Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction
| Autor: | Arild Buland, Eyvind Aker, Per Røe, Odd Kolbjørnsen, Ragnar Hauge, Abel Onana Ndingwan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Horizon (geology)
010504 meteorology & atmospheric sciences Lithology Petroleumsgeologi og -geofysikk: 464 [VDP] Bayesian probability Seismic stratigraphy 010502 geochemistry & geophysics 01 natural sciences Petroleum geology and geophysics: 464 [VDP] Seismic inversion/imaging Physics::Geophysics Seismisk inversjon/avbildning Bayesian Prediction Geophysics Geochemistry and Petrology Reservoir modeling Pore fluid Seismic inversion Petrology Geology 0105 earth and related environmental sciences |
| Zdroj: | R207-R221 Geophysics |
| ISSN: | 0016-8033 |
| Popis: | We have developed an efficient methodology for Bayesian prediction of lithology and pore fluid, and layer-bounding horizons, in which we include and use spatial geologic prior knowledge such as vertical ordering of stratigraphic layers, possible lithologies and fluids within each stratigraphic layer, and layer thicknesses. The solution includes probabilities for lithologies and fluids and horizons and their associated uncertainties. The computational cost related to the inversion of large-scale, spatially coupled models is a severe challenge. Our approach is to evaluate all possible lithology and fluid configurations within a local neighborhood around each sample point and combine these into a consistent result for the complete trace. We use a one-step nonstationary Markov prior model for lithology and fluid probabilities. This enables prediction of horizon times, which we couple laterally to decrease the uncertainty. We have tested the algorithm on a synthetic case, in which we compare the inverted lithology and fluid probabilities to results from other algorithms. We have also run the algorithm on a real case, in which we find that we can make high-resolution predictions of horizons, even for horizons within tuning distance from each other. The methodology gives accurate predictions and has a performance making it suitable for full-field inversions. We have developed an efficient methodology for Bayesian prediction of lithology and pore fluid, and layer-bounding horizons, in which we include and use spatial geologic prior knowledge such as vertical ordering of stratigraphic layers, possible lithologies and fluids within each stratigraphic layer, and layer thicknesses. The solution includes probabilities for lithologies and fluids and horizons and their associated uncertainties. The computational cost related to the inversion of large-scale, spatially coupled models is a severe challenge. Our approach is to evaluate all possible lithology and fluid configurations within a local neighborhood around each sample point and combine these into a consistent result for the complete trace. We use a one-step nonstationary Markov prior model for lithology and fluid probabilities. This enables prediction of horizon times, which we couple laterally to decrease the uncertainty. We have tested the algorithm on a synthetic case, in which we compare the inverted lithology and fluid probabilities to results from other algorithms. We have also run the algorithm on a real case, in which we find that we can make high-resolution predictions of horizons, even for horizons within tuning distance from each other. The methodology gives accurate predictions and has a performance making it suitable for full-field inversions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|