EAGE London: AI-Driven Structural Seismic Interpretation

EAGE Local Chapter London

AI-Driven Structural Seismic Interpretation – application of ‘computer vision’ to complex interpretation challenges

Royal School of Mines

The talk will have a hybrid form: in-person at Imperial College, London and broadcasted ONLINE.Link to the webinar will be provided via e-mails to registered attendees: first e-mail will be sent two days before the event and the second one just 2 hours before the event. Do not register too late! Lecture Room G38 <h4>Agenda (UK time)</h4>18:30-18:35 Introduction and Announcements18:35-19:20 AI-Driven Structural Seismic Interpretation – application of ‘computer vision’ to complex interpretation challenges19:20-19:35 Q&amp;Afrom 19:35 Networking<h3> </h3><h4>Presenters</h4><h4>Dr Anat Canning, Distinguished Advisor | AI/ML, AspenTech</h4>As Distinguished Technologist for Data Science at AspenTech, Dr. Canning leads theresearch and development of machine learning technologies for next-generation E&amp;Psolutions. Her expertise encompasses machine learning, imaging and inversion, amplitudepreservation, seismic-to-well ties, seismic anisotropy and the derivation of rock propertiesfrom seismic. Prior to joining AspenTech, Dr. Canning worked at the Houston AdvancedResearch Center (HARC) as a senior research scientist, and at the Institute for PetroleumResearch and Geophysics (IPRG) as a senior geophysicist. She has served as a lecturer atleading technical universities worldwide. <h4>Rob Bond, Advisory Consultant, AspenTech</h4>Rob Bond is Advisory Solutions Consultant for Seismic Interpretation and Infrastructure inAspenTech Europe. He has held various customer-facing technical, advisory andmanagerial roles in Europe, Scandinavia, the Middle East and Asia Pacific, including atwelve-year stint as Product Management Director for Seismic Interpretation. Rob holds aFirst in Geological Sciences from Cambridge University. He is based in Surrey. <h4>Talk outline</h4>In recent years there has been a growing interest in the use of Machine Learning (ML)technologies for processing and interpreting seismic data. Indeed, many procedures thathave traditionally been performed with deterministic methods and algorithms may now beeffectively replaced by Neural Networks and other AI methodologies, thereby improvingsimplicity, productivity, and automation.In this presentation, we will briefly review key aspects of this topic, discussing the use ofpre-trained neural networks versus training on individual datasets, strategies for datalabeling and “problem posing” used in building AI models, user prompts, and the relevantimplementations for solving specific interpretation tasks.We will illustrate multi-horizon structural interpretation with examples of complexunconformities and other cross-cutting geological features observed in seismic images,together with results obtained from training a Deep Learning model based on aninterpreter’s interpretive perspective of selected seismic lines, and propagating thosesupervised interpretations through large seismic volumes, with appropriate and rigorousQC applied at all stages of the process.

EAGE London: Geological Hydrogen: Defining the Path to $1/kg Production

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EAGE London: AI-Driven Structural Seismic Interpretation

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EAGE London: AI-Driven Structural Seismic Interpretation

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EAGE London: Geological Hydrogen: Defining the Path to $1/kg Production

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