Internship - AI Engineer – Model adaptation for Borehole images (6 months)

Key Skills

Aartificial intelligence, Algorithms, Augmented Reality (AR), dense 3D reconstruction, Feature Extraction, Keras software library, Large scale deployment, Mathematical optimization, Mathematics, Natural Language Processing (NLP), Python Programming, PyTorch, Regression Analysis, Statistical modeling, Statistics, TensorFlow, Terabyte scale datasets

Job Description

We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

Our Purpose

Together, we create amazing technology that unlocks access to energy for the benefit of all.

You can find out more about us on https://www.slb.com/who-we-are

Location: Clamart, France

Come and Join SLB’s AI Lab in Paris. We are currently offering internship to bright minds specialized in Data Science and Artificial Intelligence. Discover a multinational company. We have brought a little bit of the Silicon Valley in Paris. Experience working within a team of young and fun passionate Data Scientists, tackling real business challenges, in tandem with business experts who are sitting at your desk.

The Artificial Intelligence & Machine Learning Data Scientist helps develop software and processes that can be used for robotics, artificial intelligence programs and application. In close collaboration with the business and métiers, the data scientist offers mathematical and statistical models from the collected data to augment, improve or speed up human decisions within the Energy sector. With SLB you will be given the opportunity to apply your expertise and deploy deep learning solutions at scale on real-world problems, supporting many areas of SLB’s business. SLB is the first Energy service company to move its processes and workflows in the cloud. This gives the Embedded AI Lab the perfect opportunities to leverage these innovative technologies and resources. As part of the Embedded AI Lab you will be able to test, experiment and research with the bleeding edge environment with Petabits and Petabits of data. You will be in charge of applying research and delivery of Proof of Concepts solutions, responding to a clear and specific business needs..

Description:

Borehole image data acquired in subsurface environments (underground) differs significantly from any conventional image datasets used in model design among computer vision researchers across the world. This is mainly due to a variety of sensing technologies available in the tools employed for data acquisition in Energy industry. Such differences result in an image that spans more than 100,000 rows with only a few dozen columns. They depict various periodic patterns in x and y directions and are a result of specific mapping from 3D to 2D space.

Because the images are so long, they cannot fit into the memory during training, and we are usually splitting them into small patches. Moreover, only a very small fraction of the input images is informative of the label of interest, resulting in a low region of interest (ROI) to image ratio. However, most of the popular convolutional neural networks (CNNs) are designed for images that have relatively large ROIs and have a balanced height-to-width ratio.

The goal of this internship is to challenge the traditional CNN-based design and explore new ways of representation learning specifically adapted for borehole image datasets. Different datasets with ultrasonic, electromagnetic, and microscope borehole images have been already collected internally and from publicly available sources and will be available within the scope of this internship. This will enable us to build a cumulative foundation of techniques including dilated convolutions, multi-scale model designs, new regularization methods for squeezing and stretching the images, and video-like representation and processing to name a few. This has a wide application benefiting well integrity, geology interpretation and well construction projects across SLB.

Required skills:

• Skills: applied mathematics, probability & statistics, image processing, deep learning models like CNNs, Siamese Networks, Autoencoders
• Programming language: Python, PyTorch or Keras/Tensorflow

References:

• Yu, Fisher, and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015).
• Dai, Jifeng, et al. Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision. 2017.
• Cai, Zhaowei, et al. A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision. Springer, Cham, 2016.

SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.