Pressure Depletion Mapping Using Multi-Well DFITs and its Applications in Hydraulic Fracture Characterization and Permeability Estimation: Examples from Montney Formation
Bibliographic record
Abstract
Abstract Recovery factor for multi-fractured horizontal wells (MFHWs) at development spacing in tight reservoirs is closely related to the effective horizontal and vertical extents of the hydraulic fractures. Direct measurement of pressure depletion away from the existing producers can be used to estimate the extent of the hydraulic fractures. Monitoring wells equipped with downhole gauges, DFITs from multiple new wells close to an existing (parent) well, and calculation of formation pressure from drilling data are among the methods used for pressure depletion mapping. This study focuses on acquisition of pressure depletion data using multi-well diagnostic fracture injection tests (DFITs), analysis of the results using reservoir simulation, and integration of the results with production data analysis of the parent well using rate-transient analysis (RTA) and reservoir simulation. In this method, DFITs are run on all the new wells close to an existing (parent) well and the data is analyzed to estimate reservoir pressure at each DFIT location. A combination of the DFIT results provides a map of pressure depletion around the existing well, while production data analysis of the parent well provides fracture conductivity and surface area and formation permeability. Furthermore, reservoir simulation is tuned such that it can also match the pressure depletion map by adjusting the system permeability and fracture geometry of the parent well. The workflow of this study was applied to two field case from Montney formation in Western Canadian Sedimentary Basin. In Field Case 1, DFIT results from nine new wells were used to map the pressure depletion away from the toe fracture of a parent well (four wells toeing toward the parent well and five wells in the same direction as the parent). RTA and reservoir simulation are used to analyze the production data of the parent well qualitatively and quantitatively. The reservoir model is then used to match the pressure depletion map and the production data of the parent well and the outputs of the model includes hydraulic fracture half-lengths on both sides of the parent well, formation permeability, fracture surface area and fracture conductivity. In Field Case 2, the production data from an existing well and DFIT result from a new well toeing toward the existing wells were incorporated into a reservoir simulation model. The model outputs include system permeability and fracture surface area. It is recommended to try the method for more cases in a specific reservoir area to get a statistical understanding of the system permeability and fracture geometry for different completion designs. This study provides a practical and cost-effective approach for pressure depletion mapping using multi-well DFITs and the analysis of the resulting data using reservoir simulation and RTA. The study also encourages the practitioners to take every opportunity to run DFITs and gather pressure data from as many well as possible with focus on child wells.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".