Rigorous Estimation of the Initial Conditions of Flowback Using a Coupled Hydraulic-Fracture/Dynamic-Drainage-Area Leakoff Model Constrained by Laboratory Geomechanical Data
Bibliographic record
Abstract
Summary The application of rate-transient-analysis (RTA) concepts to flowback data gathered from multifractured horizontal wells (MFHWs) completed in tight/shale reservoirs has recently been proposed as an independent method for quantitatively evaluating hydraulic-fracture volume/conductivity. However, the initial fluid pressures and saturation in the fracture network and adjacent reservoir matrix are generally unknown at the start of flowback, creating significant uncertainty in the quantitative analysis of flowback data. In this study, we present a semianalytical flow model, coupled with a hydraulic-fracture (fracture) model and constrained with laboratory-based geomechanical data, for evaluating the initial conditions of flowback. In previous work, a semianalytical model based on the dynamic-drainage-area (DDA) concept was used to simulate water-based fluid leakoff from an MFHW into a tight oil reservoir (Montney Formation, western Canada), with minimal mobile water, during and after fracturing operations. The model assumed that each fracturing stage can be represented by a primary hydraulic fracture (PHF) containing the majority of the proppant, and an adjacent nonstimulated reservoir (NSR) or enhanced fracture region (EFR), which is an area of elevated permeability in the reservoir caused by the stimulation treatment. Each region was represented by a single-porosity system. The DDA propagation speed within the PHF during the stimulation treatment was constrained through using a simple analytical fracture model. Although this approach was considered novel, several improvements and additional laboratory constraints were considered necessary to yield more accurate predictions of initial flowback conditions. In the current work, the modeling approach described previously was improved by representing the EFR with a dual-porosity system; fully coupling the fracture model (used for PHF creation and propagation) with the DDA model for fluid-leakoff simulation into the EFR and adding a proppant-transport model; and modeling the shut-in period. Finally, to ensure that model geomechanics were properly constrained, a comprehensive suite of previously gathered laboratory data was used. Laboratory-derived propped (PHF) and unpropped (EFR) fracture-permeability/conductivity data as a function of pore pressure, as well as fracture-compressibility data, were used as constraints for the model. It should be noted that our model assumes that fracture closure has no effect on the pressure/saturation of the PHF/EFR/matrix. The improved model was reapplied to the tight oil field case and yielded more realistic estimates of initial flowback conditions, enabling more confident history matching of flowback data. The results of this study will be important to those petroleum engineers interested in quantitative analysis of flowback data to accurately obtain fracture properties by ensuring proper model creation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".