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Bridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
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Upstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableUpstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableBridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
The digital learning ecosystem Digitally and seamlessly connecting you, the learner, with pertinent learning objects and related technologies ensuring systematic, engaging and continued learning.
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Upstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableUpstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableBridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
Develop measurable skills and capabilities
We all have seen displays of seismic data in the form of sections or cubes of data. But what do they show and how are they acquired? In this course you will learn to understand that seismic data represents observations of the movement of the surface, resulting from waves emitted by a source (dynamite or vibrator) and reflected by changes in the subsurface rocks. Hence, what we record is related to the properties of the rocks, not only its minerals and its cement, but also its pore fluids.
All information on the subsurface is contained in these observations or records but are exceedingly difficult to extract and understand directly. Therefor the records need to be processed to make it possible to interpret structure and content of the pore space. In this course, the basic principles of acquisition and processing will be explained by using concepts and plain language to describe the physics behind traditional and advanced methods used.
These advanced methods allow a much more accurate interpretation of seismic data. Hence, the aim of this course is not to fully learn the underlying physics, but to understand its importance in enabling interpreters, reservoir engineers to formulate the right questions for acquisition and processing geophysicists.
The course consists of presentations, videos and exercises applying the theory to realistic examples. It uses a Blended Learning approach based on a user-friendly Learning Management System, called Moodle.
This course is a blended course. It is lead remotely by the instructor that has daily direct interactions with the trainees for several hours. Trainees are self-learning for the rest of the day by doing exercises and going through the course material.
The course consists of presentations, videos, and exercises. The presentations, also made available in pdf, are animated power point shows. The videos are either related to the exercises or general, some related to professional societies. In the exercises the methods discussed in the presentations are applied using computer programs. The solutions are discussed. Also, the course contains quizzes which are meant to reinforce the learning. Each quiz consists of multiple-choice questions.
Part 1
Part 2
Part 3
Part 4
Part 5
Non-geophysicists that want to have the skill to formulate the correct requests to geophysicist to obtain the best interpretable seismic data.
What does seismic data represent, how is it acquired and processed? What do Fresnel zone, statics, stacking and stacking velocities mean? Is it important to consider the phase of the “wavelet†in the data and what is a wavelet? How are data moved (migrated) to provide a better view on the subsurface structures? As reflections, describing the subsurface are obtained in two-way reflection time, how to map them into depth? As the pore fluid is above all important, how can it be observed in the data? Often Inversion is mentioned, but how can it help the interpreter? And finally, how helpful are artificial intelligence methods, like Machine learning?
A very basic understanding of geophysics.
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