What do I need to know?
This course is designed to accommodate a wide range of participants, from those with no prior programming or mathematical background to those who are well-versed in these areas.
To ensure everyone can effectively grasp the core concepts, we’ve tailored the curriculum so it does not assume any pre-existing programming skills or mathematical knowledge. Instead, we start from the basics and gradually build up to more complex concepts.
However, if you’re someone with a strong background in mathematics or programming, fear not. The depth and breadth of our content, paired with hands-on exercises and practical demonstrations, ensure there is ample opportunity for you to hone your skills further and gain new insights into the application of machine learning in geoscience.
The course engages participants in a variety of learning activities, all of which are designed to be accessible and meaningful, irrespective of background. These include:
– Hands-on exercises
o Pen and paper
o Using Python
o Using Shaarc
– Practical demonstrations
– Real-world examples
About The Course
- Introduction to Machine Learning in Geoscience:
– An overview of machine learning concepts and their applications in geoscience.
– Recognizing the significance of data-driven solutions in seismic analysis.
- Warm-up Section: Introduction to Basic Math and Python for Machine Learning:
- Concise tutorials covering only those fundamental mathematical concepts and Python programming skills essential for machine learning in geoscience.
- The aim is to establish a strong foundation for all participants, regardless of their background, before delving into the core topics of the course.
- Fundamentals of Neural Networks:
- Starting from the basics: understanding the concept of a single neuron and its decision-making process.
- Progressing to the creation of a small neural network and demonstrating the pivotal aspect of neural networks: backpropagation.
- Implementing the model in Python and “training” it 10,000 times.
- Introduction to TensorFlow (Keras):
- Rewriting our Neural Network Solution in Keras and comparing it to our current solution – calibration.
- Expanding the model to a larger scale, suitable for inversion tasks.
- Modelling Acoustic Impedance from Seismic Data using traditional vs Neural Network techniques:
In this section, participants will find tutorials that employ industry-standard methodologies for comparing traditional techniques with AI/ML approaches.
- Reviewing traditional inversion techniques and acquiring appropriate training and test data.
- Scaling up the neural network to handle seismic trace dimensions.
- Enhancing the architecture of our existing neural network.
- Training the expanded network.
- Inferring acoustic impedance from seismic data.
- Introduction to Colab: Exploring the deployment of machine learning models in local or cloud-based environments, tailored to specific requirements.
- Utilizing Convolution in Geoscience Data Analysis:
- Expanding the neural network to incorporate convolutional layers.
- Providing a clear and concise explanation of convolutional neural network (CNN) architecture.
- Exploring Encoder- Decoder and UNET models.
- Modelling faults from Seismic Data using traditional vs Neural Network techniques:
Reviewing the coherence imaging technique used in seismic interpretation to identify and characterize faults in subsurface structures.
- Fault detection: Utilizing coherence imaging to identify areas of low coherence, indicating the presence of faults or fractures in the subsurface.
- Fault delineation: Employing coherence imaging to trace the fault plane by following the area of low coherence along the fault.
- Training a UNET for Fault Analysis.
- Inferring Faults directly from Seismic data using a UNET.
- Machine Learning without Neural Networks:
Classifying Seismic Data using traditional vs Neural Network techniques:
- Reviewing traditional ISODATA techniques employed in SHAARC.
- Exploring alternative and or equivalent machine learning approaches.
- Transformers and the LLM behind ChatGPT:
- To close, we have a lecture on the state of the art in Artificial Intelligence: Transformers
This course was created by a team of AI specialists and Geo-scientists, including Dr Gerald Stein
Dr Gerald Stein studied Physics and Mathematics before starting in the oil industry 30 years ago. He started working in the Western Atlas Research group under the leadership of Dr Oz Yilmaz. This group had a vast collective experience in mathematics, geophysics and programming gained whilst creating the first interpretation and inversion systems with Western Atlas Software. Dr Stein along with a number of colleagues started the original Pays International company in 1994. The company had a clear mission to improve exploration and production success for clients by creating inversion, seismic classification and fault analysis workflows and the associated software products. In order to spread these ideas he created an inversion and machine learning classification training course which was delivered to over 80 training courses in 15 countries, making it one of the most prominent providers of geophysical inversion and ML classification training in the world. His company has been consistently at the cutting edge of machine learning technology in geoscience, and continue to innovate to this day.
Dr Gerald Stein
Director Pays International
You do not need to know how to code. All the coding you need for the course will be taught on the first day.
You do not need to know advanced maths. All the maths you need for the course will be taught on the first day.
You do not need to know anything about AI. We will start at the beginning.
This course starts at the begining and goes rapidly but in small steps.
Yes it is and this is the most cost effective way for a company to put the geoscientists through the course.
In house courses are available anywhere in the world.
Public courses are run in London and Houston.
Geologists, Geophysicists and petroleum engineers
The course cost is £3,500 per person.
There are discounts for multiple attendees and in-house versions.
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