Study of hydrocarbon detection methods in offshore deepwater sediment: An example in Equatorial Guinea
Bibliographic record
Abstract
Offshore oil and gas exploration has become the main field at present and in the future; however, the information about offshore deepwater exploration is less than onshore. At present, the offshore oil and gas exploration is mainly based on seismic data, thus, it is significant to use seismic data to detect hydrocarbon in offshore deepwater exploration. Using seismic data to do hydrocarbon detection is a process of inversion, and it has ambiguity and uncertainty. This paper illustrated five methods to detect hydrocarbon, namely, seismic amplitude attribute, frequency attribute, spectrum decomposition method, waveform classification and cross plot analysis of far offset stack data and near offset stack data. The combined application of those methods can greatly reduce the ambiguity and uncertainty. According to the study of deepwater sediment in Equatorial Guinea, the results of hydrocarbon detection coincide with drilled wells and the known hydrocarbon distribution. The combined using of those methods has achieved better effect in this study area, and has formed a series of methods of hydrocarbon detection in offshore deepwater oil and gas exploration.
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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 | 0.001 | 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) | 0.001 | 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 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".