Modelling Squeeze Treatments in Fractured Systems - A Case History
Bibliographic record
Abstract
Abstract Scale inhibitor squeeze treatments in high-permeability sandstone reservoirs can be readily simulated using matrix flow models. However, designing such treatments for application in fractured shale reservoirs is less developed, partly because the mechanisms for fluid flow are less well understood and partly because the manner by which the inhibitors are transported and retained in fractured shale formations differ considerably from the simple matrix flow encountered in sandstone reservoirs. Accurate prediction of squeeze treatment lifetimes is important for scale management both economically, to ensure optimum productivity at lowest cost of operation, and practically, to schedule treatments appropriately. Until recently this has not been achievable for fractured shale formations without the use of full-field simulators. This paper demonstrates that even a near-wellbore model, if appropriately modified, can achieve good agreement with inhibitor-returns field data from bullheaded squeeze treatments in 7 different multiply fractured wells in 2 different Unconventional shale formations. Field data were compared with simulations using a model that couples inhibitor diffusion into and out of the rock matrix with adsorption either onto the rock matrix or the fracture proppant or both; it was found that in some field cases there is negligible difference between inclusion or exclusion of proppant adsorption, whereas in others much better simulation of inhibitor returns is observed if proppant adsorption is included. Other aspects have been included in the model, such as the influence of proppant embedment (changing the porosity of the fracture void) and treatment of only a fraction of the multiple fractures present in a well. Interestingly, inhibitor returns in the 3 wells in one field correlated best with simulations assuming only a low fraction (up to 30%) of fractures were treated by the squeeze, whereas simulations from 4 wells in another field correlated better with a much higher fraction of fractures (60 – 90%) being treated. This paper illustrates that an appropriate near-wellbore model can give good agreement with field data provided plausible physical phenomena are included, and that such a model can be used to design better squeeze treatments in Unconventional fractured-shale reservoirs without the need for complex full-field simulators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".