Uncertainties and Geostatistics for Subsurface Managers
| Start Date | End Date | Venue | Fees (US $) | ||
|---|---|---|---|---|---|
| Uncertainties and Geostatistics for Subsurface Managers | 02 Aug 2026 | 06 Aug 2026 | Dubai, UAE | $ 3,900 | Register |
| Uncertainties and Geostatistics for Subsurface Managers | 15 Nov 2026 | 19 Nov 2026 | Istanbul, Turkey | $ 4,500 | Register |
Uncertainties and Geostatistics for Subsurface Managers
| Start Date | End Date | Venue | Fees (US $) | |
|---|---|---|---|---|
| Uncertainties and Geostatistics for Subsurface Managers | 02 Aug 2026 | 06 Aug 2026 | Dubai, UAE | $ 3,900 |
| Uncertainties and Geostatistics for Subsurface Managers | 15 Nov 2026 | 19 Nov 2026 | Istanbul, Turkey | $ 4,500 |
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.
This task is additionally complicated with the uncertainties and risks associated with geology, petrophysics, and economic issues. This training course is designed to help subsurface managers tackle the risks and uncertainties related primarily to geoscience, and it will also cover the wider area, as the subsurface team consists of: Production geologist, Geophysicist, Petrophysicist, Reservoir engineer, Production engineer, Production chemist, Well Engineer and Economist. Therefore, the full understanding of risk and uncertainties related to these fields will be taken into consideration and presented for the Subsurface Managers to be able to communicate with the members of their teams and find the optimal compromise between them.
This training course will highlight:
- Uncertainties and risks associated with the oil and gas field development,
- Geostatistics and Quantification of Uncertainty and Risk Qualified Decision Making
- Issues and optimization of subsurface data management and Life-cycle uncertainty management
- Analytical interpretation of centrifuge data to determine the relative permeability curve
- Construction of Q-Q plots, semivariograms, kriging, and uncertainty modelling
Objectives
- 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
By the end of this course, participants will learn to:
Training Methodology
The course is delivered in a combination of lecture-style and computer-based training. In addition, a significant amount of time is set aside for small working group activity when addressing case study problems. Extensive use is made of case study material to underline the key aspects of the course and to give the delegates exposure to current best practice.
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 Outline
Day One: Statistical Analysis and Probability Theory
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Describing Data with Numbers
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Probability and Displaying Data with Graphs
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Random Variables, Probability Density Function (pdf)
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Expectation and Variance
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Bivariate Data Analysis
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Sample case: preparing a well log plot and identifying the correlation
Day Two: Descriptive Geostatistics
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Geologic constraints
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Univariate distribution and Multi-variate distribution
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Gaussian random variables
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Random processes in function spaces
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Geostatistical Mapping Concepts
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Structural Modeling
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Cell-Based Facies Modeling
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Sample case: Analytical interpretation of centrifuge data to determine the relative permeability curve
Day Three: Modeling Uncertainty
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Sources of Uncertainty
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Deterministic Modeling
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Models of Uncertainty
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Model and Data Relationship
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Model Verification and Model Complexity
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Sample case: Reservoir Modeling
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Creating Data Sets Using Models
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Parameterization of Subgrid Variability
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Day Four: Quantifying Uncertainty
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Introduction to Monte Carlo methods
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Sampling based on experimental design
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Gaussian simulation
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General sampling algorithms
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Simulation methods based on minimization
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Sample case: Monte Carlo method for determining oil and gas reserves
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Sample case: Multiwell systems calculation using Darcy’s law
Day Five: Visualizing Uncertainty
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Distance Methods for Modeling Response Uncertainty
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K-means clustering
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Estimation using simple kriging
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Petrophysical Property Simulation
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Sample case: Oil reservoir uncertainty visualization
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Value of Information and the cost of data gathering

