Novel Techniques for Modelling Re-Fracturing in Tight Reservoirs
Bibliographic record
Abstract
Abstract In the current low commodity price environment, several operators have chosen to return to their mature fields to re-fracture the reservoirs to improve overall recovery of reserves. Although modelling remains challenging, re-fracturing treatments have been successfully executed on multiple wells worldwide. This study compares three different approaches to model re-fracturing in tight sands as a means of reducing the overall uncertainty. Multi-domain integration was utilised in which geomechanical data was coupled with petrophysical analysis to build a three-dimensional (3D) hydraulic fracture model. Fracture pressure history matching was performed and the model was validated using calibration data available from microseismic analysis and extended leakoff tests. Production history matching was performed to validate the reservoir simulation model and estimate the resulting depletion around the wellbore. Geomechanical properties and the resulting minimum in-situ horizontal stress were re-calculated incorporating the results of depletion using three different approaches: Pore Pressure Model, Scale Model and Non-Scale Model. Finally, the hydraulic fracture models were re-constructed using the revised geomechanical properties to simulate re-fracturing using various pumping treatments. The fracture geometries obtained from re-fracture simulations were dependent on the model used for re-computation of geomechanical properties post-production as well as the fluid volume pumped. These properties were also validated using laboratory testing. Comparison of the three approaches indicated consistent geometries when small volumes of fluid or typical ‘Plug and Perf’ designs were pumped. Upon pumping larger volumes or typical ‘Sliding Sleeve’ treatments, major differences were observed using the three approaches indicating larger uncertainty and warranting the use of the more rigorous Non-Scale Model. Validated models are important tools for designing hydraulic fracturing treatments to avoid risks of sub-optimisation of fracture designs or undesirable bashing of offset parent wells. Good understanding of re-fracture models helps oil companies make informed decisions regarding their completion programme, hence improving overall hydrocarbon recovery. Modelling of re-fracture treatments continue to pose a challenge for engineers due to added subsurface static and dynamic complexities. This study presents basic guidelines to follow for modelling, with results verified from laboratory testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".