

Machine Learning Application for Well logging and Petrophysical Interpretation
Unlock the power of machine learning to enhance well logging and petrophysical interpretation. This course provides practical knowledge and hands-on experience to help industry professionals apply advanced data science techniques for more accurate reservoir characterization and decision-making.
Description
This course will provide all methodologies and
practical application of machine learning for prediction of well logging and
petrophysical properties.
A lot of wells in your field or basin don’t have
sonic, neutron or density and these logs are essential for well logging and
petrophysical interpretation so machine learning is the solution to predict
these logs for your petrophysical and geological studies.
Core data is always not acquired in all the wells, for
example permeability and irreducible water saturation are important parameters
for static modelling and petrophysical studies, machine learning will provide a
way to predict these logs in your field.
Machine learning became essential for any
petrophysical and reservoir characterization studies including rock typing,
saturation height modelling, rock physics, core log integration, and facies
analysis.
Demo Class
Introduction
Machine learning offers powerful tools for advancing traditional methods in well logging and petrophysical interpretation. In this course, participants will delve into the applications of machine learning within petrophysics, learning to process, analyze, and interpret well log data with advanced data science methods. Practical sessions and real-world case studies will support attendees in gaining essential skills for accurate reservoir characterization, predictive analysis, and decision-making in the oil and gas industry.
Objectives
·
Understand basic pythons.
·
Understand different machine learning
algorithms.
·
Use machine learning (regression) algorithms to
predict different petrophysical properties (permeability, FZI, irreducible
water saturation, cementation exponent and initial water saturation) by using
software, excel and python.
Training Methodology
This course utilizes a blend of interactive lectures, hands-on exercises, and case study discussions. Participants will engage in practical sessions, working with real datasets to reinforce theoretical knowledge. Collaborative learning and feedback from instructors will ensure a comprehensive understanding of machine learning applications in petrophysics.
Organisational Impact
Personal Impact
Who Should Attend?
Petrophysicists, reservoir engineers, geomodellers,
development and exploration geologists, well log and core analysts, well site
geologists, mud logging engineers, drilling engineers and completion engineers,
geoscience and engineering students and anyone working or interested to work in
oil and gas.
Day-1: Basic Python:
· Variables
(codes) (basic python)
· Integers
and float. basic python)
· Strings
(basic python)
·
Conditional and Booleans.
(basic python)
· Loops and iteration (basic python)
Day-2: Basic Python and Data importing:
· Functions
(basic python)
· Lists,
tuples and dictionary. (basic python)
· Juptyer
notebook
· Data
importing into software
· Git hub
Day-3: Numpy and pandas:
· Numpy
Creating Arrays (Numpy course)
· Numpy
flattening (Numpy course)
· Array
search numpy (Numpy course)
· Sorting
and splitting (Numpy course)
· Pandas
reading, reshaping data
· Describing
data.
· Handling
missing data
· Panda correlation
Day-4: Data preprocessing and model evaluation:
· Normalization
of data
· Splitting
data
· Plotting
data
· Sorting
and splitting of data.
· Gradient
descent
· Bias
and variance.
· Model
evaluation (mean square error and confusion matrix)
· K
fold cross validation
· Matplot lib codes
Day-5: Linear Machine learning models:
- Linear regression
- Multiple linear regression
- Practical prediction of permeability,
water saturation and FZI.
Day-6: Linear Machine learning models:
- Polynomial regression
- Logistic regression.
- Practical prediction of shear slowness and badhole flags.
Day-7: Support vector Machine:
- Support vector regression
- Support Vector classifier
- Add grid search, specificity and sensitivity.
- Practical prediction of permeability, compressional
slowness and rock typing.
Day-8: Tree based Machine learning:
- Decision tree.
- Random forest
- Gradient boosting
- Practical prediction of permeability, compressional
slowness and rock typing.
Day-9: Neural Network models:
- Neural network regression.
- Neural network classification.
- Practical prediction of permeability, initial water saturation and lithology.
Day-10: Self organizing maps and k mean clusters:
- Self-organizing maps classification for rock types.
- K mean clusters for rock types and lithology.
Day-11: Principal component analysis
Day-12: Petrophysical analysis with python
On successful completion of this training course, PEA Certificate will be awarded to the delegates
Osama Osman is a skilled petrophysicist with 15+ years of experience in the oil and gas industry.
He excels in analyzing reservoirs, interpreting well logs, and applying machine learning to optimize production.
Osama has a proven track record of success in various roles, from Senior Petrophysicist at SLB to consultant and researcher.
He's a problem-solver who combines traditional techniques with cutting-edge technology to maximize reservoir understanding.