Numerical modeling of seismic responses from fractured reservoirs in 4D monitoring — Part 1: Seismic responses from fractured reservoirs in carbonate and shale formations
Bibliographic record
Abstract
ABSTRACT One of the most urgent problems of oil and gas reservoir monitoring is the assessment of fractured reservoir infill type — with fluid-filled, gas-filled, or closed (no-reservoir situation) fractures, which is of significant value for time-lapse seismic technology. We used the grid-characteristic method for numerical modeling of seismic responses from fractured periodic elastoacoustic structures. We consider every single fracture individually (without using the effective medium approach), and we set explicit boundary conditions on fracture surfaces. We assume realistic height-to-thickness ratios — fracture opening (aperture) — equaling three to five orders of magnitude. These techniques make our models as close to real fractured reservoirs as possible. Analyzing the simulated seismic responses, we solve the problem of assessing the fractured reservoir infill type. As a result, previously unknown properties of seismic responses from fractured reservoirs were revealed. We use amplitude variation with offset (AVO) as the main tool for the analysis of the fracture infill type effect on the seismic response in three frequency ranges. Three out of four models exhibit a stable positive AVO gradient regardless of the rock type and frequency range. The analysis of linearized Zoeppritz equations confirms such AVO behavior. We have developed quantitative criteria (indicators) for recognition of a fracture infill type. Amplitude-frequency analysis is shown to expand the capabilities of the infill type recognition. Thus, a method for determining the fractured reservoir infill type is established for carbonate and shale formations, which could become the basis for a new direction in time-lapse technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".