|PB119||18 - 22 Oct 2020||Dubai - UAE||4,950|
|PB119||11 - 15 Apr 2021||Dubai - UAE||4,950|
|PB119||17 - 21 Oct 2021||Dubai - UAE||4,950|
*All fees are exclusive of VAT
Why Choose this Training Course?
As the subsurface teams comprise of 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 extremely difficult task that falls on the shoulders of Subsurface Managers.
This task is additionally complicated with the uncertainties and risks associated with the geology, petro physics and economic issues. This PetroKnowledge 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 PetroKnowledge 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, semi variograms, kriging, and uncertainty modelling
What are the Goals?
By the end of this PetroKnowledge training 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 is this Training Course for?
This 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 training course is suitable to a wide range of professionals but will greatly benefit:
- Subsurface Managers
- Production geologists
- Reservoir engineers
- Production engineers
- Production chemists
- Well Engineers
How will this Training Course be Presented?
Participants to this 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.
The main problem of every upstream oil and gas company is handling uncertainty and risk associated with the oil and gas Exploration, Recovery and Production projects as they require high capital expenditure and return on investment can come only after five to ten years.
Therefore, adequate knowledge of concepts, methods and models for determining oil and gas reserves, adequate interpretation of well logs and adequate calculation of pre-drill GRV (Gross Rock Volume) / column prediction are all prerequisites for successful and sustainable operations in upstream hydrocarbon production with volatile oil and gas prices environment.
Managing highly complex Subsurface Teams, delivering on volume promise and identifying options to increase production from the field is the primary role of Subsurface Managers, this highly complex role is dependent on the knowledge of the risk and uncertainties as well as how to identify, calculate and mitigate them.
Resource estimation plays a primary role in the decision to explore or develop a hydrocarbon prospect. The Subsurface team needs to know favorable sites for locating a well, likelihood of success, expected level of production, and the average or cumulative production across an area such as a lease block.
Making decisions under uncertainty is a day-to-day job for the Subsurface managers, and therefore learning how to analyze the data, identify correlation and probabilities is the basis of scientific decision making. This course is designed to provide this kind of knowledge to the Subsurface Managers and the people who would wish to become Subsurface Managers.
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