Click on the buttons below to access this repo trhough Binder or Google Colab:
Instructors: Lalitha Venkataramanan (SLB), Andy McDonald (LR) & Vikas Jain (SLB)
This workshop will focus on the applications of Artificial Intelligence (AI) and Machine Learning (ML) to the upstream O&G industry. It will provide an introduction to machine learning, lay out sample workflows and steps for ML applications and summarize some of the use cases within the industry.
The workshop will cover both supervised and unsupervised learning and highlight applications such as QA/QC, outlier detection, facies mapping and learning complex functional mapping.
Hands-on tutorials with Python code to analyze a publicly available data set will also be provided. These can be found within this repository.
This repository contains a series of notebooks that will be used during the workshop.
There are multiple options for following along:
The first option is to clone the repository to a local folder and run using your own version of Python & Jupyter. If you do this, please make sure you install the required packages listed in requirements.txt. See here: https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository for details on how to clone a repository and here https://note.nkmk.me/en/python-pip-install-requirements/ for details on how to install the required packages
Once you have cloned the repository you can open it up in Jupyter Notebooks, Jupyter-Lab or VSCode.
If you are restricted from installing Python on your computer or are unable to load the required libraries, you can use the binder link https://mybinder.org/v2/gh/andymcdgeo/spwla_2022_machine_learning_workshop/HEAD. This will load up the repository allowing you to work through the course notebooks and interact with the code.
Alternatively, you can use Google Colab: https://colab.research.google.com/. once on the Google Colab page you will be asked to select the data source. On the yellow menu at the top select GitHub, and then copy and paste the URL of this repository into the textbox that says: "Enter a GitHub URL or search by organization or user"
Further details on using Google Colab with GitHub can be found here: https://medium.com/analytics-vidhya/how-to-use-google-colab-with-github-via-google-drive-68efb23a42d
Presentations and code can be downloaded from:
https://drive.google.com/drive/folders/120jGpHmXmA57YKiRPr0E6r_00zx9Ue8M?usp=sharing
Lalitha Venkataramanan is the Reservoir Performance: Data Science Advisor in Schlumberger. She is also a Scientific Advisor and the Associate Editor for NMR-Petrophysics. She is regional distinguished speaker for SPWLA for 2018-19. She is on the board of SIAM and NSERC as well as Business-Industry-Government Math network. Her current interests include machine learning, mathematical modeling and inversion, optimization, probability and stochastic processes. Trained as an Electrical Engineer, she obtained her M.S. and Ph.D. degrees from Yale University in 1998. She has co-authored 40+ peer-reviewed publications and has over 24 granted US patents and 18 pending patent applications.
Andy McDonald is a Petrophysicist with Lloyd’s Register in Aberdeen, Scotland, and provides domain expert knowledge in petrophysics, machine learning and python programming to the development of IP (Interactive Petrophysics). Prior to joining Lloyd’s Register, Andy worked within the geoscience group at Baker Hughes where he was the focal point for borehole acoustic waveform processing and interpretation, as well as petrophysics and well log data quality control. In 2021, he became a distinguished speaker for the SPWLA. He holds an MSc in Earth Science and a BSc (Hons) in Geology & Petroleum Geology. Andy has coauthored several technical papers for SPWLA and SPE conferences covering machine learning, heavy oil, and low salinity waterflooding. He also hosts his own YouTube channel covering applications of python development to petrophysical and geoscience data.
Vikas Jain is currently working in Schlumberger’s Analysis and Interpretation business as Principal Petrophysicist and Global Domain Head of Petrophysics and Acoustics, based out of Houston, U.S.A. His focal area is development of custom and intelligent solutions, leveraging machine learning and artificial intelligence, to help solve common upstream challenges. Previously he was managing Interpretation Engineering projects of NMR Answer Products and Next Generation Data-Driven Petrophysics in Schlumberger’s Engineering Center in Houston. He started his career with Schlumberger in 2001 as a field engineer. Since then, he has held positions in operations, sales, marketing, domain and engineering. He has authored more than 35 publications and 15 patent applications/grants.
In 2018, Equinor released the entire contents of the Volve Field to the public domain to foster research and learning. Data includes:
- Well Logs
- Petrophysical interpretaions
- Reports
- Core measurements
- Seismic data
- Models
- And more
The data is licensed under the Equinor Open Data Licence. The full licence agreement can be found here: https://www.equinor.com/content/dam/statoil/documents/what-we-do/Equinor-HRS-Terms-and-conditions-for-licence-to-data-Volve.pdf
The Volve Field is located some 200 km west of Stavanger in the Norwegian Sector of the North Sea. Hydrocarbons were discovered within the Jurassic aged Hugin Formation in 1993. Oil production began in 2008 and lasted for 8 years (twice as long as planned) until 2016, when production ceased. In total 63 MMBO were produced over the field's lifetime and reached a plateau of 56,000 B/D.
Details for the Volve Field and the entire dataset can be found at: https://www.equinor.com/en/what-we-do/norwegian-continental-shelf-platforms/volve.html