SEISMIC DOWNSCALING

Reservoir models that honor field data and geological concepts are more likely to accurately forecast production and help risk analysis. However, few current methods can efficiently generate these useful models. We develop a new machine learning method Stochastic pix2pix. With this method we can efficiently generate reservoir models that match seismic, well-log and production data, and thus help decision making in reservoir development. We find correct geological concepts and accurate field measurements give accurate production forecasts, but a wrong geological concept and inaccurate measurements can introduce bias into the production forecasts. we recommend the use the new method for reservoir modeling when only small uncertainties are in the depositional environment.

Model high resolution reservoir architecture realizations from low resolution seismic information.

• Training from rule-based channel models upscaled from element to complex scale.
• Pixel-2-Pixel method learns relationships between element and complex scale architecture.
• Integration of well and seismic conditioning, full reservoir model.