Unit Presentation:
Video: https://screencast-o-matic.com/watch/cqVZht3OAh
PDF (contains links to readings, etc.)
http://zenzebra.net/risk/risk-management-nash-pt3.pdf
Scenario 3: Predicting Risk: Approaches using Artificial Intelligence and Machine Learning
Julia, Patricio, and Reyna are part of a team that is tasked with classifying old shallow-water offshore wells in the Gulf of Mexico in new ways that will help them develop a plan to boost production.
They feel very fortunate in that around a million geological and production records have been scanned, and they cover the 150 or so wells in the field. It’s a treasure trove of data, and they want to incorporate it with the new data in order to develop a profile of the best wells, as well as the good, mediocre, and underperforming wells.
Your Task: Help Julia, Patricio, and Reyna develop a plan to analyze the data, and then help them determine where, when, and how they can use artificial intelligence and machine learning to create profiles.
Here are a few things to consider:
How will you select the data to use?
How will you organize it?
What does it mean for a well to be:
Excellent
Good
Mediocre
Bad
What are the attributes or clusters of characteristics you’ll use?
What approach will you use to select data?
To clean the data?
To analyze the data?
What kind of AI / ML approach will you use?
How will you use the results?
Readings:
Overview thoughts / concepts
Lists of uses of AI / Machine Learning the energy industry
Upstream
Classify wells using your own unique set of criteria
Identify high-value (or potential high-value) blocks
Midstream
Classify infrastructure (pipelines, etc) with your own criteria
Predict overall performance and the location of bottlenecks
Downstream
Refining
Retail / distribution
Wind energy Identify high-value, high-return new locations
Identify small businesses that would benefit from local energy
Solar energy
Workflow for machine learning (in general)
● Pinpoint the problem you want to solve.
● Identify the data you’ll need to use
● Collect the data
● Clean the data
● Organize your data (put into a model - if structured, may use Open Source models such as those from Apache HaDoop)
● Find a model
● Develop algorithms (May use repositories and also cloud-based interfaces)
● Train the model
● Test with data sets
● Reality check
● Decision points
How do I clean data?
What is “dirty” data?
Does not make sense
Bad labels
Incorrect formatting
Too many “nulls”
Part of the data in a different order or different columns
Brendon Bailey’s Guide: Use Excel or Python to Clean Data?
Use Excel if: You have fewer than 1 million records
You need to do the job quick and easy
There is a logical pattern to cleaning the data and it’s easy enough to clean using Excel functions
The logical pattern to cleaning the data is hard to define, and you need to clean the data manually
When you might use Python or another scripting language:
Use Python if: You need to document your process
You plan on doing the job on a repeat basis
There is a logical pattern to cleaning the data, but it is hard to implement with Excel functions
Brendon Bailey. “Data Cleaning 101” TowardDataScience.com
https://towardsdatascience.com/data-cleaning-101-948d22a92e4
Where do you keep the data?
cloud solutions (Google, Amazon Web Services (AWS))
Software for risk analytics (free / open source):
Spotfire (http://www.spotfire.com)
Qlik.com (free Spotfire alternative, Qlik.com)
Jupyter Notebook https://jupyter.org/
iPython
R
C++
Julia
A Gallery of interesting Jupyter Notebooks (ready to share)
https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks
How do we predict where and when high-risk situations may take place?
Analyze data
Probabilistic analysis (Spotfire, etc.)
Using geospatial elements
What is the ideal combination of variable or factors to tell us when / where / how conditions are ideal for a) optimization; b) an accident or problem ?
Use multivariate analysis
Bring together all risk factors: geological, logistical, political, economic, legal, environmental, etc.
Weight them by importance (assign a percentage)
https://www.kinetica.com/wp-content/uploads/2017/09/OilGas_jt1.0mn.pdf https://medium.com/syncedreview/how-ai-can-help-the-oil-industry-b853dda86be6
Learn and Use Machine Learning
Tensorflow: https://www.tensorflow.org/tutorials/keras/
Tensorflow Machine Learning Cookbook: https://github.com/nfmcclure/tensorflow_cookbook
AI and Probabilistic Models
Part I
https://medium.com/tensorflow/industrial-ai-bhges-physics-based-probabilistic-deep-learning-using-tensorflow-probability-5f215c791863
Part II
https://medium.com/tensorflow/predicting-known-unknowns-with-tensorflow-probability-industrial-ai-part-2-2fbd3522ebda
Bougher, Benjamin Bryan. (2016) Machine Learning Applications to Geophysical Data Analysis. Open Collections. University of British Columbia.
https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0308786
Bougher, Ben B. (2016) Using the scattering transform to predict stratigraphic units from well logs. Seismic Laboratory for Imaging and Modeling (SLIM), The University of British Columbia, Vancouver
https://www.slim.eos.ubc.ca/Publications/Public/Journals/CSEGRecorder/2016/bougher2015CSEGust/bougher2015CSEGust.html
Data: Trenton Black River gamma ray logs
Methodology: supervised learning ("uses labelled datasets to train a classifier to make predictions about future data" (Bougher, 2016))
Methodology - what's the algorithm? Bougher uses a scattering transform - and then it fieeds a K-Nearest Neighbours (KNN) classifier).
How can I do this?
Using convolutional neural networks to solve a mineral prospectivity mapping problem
Framing the exploration task as a supervised learning problem, the geological, geochemical and geophysical information can be used as training data, and known mineral occurrences can be used as training labels. The goal is to parameterize the complex relationships between the data and the labels such that mineral potential can be estimated in under-explored regions using available geoscience data.
Granek, Justin. (2016). Application of Machine Learning Algorithms to Mineral Prospectivity Mapping. Open Collections. University of British Columbia.
https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0340340
TAMUT MBA in Energy Leadership: Click link to apply - more information
For more information about the courses (and this full course), please contact me.
