August 5, 2021 | 9:00 a.m. - 9:30 a.m. CST (UTC-05:00)

Domain conversion of seismic data using a well-calibrated velocity model is currently the most common method for estimating subsurface depth during exploration. However, this method entails multiple, time-intensive steps that are user expertise dependent to ensure accurate results.

This conventional method also involves workflows that demand cross-domain inputs and qualitative analysis, which might introduce ambiguities and uncertainties in the final depth values. An erroneous depth estimation can result in an explorational disaster in terms of discovery failure and well integrity.

An expert velocity modeler uses several methods to ensure minimum time-to-depth conversion error. With this Artificial Intelligence (AI) and Machine Learning (ML) based domain conversion method, we replicated an expert velocity modeler’s approach to domain conversion by adopting their best practices and judgement. In this webinar, we discuss a case study where this AI/ML approach produced a faster and more efficient way of executing seismic domain conversion and how it can be implemented in near real-time for an exploratory field.

THREE REASONS TO ATTEND:

• Learn how AI/ML can be used to convert seismic time domain data into depth;
• Understand how to leverage advanced analytics in hydrocarbon explorational workflow; and
• Get insights about the data science approach to complement physics driven velocity modeling.