Reservoir Engenerrign with Petrel And Python
Analyze data from Reservoir , make forcasting of physical parameters behaviour , underline behaviour and economical
What you'll learn
⚡ Theory of Fluid of Pressure
⚡ Production Mechanisms
⚡ Hydrocarbon Phase beahaviout
⚡ Hydrocarbon phase behaviour continue
⚡ Prediction Porosity Change in Time with Petrel
Requirements
❗ A foundational understanding of general reservoir engineering concepts and basic familiarity with Python programming.
Description
Modern reservoir management demands both deep domain expertise and computational power. This comprehensive course bridges classical reservoir engineering with advanced data science, equipping participants to make faster, more reliable subsurface decisions. By combining Schlumberger’sPetrel platform with custom Python scripting, you will learn to automate complex workflows, characterize reservoir dynamics, and build integrated predictive models.
Reservoir Data Analysis & Petrel Integration: Extract, clean, and visualize complex subsurface datasets. Learn to interface Petrel with Python data libraries (Pandas, NumPy, and SciPy) to rapidly process well logs, core data, and fluid properties across large fields.
Parameter Forecasting & Predictive Modeling: Move from static descriptions to dynamic forecasts. Develop physics-governed and data-driven models to predict critical physical parameters, including pressure depletion, fluid saturation shifts, and production decline rates.
Underlying Reservoir Behavior: Evaluate the fundamental mechanisms governing fluid flow, drive energy, and sweep efficiency. Analyze how structural heterogeneity, faults, and petrophysical variations influence long-term field behavior under primary, secondary, and tertiary recovery modes.
Economic Evaluation & Risk Analysis: Connect physical reservoir forecasts directly to financial performance. Use Python to build automated economic frameworks that perform net present value (NPV) calculations, cash flow modeling, sensitivity analyses, and capital risk assessments across fluctuating energy markets.
This course is designed forReservoir Engineers, Petroleum Geologists, Production Engineers, and Energy Data Scientists seeking to enhance their subsurface workflows by combining industry-standard geological software with python-based computational modeling
Who this course is for
⭐ Reservoir Engineers,
⭐ Petroleum Geologists
⭐ Production Engineers,
⭐ Energy Data Scientists
Published 8/2026
Created by Matteo Mirabilio
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 9 Lectures ( 8h 30m ) | Size: 4.1 GB
Download
*
Analyze data from Reservoir , make forcasting of physical parameters behaviour , underline behaviour and economical
What you'll learn
⚡ Theory of Fluid of Pressure
⚡ Production Mechanisms
⚡ Hydrocarbon Phase beahaviout
⚡ Hydrocarbon phase behaviour continue
⚡ Prediction Porosity Change in Time with Petrel
Requirements
❗ A foundational understanding of general reservoir engineering concepts and basic familiarity with Python programming.
Description
Modern reservoir management demands both deep domain expertise and computational power. This comprehensive course bridges classical reservoir engineering with advanced data science, equipping participants to make faster, more reliable subsurface decisions. By combining Schlumberger’sPetrel platform with custom Python scripting, you will learn to automate complex workflows, characterize reservoir dynamics, and build integrated predictive models.
Reservoir Data Analysis & Petrel Integration: Extract, clean, and visualize complex subsurface datasets. Learn to interface Petrel with Python data libraries (Pandas, NumPy, and SciPy) to rapidly process well logs, core data, and fluid properties across large fields.
Parameter Forecasting & Predictive Modeling: Move from static descriptions to dynamic forecasts. Develop physics-governed and data-driven models to predict critical physical parameters, including pressure depletion, fluid saturation shifts, and production decline rates.
Underlying Reservoir Behavior: Evaluate the fundamental mechanisms governing fluid flow, drive energy, and sweep efficiency. Analyze how structural heterogeneity, faults, and petrophysical variations influence long-term field behavior under primary, secondary, and tertiary recovery modes.
Economic Evaluation & Risk Analysis: Connect physical reservoir forecasts directly to financial performance. Use Python to build automated economic frameworks that perform net present value (NPV) calculations, cash flow modeling, sensitivity analyses, and capital risk assessments across fluctuating energy markets.
This course is designed forReservoir Engineers, Petroleum Geologists, Production Engineers, and Energy Data Scientists seeking to enhance their subsurface workflows by combining industry-standard geological software with python-based computational modeling
Who this course is for
⭐ Reservoir Engineers,
⭐ Petroleum Geologists
⭐ Production Engineers,
⭐ Energy Data Scientists
Published 8/2026
Created by Matteo Mirabilio
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 9 Lectures ( 8h 30m ) | Size: 4.1 GB
Download
*