July 7, 2021 | 9:00 a.m. - 9:30 a.m. CST (UTC-05:00)


One of the most important factors in successfully identifying reservoirs and estimating realistic reservoir parameters is to understand play characteristics within the associated geology. The first step is to identify geobodies with anomalous seismic behavior and then visualize them without any artifacts such as multiples, smiles or frowns resulting from migration anomalies, or offset gathers which are not flat, etc.

The second step is to rank the geobodies based on the likelihood of hydrocarbon occurrence in the sub-surface versus the estimated risks associated with recovering it. Determining the types of reservoir fluids is the most important factor in this workflow. The third step is to quantify other reservoir properties, such as risked volumes of gas in identified prospects. Artificial Intelligence (AI) and Machine Learning (ML) can help explorationists overcome the myriad challenges making reservoir fluid predictions.  Applying AI to estimate fluid likelihood in identified geoanomalies can reduce the risks significantly.

In this webinar, we propose a methodology where explainable AI has been used successfully to estimate the likelihood of several geoanomalies on the merit of their contents, i.e., reservoir fluids, pre-stack gather behaviors, and several other engineered features.


  • Learn how to adopt Explainable AI in quantitative seismic analysis;
  • Understand how to leverage advanced analytics in exploration risk management; and
  • Know more about the data science approach to complement physics driven AVO/AVA analysis.


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Presented by:

Samiran Roy

Samiran Roy

Global Advisor, Data Science

Shashwat Verma

Shashwat Verma

Consultant, Data Science