Flowback Analysis of Three-Phase Fluid from Hydraulically Fractured Shale Oil Wells
Bibliographic record
Abstract
Abstract Flowback data obtained immediately following the Hydraulic fracturing (HF) job offers a valuable opportunity to characterize HF through rate transient analysis (RTA). This study introduces a three-phase flowback RTA specifically designed for shale oil wells by considering the emerging dissolved gas and its flow in addition to the water and oil flow during the flowback period. We propose a three-phase flowback model based on material balance for the flow of water, oil, and gas within the HF network in multi-fractured horizontal oil wells (MFHWs). Gas begins to emerge once HF pressure drops below the bubble point pressure. We derive pressure diffusivity equations for each phase and use a material balance approach to calculate the HF average pressure. Pseudo-variables are defined to create diagnostic plots for identifying flow regimes and specialty plots for characterizing HF properties. We validate the proposed model against the results of a numerical simulation. Diagnostic plots for each phase are constructed based on three-phase production data. During the early flowback period, the diagnostic plots for the water and oil phases display a half-slope straight line, indicating the infinite-acting linear flow (IALF) regime. This is followed by a unit-slope line, signaling the transition to the boundary-dominated flow (BDF) regime. With the known start point of the BDF, we apply straight-line analysis by defining specialty plots. The estimated initial HF permeability and HF half-length from our three-phase flow models closely matched the set values from the numerical model, with relative errors below 10%. The results of flowback RTA using the proposed model indicate that considering only water and oil flows may not be sufficient to estimate HF properties accurately. The proposed model is also applied to a field case in a tight oil reservoir in Western Canada to demonstrate its applicability. The proposed model provides an early understanding of HF performance, dynamics, and closures by considering the three-phase flow within MFHWs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".