Estimating reservoir zone from seismic reflection data using maximum-likelihood sparse spike inversion technique: a case study from the Blackfoot field (Alberta, Canada)
Bibliographic record
Abstract
Seismic inversion involves extracting qualitative as well as quantitative information from seismic reflection data that can be analyzed to enhance geological and geophysical interpretation which is more subtle in a traditional seismic data interpretation. Among many approaches that have been made to improve interpretation of post-stack seismic data, a great effort has been made to use maximum likelihood (ML), sparse spike inversion (SSI) along with multi-attribute analysis (MAA) aimed to increase the resolution power of interpreting seismic reflection data and mapping into the subsurface lithology. These methods are applied to the Blackfoot seismic reflection data to estimate reservoir. The methods were first applied to the composite trace close to well locations and were inverted for acoustic impedance (AI). The results depict that the inverted AI matches very well with the well log AI. The statistical analysis demonstrates good performance of the algorithm. Thereafter, the entire seismic section was inverted to acoustic impedance section. The analysis of the inverted impedance section shows an anomaly zone in between 1060 and 1075 ms time and characterize it as reservoir. Further, the multi-attribute analysis is performed to estimate porosity and density in the inter-well region. The inverted porosity section shows a high porosity anomaly and a low density anomaly in between 1060 and 1075 ms time intervals which corroborated well with the low impedance zone and confirm the presence of a reservoir.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".