Drilling success as a result of probabilistic lithology and fluid prediction—a case study in the Carnarvon Basin, WA
Bibliographic record
Abstract
The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".