractures during refracturing tend to propagate preferentially and dominantly in the depleted areas. Improved stimulation effect can be obtained with an optimal injection rate and a critical cluster spacing. The proposed model of temporary-plugging refracturing can well describe the temporary plugging of dominant existing-fractures and the creation of new-fractures after fracturing fluid is forced to divert into other clusters from previous dominant clusters. Multiple temporary plugging can improve the balanced propagation of multi-cluster fractures and obtain the maximum fracture area. The established numerical model and research results provide theoretical guidance for the design and optimization of key operating parameters for refracturing, especially for temporary-plugging refracturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".