Curvature attributes with the 3D seismic Kalman filter for fault opening description — An application to a shale oil reservoir in the Jimsar sag, Junggar Basin, Xinjiang
Bibliographic record
Abstract
ABSTRACT The large-scale shale-oil production in the Jimsar sag of the Junggar Basin is being aggressively developed. To improve unconventional production and make precise suggestions for horizontal well developments, fault interpretations using seismic data play an important role. The target reservoir in the Jimsar sag of the Junggar Basin has a comprehensive network of faults, according to the drilled wells and fault identification from the broadband azimuth and high-density 3D seismic data. Several reasons such as the integration between source and reservoir, the substantial abundance of organic matter, and the major reservoir plasticity result in different fault types, which then create different impacts on horizontal wells. We have developed a fault-type identification tool using the curvature attributes via the 3D seismic Kalman filter to provide a more accurate description of fault types. In contrast with previous studies, we derive the Kalman-curvature formulation in terms of space and time, by using a dynamically iterative optimization model. Our strategy reveals that various fault types (open faults, semiopen faults, and sealed faults) represent their characteristics associated with the 3D Kalman-curvature attributes. The resultant fault divisions are valid by combining the drilled information of lost circulation and fracturing crosstalk of the horizontal wells. High-quality field results validate the robustness of our algorithms.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".