125 research outputs found
Multi-scale uncertainty quantification in geostatistical seismic inversion
Geostatistical seismic inversion is commonly used to infer the spatial
distribution of the subsurface petro-elastic properties by perturbing the model
parameter space through iterative stochastic sequential
simulations/co-simulations. The spatial uncertainty of the inferred
petro-elastic properties is represented with the updated a posteriori variance
from an ensemble of the simulated realizations. Within this setting, the
large-scale geological (metaparameters) used to generate the petro-elastic
realizations, such as the spatial correlation model and the global a priori
distribution of the properties of interest, are assumed to be known and
stationary for the entire inversion domain. This assumption leads to
underestimation of the uncertainty associated with the inverted models. We
propose a practical framework to quantify uncertainty of the large-scale
geological parameters in seismic inversion. The framework couples
geostatistical seismic inversion with a stochastic adaptive sampling and
Bayesian inference of the metaparameters to provide a more accurate and
realistic prediction of uncertainty not restricted by heavy assumptions on
large-scale geological parameters. The proposed framework is illustrated with
both synthetic and real case studies. The results show the ability retrieve
more reliable acoustic impedance models with a more adequate uncertainty spread
when compared with conventional geostatistical seismic inversion techniques.
The proposed approach separately account for geological uncertainty at
large-scale (metaparameters) and local scale (trace-by-trace inversion)
Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning
Funding: This research was supported by Wood Mackenzie through funding of a Postdoctoral Research Associate position at Heriot Watt University, and through access to data from two basins. Acknowledgments: This work was supported by Wood Mackenzie through funding research collab- oration with Heriot-Watt University. All the data were anonymised and supplied by Wood Mackenzie and authors are thankful for the opportunity to publish the outcomes of this research. Authors also thank Mikhail Kanevski of University of Lausanne for the peer exchange on feature selection and the opportunities opened during his course on Machine Learning hands-on applications. Authors acknowledge the use of Orange Data Mining [27] and ML Office for SOM application [30]. We thank Susan Agar, who reviewed the paper most comprehensively and helped improve it along with two anonymous reviewers.Peer reviewedPublisher PD
Centar društvenih aktivnosti - Okrug Gornji : diplomski rad
Centar društvenih aktivnosti projektiran je u Okrugu Gornjem na otoku Čiovu, na mjestu sadašnje Lučice. U centru se isprepleću sadržaji društvenog centra, ugostiteljski sadržaji te sadržaji potrebni za funkcioniranje marin
- …