Fault Identification for the Purposes of Evaluating the Risk of Induced Seismicity: A Novel Application of the Flowback DFIT
Bibliographic record
Abstract
Summary The existence of faults, pre-existing hydraulic fractures, and depleted areas can negatively impact the development of unconventional reservoirs using multifractured 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. The objective of this study is to develop a diagnostic tool to identify these conditions using DFIT-FBA, a modified diagnostic fracture injection test (DFIT) with flowback analysis (FBA). The time and cost efficiencies of the DFIT-FBA method in reservoir characterization provides an opportunity to conduct multiple field tests at a single point or along the lateral section of a horizontal well. An analytical model that considers critical processes and mechanisms occurring during DFIT-FBA was first developed herein. The results of analytical modeling demonstrate that reservoir heterogeneities (i.e., faults) can be identified either by implementing multiple cycles of the DFIT-FBA method at a single point or by applying multiple DFIT-FBAs at different points along the lateral section of a horizontal well or at different wells. The analytical model is then verified using a fully coupled hydraulic fracture, reservoir, and wellbore simulator, and flowing pressure responses in the presence of a fault are illustrated. The practical application of the proposed method is demonstrated using DFIT-FBA field examples performed in a tight reservoir. Analysis of the field examples leads to the conclusion that a fault likely 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.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".