Field Production Optimization Using Agent Based Simulation
Select Other "city & date"
Field Production Optimization Using Agent Based Simulation Course
Introduction:
As real-life systems become more complex, it is essential to enhance our understanding of them. In the oil and gas exploration, production, and transportation industries, recurring issues must be optimized to improve productivity and efficiency.
Companies strive to increase output and reduce costs, leading them to undertake numerous projects. However, this approach can sometimes result in system slowdowns, interruptions, or even project cancellations before they can progress successfully.
Course Objectives:
By attending this training course delegates will be able to make a substantial, positive impact on the Field Production Optimization best practices within their organization, more specifically:
- Provide a step-by-step guide to Agent-Based Simulation techniques and software available
- Understand the ways to simulate entire processes and update them using real-life data
- Learn Agent-based, system dynamic, and discrete event modeling
- Acquire the knowledge of Any Logic simulation software for process analysis and optimization
- Implementation of the optimum values of parameters in the production system
- Get the hands-on examples already implemented, their experiences and limitations
Who Should Attend?
This training course is suitable for professionals:
- Petroleum engineers
- Data Scientists
- Project managers
- Senior and Middle Managers
- Optimization professionals
- FEED engineers
- Board level executives and non-executives
- Consultants in Data Science, Optimization and Petroleum Engineering
Course Outlines:
Petroleum Production Engineering Fundamentals
- Petroleum Production System
- Properties of oil and gas
- Reservoir deliverability
- Wellbore performance
- Exercise: Forecast of good production
- Exercise: Production decline analysis
Optimization Techniques
- Linear Programming
- Non-linear programming
- Mixed-Integer Linear (MILP)
- Optimizing Controllable Rig Time Loss
- Introduction to simulation
- Introduction to Any Logic software
- Exercise: Using Any Logic software
Simulation Techniques and Process Modelling
- Discrete event modelling
- System dynamics modelling
- Agent-based models
- Multi-method modelling (combining all three methods in one simulation)
- Exercise: Creating a simple process in Any Logic
- Exercise: Creating an agent-based model in Any Logic
Oilfield Process Modelling
- Defining the process of oil production
- Data gathering
- Determination of data distributions
- Output data
- Introduction to Any Logic fluid library
- Exercise: Creating a simulation model with Any Logic fluid library
Model Analysis and Optimization
- Scenario analysis
- Output data measurement and comparison
- GIS connectivity
- Exercise: Review of the developed oil supply chain
- Exercise: Incorporating the worker performance into the models
- Steps to apply multi-method simulation in FEED engineering