course | Advanced Petrophysics Integration of Core, Log Data and Machine Learning for Defining and Predicting
Looking to excel in petrophysics integration core log data? Advanced Petrophysics Integration of Core, Log Data and Machine Learning for Defining and
OGC-1284 | Advanced Petrophysics Integration of Core, Log Data and Machine Learning for Defining and Predicting
Course Sector : Oil, Gas and Chemical
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 21/09/2026 | 25/09/2026 | London | $4,950 | Book now |
| 5 Days | 28/12/2026 | 01/01/2027 | Dubai | $4,250 | Book now |
| 5 Days | 15/03/2027 | 19/03/2027 | Dubai | $4,250 | Book now |
| 5 Days | 13/06/2027 | 17/06/2027 | Al Khobar | $4,250 | Book now |
Course Introduction
This course is essential for reservoir engineers, reservoir geologists, and Petrophysicists dealing with reservoir modeling. The course will provide the different techniques to establish hydraulic flow units from core data. Also, it will discuss the different methods to predict the defined HFU for uncored wells and intervals. Then define the saturation height function (SHF) for different HFUs, either on capillary-based or on log-based.
Course objective
- Provide the different techniques to establish hydraulic flow units from core data: RQI, Winland R35, and Pittman.
- Discuss and practice the different methods to propagate the defined HFU for uncored wells and intervals. NN, Fuzzy Logic, MRL, SOM, Cluster Analysis, PCA, Contingency Tables.
- Establish the saturation height function (SHF) for different HFUs, either on capillary or log-based, with reconciliation with logs.
Course audience
- Reservoir Engineers
- Reservoir Geologists
- and Petrophysicists.
Course Outline | 01 Day One
Data Preparation and QC
- Data Available Loading
- Log Data Editing and Create Flags
- Pc Data QC and Corrections
- Data Visualization
Course Outline | 02 Day Two
Generate HFU
- RQI
- Winland R35
- Pittman
Course Outline | 03 Day Three
Predicate HFU & Permeability
- Principal Component Analysis
- Multiple Linear Regression
- Fuzzy Logic
- Cluster Analysis
- Self-Organizing Maps
- Neural Network
- Contingency Table
Course Outline | 04 Day Four
Generate Saturation Height Modeling
- Fitting and Smoothing
- Averaging
- Reconciliation with Log Data
Course Outline | 05 Day Five
Group discussion and Exercises using Software
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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