f Realistic Uncertainty Estimation in an Integrated Geostatistical Seismic Property Modeling

Geostatistical Seismic property modelling is about taking all available prior information and measurements into account. Evidently, one very important and valuable piece of information is the seismic data, which has extensive lateral coverage compared to the sparse information provided at well locations. Using Bayesian inference, we can incorporate a variety of data and expert knowledge to obtain predictions that are more realistic.

At the same time, using multiple realizations we are taking into account overall uncertainties including both variance and bias ( Moradi Tehrani 2016 ) that give us a handle to estimate and mitigate risk and make informed decision.

As there is no single right answer, the answer depends on the question. If we know our objective, then we can get the answer. In this case, we can also estimate how reliable the answer is.

By analyzing the realizations through ranking, we can determine the range of possible answers. Ranking relies on defining an objective criterion that captures the key characteristic of interest.

This approach is powerful for realistic uncertainty estimation and therefore provides a sturdier foundation for making informed decisions.