Managing subsurface teams, which consist of individuals from various disciplines focused on maximizing oil and gas production, is a challenging responsibility that falls to Subsurface Managers. The task becomes even more complex due to the uncertainties and risks tied to geology, petrophysics, and economic factors.
This Energy training course is designed to assist subsurface managers in addressing the risks and uncertainties primarily linked to geoscience. It also broadens the scope, considering the diverse members of the subsurface team, including Production Geologists, Geophysicists, Petrophysicists, Reservoir Engineers, Production Engineers, Production Chemists, Well Engineers, and Economists. The course aims to provide a comprehensive understanding of the risks and uncertainties in these areas, enabling managers to effectively communicate with team members and make optimal decisions despite conflicting viewpoints.
This Uncertainties & Geostatistics for Subsurface Managers training course will focus on:
- Identifying the risks and uncertainties in oil and gas field development
- Utilizing geostatistics to quantify uncertainty and risk for informed decision-making
- Addressing the challenges of subsurface data management and lifecycle uncertainty
- Interpreting centrifuge data to derive relative permeability curves
- Constructing Q-Q plots, semivariograms, kriging, and uncertainty modeling techniques for risk analysis
By the conclusion of this Uncertainties & Geostatistics for Subsurface Managers training course, participants will be able to:
- Recognize the uncertainties and risks involved throughout the exploration and production lifecycle
- Apply statistical tools to make well-informed decisions in uncertain conditions
- Understand the appropriate modeling techniques for various reservoir types
- Conduct data analysis through methods such as inference, outlier detection, declustering, and trend analysis
- Execute Monte Carlo simulations to estimate oil and gas reserves
Participants to this Energy training course will receive a thorough training on the subjects covered by the seminar outline with the Tutor utilising a variety of proven adult learning teaching and facilitation techniques. Seminar methodology includes presentation of theoretical concepts, video lectures, example case studies and many exercises that will be done through the guided work of the delegates themselves.
A key challenge for upstream oil and gas companies is managing the uncertainty and risk inherent in exploration, recovery, and production projects. These ventures often require significant capital investment, with returns typically realized only after five to ten years.
To ensure successful and sustainable operations in the volatile oil and gas market, it is essential to have a solid understanding of concepts, methods, and models for assessing oil and gas reserves. Additionally, proper interpretation of well logs and accurate calculation of pre-drill Gross Rock Volume (GRV) and column predictions are critical for effective decision-making in upstream hydrocarbon production
Subsurface Managers are responsible for overseeing complex teams, meeting production targets, and finding ways to boost field production. Their role heavily relies on understanding the risks and uncertainties associated with the subsurface, and knowing how to identify, assess, and mitigate them.
Accurate resource estimation is crucial in deciding whether to explore or develop a hydrocarbon prospect. The Subsurface team must evaluate the best locations for drilling, the chances of success, anticipated production levels, and the overall production potential across a specific area, such as a lease block.
Making informed decisions amidst uncertainty is a routine task for Subsurface Managers. Therefore, learning how to analyze data, recognize patterns, and calculate probabilities is central to their decision-making process. This course is specifically designed to equip current and aspiring Subsurface Managers with the necessary knowledge to navigate these challenges.
This Energy training course is designed for all professionals working in the field of data analysis, oil and gas exploration, geology and reservoir modelling. But the main focus is on Subsurface Managers, or the people trying to become Surface Managers and effectively manage Subsurface Teams.
This Uncertainties & Geostatistics for Subsurface Managers training course is suitable to a wide range of professionals but will greatly benefit:
- Subsurface Managers
- Production Geologists
- Geophysicists
- Petrophysicists
- Reservoir Engineers
- Production Engineers
- Production Chemists
- Well Engineers
- Economists
Day One: 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 correlation
Day Two: 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
Day Three: 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 Sub grid Variability
Day Four: 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
Day Five: 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