Parametric Probabilistic Models for Fluid Diffusivity Inversion and Forward Microseismic Generation Using Seismicity Rates
Bibliographic record
Abstract
Abstract Fluid diffusivity inversion and injection-induced microseismicity provide useful means of evaluating unconventional reservoirs. When taking into consideration geomechanics, linear poroelasticity equations provide the key connection between diffusion and microseismicity. This work explores microseismicity generation from a probabilistic point of view, embedding uncertainty assessment in its quantification. Bayesian framework serves as the base for probabilistic analysis. The primary objective of this work is to develop a new microseismicity probabilistic model framework that can be used for uncertainty quantification in data analysis as well as to provide forward modeling of microseismicity. Results are tested against real data of Horn Rives shales in Western Canada, and show good prediction of the number of actual microseismicity occurrences against time. The novel probabilistic model is derived from Directed Graphic Model using a statistical learning framework. Stochastic Poisson process combined with a specific rate model is integrated to generate a likelihood function. Both, parameter inference and microseismic event forecast are assisted by Bayesian theorem. The model has an intrinsic statistical learning root, which specifically uses observed microseismic data to update model parameters and then is applied for microseismicity prediction. The model is extended to take into account basic geomechanical principles. Dieterich (1994) relative seismicity rate model is integrated into the aforemantioned probabilistic model. We use a synthetic case to demonstrate how the probabilistic model can be connected with stress and pressure information from geomechanical calculations. The novelty of this study is the development of a probabilistic microseismic prediction model which obeys rate-and-state law based relative seismicity rate constitutive equations. The model inherently considers time dependence of nucleation and fault geomechanics. It can be used for planning purposes in the pre-hydraulic fracturing stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".