A review on multicomponent seismology: A potential seismic application for reservoir characterization
Bibliographic record
Abstract
Searching for hydrocarbon reserves in deep subsurface is the main concern of wide community of geophysicists and geoscientists in petroleum industry. Exploration seismology has substantially contributed to finding and developing giant fields worldwide. The technology has evolved from two to three-dimensional method, and later added a fourth dimension for reservoir monitoring. Continuous depletion of many old fields and the increasing world consumption of crude oil pushed to consistently search for techniques that help recover more reserves from old fields and find alternative fields in more complex and deeper formations either on land and in offshore. In such environments, conventional seismic with the compressional (P) wave alone proved to be insufficient. Multicomponent seismology came as a solution to most limitations encountered in P-wave imaging. That is, recording different components of the seismic wave field allowed geophysicists to map complex reservoirs and extract information that could not be extracted previously. The technology demonstrated its value in many fields and gained popularity in basins worldwide. In this review study, we give an overview about multicomponent seismology, its history, data acquisition, processing and interpretation as well as the state-of the-art of its applications. Recent examples from world basins are highlighted. The study concludes that despite the success achieved in many geographical areas such as deep offshore in the Gulf of Mexico, Western Canada Sedimentary Basin (WCSB), North Sea, Offshore Brazil, China and Australia, much work remains for the technology to gain similar acceptance in other areas such as Middle East, East Asia, West Africa and North Africa. However, with the tremendous advances reported in data recording, processing and interpretation, the situation may change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".