Volumetric Analysis of Two-Phase Flowback Data for Fracture Characterization
Bibliographic record
Abstract
Abstract Analysis of early-time production data obtained during the ‘flowback’ period presents the earliest opportunity to characterize a stimulated reservoir volume (SRV). Previous studies have developed analytical models/methods to analyze two-phase flowback data for fracture characterization. However, the mechanisms responsible for the early-time gas production in shales are poorly understood. The objective of this paper is to understand the mechanisms responsible for early gas production and develop a mathematical model to estimate effective fracture volume. The study incorporates a comprehensive field data analysis from 8 wells of a single well pad completed in the Horn River Basin. Firstly, we develop several diagnostic plots [production rates, cumulative gas production (Gp) vs. cumulative water production (Wp), GWR vs. Gp] to identify the early time trends/signatures. The Gas-Water-Ratio (GWR) plots from the wells considered indicate a V-shape trend, dividing the flowback data into two regions: 1) Early Gas Production-EGP and 2) Late Gas Production-LGP. Water rate increases during EGP, resulting in a decreasing GWR curve. Production then ’rolls over’ to the gas dominant phase with a positive GWR slope. Secondly, we introduce a method to estimate effective fracture volume by assuming a simplistic two-phase tank model for the fracture system. Conventional p/Z analysis shows that the fracture network can be approximated by a tank model during the EGP phase and that the dominant main mechanisms during EGP include the expansion of initial free gas in fractures (IGIF), fracture closure and the expansion of residual frac fluids (water). Effective fracture volume is calculated using a modified material balance approach for the two-phase system. The material balance approach enables the estimation of effective fracture volume regardless of the fracture geometry. Finally, the proposed model is validated by numerical simulation and is applied to estimate effective fracture volume using field production data.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".