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.

·        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.

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.

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