Coupled Versus Stratified Flow of Water and Hydrocarbon During Flowback and Post-flowback Processes
Bibliographic record
Abstract
Abstract Pore-scale images and core-scale imbibition experiments suggest that hydrocarbon and water tend to flow through their own pore network, referred to as "stratified flow". The objective of this paper is to investigate the occurrence of these phenomena at reservoir scale by analyzing flowback and post-flowback production data. Understanding multiphase flow regimes is the first step for selecting proper relative permeability curves and developing representative multiphase rate-transient models. Another objective of this paper is to investigate the harmonic decline (HD) behavior of water-oil ratio (WOR) versus cumulative water production volume and how it could be employed to compare load recovery performance of different wells. We analyzed flowback and post-flowback production data of six multi-fractured horizontal oil wells completed in Eagleford Formation. The proposed data-driven methodology involves using multiphase diagnostic plots of rate-normalized pressure, rate decline, and WOR. We applied this methodology to i) investigate the relationship between water and hydrocarbon at early production time; ii) model WOR with respect to cumulative water production; and iii) evaluate how fracturing/completion design parameters affect well performance. The results show three key findings: i) During early production time, we observe independent flow regimes (stratified flow) of water and oil indicating their production under different drive mechanisms. Water is produced from an apparently closed tank comprising induced fractures and the surrounding stimulated matrix, and oil is produced independently at a significantly lower rate due to oil influx from matrix into fractures. ii) After jet-pump installation, we observe coupled flow of water and oil indicating their production under similar drive mechanisms provided by the pump. iii) Semi-log plot of WOR versus cumulative water production shows HD trend that is relatively less sensitive to operational changes compared to water rate-decline plots.
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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 | 0.000 | 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) | 0.001 | 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".