Numerical Investigation of Coupling Multiphase Flow and Geomechanical Effects on Water Loss During Hydraulic-Fracturing Flowback Operation
Bibliographic record
Abstract
Summary Less than half the fracturing fluid is typically recovered during the flowback operation. This study models the effects of capillarity and geomechanics on water loss in the fracture/matrix system, and investigates the circumstances under which this phenomenon might be beneficial or detrimental to subsequent tight-oil production. During the shut-in (soaking) and flowback periods, the fracture conductivity decreases as effective stress increases because of imbibition. Previous works have addressed fracture closure during the production phase; however, the coupling of imbibition caused by multiphase flow and stress-dependent fracture properties during shut-in is less understood. A series of mechanistic simulation models is constructed to simulate multiphase flow and fluid distribution during shut-in and flowback. Three systems—matrix, hydraulic fracture, and microfractures—are explicitly represented in the computational domain. Sensitivities to wettability and multiphase-flow functions (relative permeability and capillary pressure relationships) are investigated. As wettability to water increases, matrix imbibition increases. Imbibition helps to displace the hydrocarbons into nearby microfractures and hydraulic fractures, enhancing initial oil rate, but it also hinders water recovery. The results indicate that fracture closure may enhance imbibition and water loss, which, in turn, leads to further reduction in fracture pressure and conductivity. Results also suggest that more-aggressive flowback is beneficial to water cleanup and long-term oil production in stiff rocks, whereas this benefit is less prominent in medium-to-soft formations because of excessive fracture closure. Because no direct correlation between high initial oil-flow rate and improved cumulative oil production is observed, measures for increasing oil relative permeability are recommended for improving long-term oil production. This work presents a quantitative study of the controlling factors of water loss caused by fluid/rock properties and geomechanics. The results highlight the crucial interplay between imbibition and geomechanics in short- and long-term production performances. The results in this study would have considerable impact on understanding and improving current industry practice in fracturing design and assessment of stimulated reservoir volume.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".