Oil & Gas and Petroleum
Uncertainties and Geostatistics for Subsurface Managers
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Uncertainties and Geostatistics for Subsurface Managers Course
Introduction:
As the subsurface teams comprise a multitude of people and disciplines which are focused on managing production from the field and getting more oil and gas from the field itself, managing the team is an extremely difficult task that falls on the shoulders of Subsurface Managers.
Course Objectives:
By the end of this course, participants will learn to:
- Identify uncertainties and risks associated with E&P lifecycle
- Use statistical tools to make adequate decisions under uncertainty
- Learn which modelling techniques are used for different reservoir types
- Perform data analysis trough inference, identifying outliers, declustering, and trend analysis
- Perform Monte-Carlo simulation to determine oil and gas reserves
Who Should Attend?
This training course is suitable for a wide range of professionals but will greatly benefit:
- Subsurface Managers
- Production geologists
- Geophysicists
- Petrophysicists
- Reservoir engineers
- Production engineers
- Production chemists
- Well Engineers
- Economists
Course Outlines:
Statistical Analysis and Probability Theory
- Describing Data with Numbers
- Probability and Displaying Data with Graphs
- Random Variables, Probability Density Function (pdf)
- Expectation and Variance
- Bivariate Data Analysis
- Sample case: preparing a well log plot and identifying the correlation
Descriptive Geostatistics
- Geologic constraints
- Univariate distribution and Multi-variate distribution
- Gaussian random variables
- Random processes in function spaces
- Geostatistical Mapping Concepts
- Structural Modeling
- Cell-Based Facies Modeling
- Sample case: Analytical interpretation of centrifuge data to determine the relative permeability curve
Modeling Uncertainty
- Sources of Uncertainty
- Deterministic Modeling
- Models of Uncertainty
- Model and Data Relationship
- Model Verification and Model Complexity
- Sample case: Reservoir Modeling
- Creating Data Sets Using Models
- Parameterization of Subgrid Variability
Quantifying Uncertainty
- Introduction to Monte Carlo methods
- Sampling based on experimental design
- Gaussian simulation
- General sampling algorithms
- Simulation methods based on minimization
- Sample case: Monte Carlo method for determining oil and gas reserves
- Sample case: Multiwell systems calculation using Darcy’s law
Visualizing Uncertainty
- Distance Methods for Modeling Response Uncertainty
- K-means clustering
- Estimation using simple kriging
- Petrophysical Property Simulation
- Sample case: Oil reservoir uncertainty visualization
- Value of Information and the cost of data gathering
