Machine learning and seismic attributes for petroleum prospect generation and evaluation: An example from offshore Australia
Bibliographic record
Abstract
Abstract Seismic attributes play pivotal role in studying and understanding subsurface formations. Attributes can be extracted from seismic data and from postseismic inversion data. The growing number of seismic and elastic attributes poses a challenge, making the full benefit from each attribute very difficult, if not impossible. Various approaches are routinely used to select the best attributes for specific purposes. Machine learning algorithms have demonstrated good capabilities in combining appropriate attributes to address reservoir characterization problems. We aimed to use seismic and elastic attributes to detect hydrocarbon-saturated reservoirs, source rock, and seal rocks in the Poseidon field, offshore Australia. A large number of attributes weer extracted from seismic data and from impedance data. Artificial neural networks were implemented to combine the extracted attributes and convert them into petrophysical properties, namely, resistivity volume, and gamma ray volume, from which shale probability volume, sand probability volume, effective porosity volume, and gas chimney probability cubes are produced. The cubes were deployed for a detailed analysis of the petroleum system in the area. The produced shale and resistivity cubes helped delineate the seal rock and source rock in the area. Next, the reservoir intervals were identified using the porosity, shale, and resistivity volumes. A pretrained convolutional neural network was trained using another carefully selected attribute set to detect subtle faults that hydrocarbons might migrated through from source rock to trap. The integration of all the extracted cubes contributed to finding new prospects in the area and assessing their geologic probability of success. Our approach stands out for its multiphysical attribute integration, ML and human expertise incorporation, possible applicability to other fields.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".