Stochastic Modeling of Multi-Phase Flowback From Multi-Fractured Horizontal Tight Oil Wells
Bibliographic record
Abstract
Abstract As a result of current commodity price differentials, North American resource development has shifted towards unconventional liquids-rich and light tight oil plays. Due to the low permeability of these plays, extensive hydraulic fracturing is commonly required for commercial development. Operators are seeking new methods to characterize hydraulic fractures, particularly early in the well life. One such method is to utilize regularly gathered high frequency (hourly or greater) fluid production and flowing pressures to model the flowback process. Previous studies have suggested that this data can be quantitatively analyzed to estimate hydraulic fracture half-length and other fracture properties. Flowback of multi-fractured horizontal tight oil wells, stimulated with water-based fluids, commonly exhibit two distinct segments: a) a short period of single-phase water flow, which continues until breakthrough of formation fluids; and b) multi-phase flow (water, oil and gas) following breakthrough. The first flow regime observed in data collected at common frequencies typically consists of fracture storage/depletion of fracture fluid. This flow regime is followed by the breakthrough of hydrocarbon and formation water which results in a deviation from the depletion signature. The analysis procedure used for analyzing this data builds upon the analytical history-matching methodology presented by Clarkson and Williams-Kovacs (2013b). Consistent with the previous work we model the first segment as a single phase depletion of the fracture pore volume, from which a pre-breakthrough estimate of fracture permeability and half-length can be determined. The second segment is modelled by assuming transient linear flow of oil and formation water to the fracture, under the assumption of perfect displacement of frac water by formation fluids. From the second segment we are able to estimate long-term effective fracture permeability and half-length. However, as pointed out by Clarkson and Williams-Kovacs (2013b) 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 will be 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. Additional improvements over the previous work include the consideration of different fracture geometries, the use of several fracture models to estimate fracture permeability, modeling produced water salinity to track the contribution of formation water and salt dissolution and additional constraints on relative permeability curve selection. The field case presented by Clarkson and Williams-Kovacs (2013b) for a prolific light tight oil reservoir is reanalyzed, along with a second well from the same pad 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) | 0.002 | 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".