Stochastic Modeling of Two-Phase Flowback of Multi-Fractured Horizontal Wells to Estimate Hydraulic Fracture Properties and Forecast Production
Bibliographic record
Abstract
Abstract Although high-frequency fluid production and flowing pressures (hourly or greater) are commonly gathered in multi-fractured horizontal wells (MFHW), this data has rarely been used by industry in a quantitative manner to characterize hydraulic fracture or reservoir parameters and there has been ongoing debate about the usefulness of this data. It is likely that the multi-phase flow nature and the possibility of early data being dominated by wellbore storage have deterred many analysts. This work will expand on the flowback analysis work presented by Clarkson (2012b). Consistent with that work, our interpretation is that the early flowback data corresponds to wellbore + fracture volume depletion (storage) and it is assumed that fracture storage volume is much greater than wellbore storage. From this flow-regime, bulk permeability (dominated by fracture permeability) and effective fracture half-length can be estimated. However, as pointed out by Clarkson (2012b) there is a large degree of uncertainty in this type of analysis as a result of the number of unknowns which are being adjusted to provide an adequate history match. To better understand the uncertainty and the impact of each parameter, stochastic simulation was used to provide a range of parameter values, which provide an adequate fit of the data, and to determine which parameters have the greatest impact on the match. Stochastic simulation is also used to derive a long-term forecast using parameters derived from flowback analysis. Additional improvements over previous work include the consideration of different fracture geometries, the use of the matchstick model to estimate fracture permeability and additional constraints on relative permeability curve selection. The field cases presented by Clarkson (2012b) for shale gas reservoirs are reanalyzed for proof of concept and demonstration of the developed techniques.
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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 | 0.000 | 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".