Advanced Modeling of Horizontal Well Performance Under Different Re-fracturing Designs in Tight Oil Reservoir
Bibliographic record
Abstract
Abstract Re-fracturing of a horizontal well is a method to restore the productivity of the well in unconventional reservoirs after the expected production decline. Consequently, a re-fracturing approach may be necessary to improve/enhance production and ultimate recovery. However, there are many challenges for optimizing re-fracturing treatment design, due to lack accurate quantification of depletion-induced pressure and local stress change around fractures. The workflow presented can be applied to study and optimize a re-fracturing job to prevent potentially catastrophic fracture hits during re-fracturing operations. In this paper, an integrated re-fracturing workflow was created and applied to determine the optimum re-fracturing strategy for multi-wells pad. This comprehensive workflow represents a multidisciplinary approach that integrates complex hydraulic fracture models, geomechanical models, and multi-well production simulation. The approach is able to couple simulated 3D reservoir pressure with a geomechanical model to quantify depletion-induced stress and pressure field change. Then, the altered stress field is utilized as the input for modeling the new fracture system created by the re-fracturing treatment. Two field cases from a tight oil reservoir are evaluated by comparing the model prediction to the pressure response. The model prediction agrees well with the observed pressure response and surface tiltmeter observations. The synthetic cases of interference between wells due to stresses and fracture design (number, placement and timing) are investigated in this work. A systematic sensitivity study is performed on the effects of re-fracturing time, fracturing spacing. It is shown that quantification of stress field changes during reservoir depletion provides new insights for the design and evaluation of re-fracturing treatments to enhance field development. There is a critical time in the life of the well that protection refrac could help pressurizing the formation directly by increasing pore pressure through fluid injection and indirectly by mechanical dilation of existing fractures. The dynamic changes of stress field can guide the optimization of re-fracturing mode and zipper fracturing to reduce stress shadow effect. The dynamic change of the pressure field optimizes the fracturing fluid volume, which can increases the volume of the fracture transformation and supply formation energy. The paper presents aN approach in calculating stress changes and dynamic fracture propagation into depleted region over time. Results obtained by this study give better understanding about propagation of new fractures as well as old fractures in re-fracturing process.
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".