Effect of Fluid Rheology and Reservoir Compressibility on Microseismicity During Hydraulic Fracturing
Bibliographic record
Abstract
Abstract Microseismic monitoring, as an essential surveillance tool of hydraulic stimulation, enables the industry to optimize the development of unconventional reservoirs ranging from wellbore azimuth and wellbore spacing to completion and treatment design as well as providing a means for quality control and treatment efficiency evaluation. In order to correctly interpret microseismic data, a clear understanding of the mechanisms driving the generation of microseismicity is crucial. It is widely accepted and supported by full moment tensor inversion that microseismic activity associated with subsurface fluid injection is mainly caused by shear failure. Microseismic events can be separated into two main categories: near-field events and far-field events. Near-field events are the microseismic events close to the induced fracture (i.e. hydraulic fracture); as a result, the stress shadow effect (i.e. stress perturbation around a hydraulic fracture) is the dominant factor controlling such events. Far-field events are events that are not occurring in the vicinity of the tensile hydraulic fracture, hence the stress shadow effect is not the dominant phenomenon, but they are mainly controlled by pore pressure diffusion. Recent seismicity-based permeability characterization models couple fluid flow and reservoir geomechanics in order to capture microseismic mechanisms and calculate bulk reservoir-scale permeability. The same fundamentals were used in a forward modeling sense; by knowing reservoir properties we were able to predict the spatio-temporal distribution of microseismic activity. The effects of three parameters, including reservoir fluid viscosity, fracture fluid viscosity, and total reservoir compressibility were studied. Furthermore, the microseismic response of the reservoir in conjunction with instantaneous pump attributes was investigated. A physics-based model showed that increasing the viscosity of both reservoir and hydraulic fracture fluid will decrease the local extent of the propagation of the microseismic cloud. Studying the effect of reservoir compressibility showed that an increase in total reservoir compressibility decreases the local extent of the microseismic cloud. Results of the analysis were then used to explain observations made in field data and described in the literature.
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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 | 0.001 |
| 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 | 0.000 | 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".