Fracture Propagation, Leakoff and Flowback Modeling for Tight Oil Wells Using the Dynamic Drainage Area Concept
Bibliographic record
Abstract
Abstract Recently it has been demonstrated that flowback data obtained immediately after fracture stimulation of multi-fractured horizontal wells (MFHWs) completed in tight/shale reservoirs may be analyzed quantitatively for hydraulic fracture properties. However, the initial conditions of fluid pressures and saturations at the start of flowback, which are a critical starting point for flowback simulation, are generally unknown and must be approximated. In order to properly initialize flowback simulations, the pre-flowback fracture stimulation treatment, as well as post-treatment shut-in period, should first be modeled in order to provide a reasonable estimate of fluid pressures and saturations within the hydraulic fracture and adjacent reservoir. In recent work, the authors developed a semi-analytical model to history-match flowback and early-time production data of MFHWs completed in tight oil reservoirs using the "dynamic drainage area" (DDA) concept. The model assumes that each fracture stage consists of a primary hydraulic fracture (PHF) region, and an adjacent enhanced fracture region (EFR) or non-stimulated region (NSR) in the reservoir. However, initial fracture fluid pressure in the PHF, and fluid pressure/saturation distributions in the adjacent EFR/NSR are required for the model initialization and are highly uncertain. In the current work, flowback data from a previously-analyzed MFHW horizontal well completed in a tight oil reservoir is revisited to determine if flowback initial conditions could be constrained rigorously. For this purpose, frac modeling (net-pressure analysis) was first performed using fracture treatment data for the well and commercial and publically-available simulators to constrain PHF property input for the DDA flowback model. The DDA model, run in injection mode, was then used to calculate the frac fluid leakoff rate from the PHF to the NSR during the fracture treatment, using the field frac pressures as input. Importantly, leakoff is modeled more rigorously using the DDA model than for the frac simulator, because it accounts for two-phase flow and stress-dependent permeability. Leakoff after the fracture treatment and before the flowback period was also modeled using the DDA approach to estimate the pre-flowback NSR fluid saturations and pressures, which served as the initial conditions for flowback modeling. The amount of leakoff estimated with the model is relatively small in this case, in part due to the small volumes of fluid used in the fracture treatment and low permeability of the reservoir. The resulting flowback history-match (also performed using the DDA model, in flowback mode) is similar to that achieved previously because the pre-flowback leakoff modeling resulted in only a slightly elevated water saturation estimate over virgin reservoir conditions. The results of this innovative approach to flowback modeling should be of interest to those petroleum engineers interested in quantitative analysis of flowback data to obtain fracture properties, but who are concerned about correct initialization of models for flowback simulation leading to more realistic results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".