Fault Identification for the Purposes of Evaluating the Risk of Induced Seismicity: A Novel Application of the Flowback DFIT (DFIT-FBA)
Bibliographic record
Abstract
Abstract The existence of faults, pre-existing hydraulic fractures, and depleted areas can have negative impacts on the development of unconventional reservoirs using multi-fractured horizontal wells (MFHWs). For example, the triggering of fault slippage through hydraulic fracturing can create the environmental hazard known as induced seismicity (earthquakes caused by hydraulic fracturing). A premium has therefore been placed on the development of technologies that can be used to identify the locations of fault systems (particularly if they are subseismic), as well as pre-existing hydraulic fractures and depleted areas that can similarly negatively impact reservoir exploitation. The objective of this study is to develop a diagnostic tool to identify these conditions using DFIT-FBA. DFIT-FBA is a modified diagnostic fracture injection test (DFIT) whereby a sequence of injection and flowback steps are performed to estimate minimum in-situ stress, fracture surface area, reservoir pressure, and permeability in shale and tight reservoirs. The time- and cost-efficiency of the DFIT-FBA method provides an opportunity to conduct multiple field tests at a single point, or along the lateral section of a horizontal well, without significantly delaying the completion program. The proposed diagnostic tool uses an analytical model which considers critical processes and mechanisms occurring during a DFIT-FBA test, including wellbore storage, leakoff rate, and fracture stiffness development. The results of analytical modeling demonstrate that faults, pre-existing hydraulic fractures, and depleted areas of the reservoir can be identified either by implementing multiple cycles of the DFIT-FBA test at a single point, or by applying multiple DFIT-FBA tests at different points along the lateral section of a horizontal well or at different wells. The analytical model is first verified using a fully-coupled hydraulic fracture, reservoir, and wellbore simulator, and flowing pressure responses in the presence of different reservoir heterogeneities are then illustrated. Practical application of the proposed method is demonstrated using DFIT-FBA field examples performed in a tight reservoir. Analysis of the field examples results in the conclusion that a fault occurs near the toe of the horizontal lateral. This finding was confirmed by other field information and provides the opportunity to modify the main-stage hydraulic fracturing design to avoid induced seismicity events. This study proposes a novel, fast, and low-cost approach for identifying faults, pre-existing hydraulic fractures, and depleted areas using the DFIT-FBA test. The recommended approach can help engineers to characterize the reservoir quality along a horizontal well, as well as identify features/conditions that could negatively influence reservoir development, such as faults (and the possibility of creating induced seismicity), pre-existing hydraulic fractures, and reservoir depletion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".