The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Management
- Data Science & Engineering Analytics
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The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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For assets having gas-lift as a predominant form of artificial lift, the full field optimization is essential due to the closed-loop nature of the system with significant interdependence amongst reservoir, wellbore and surface installation. This course will review full-field optimization concepts applicable to the gas lifted wells from subsurface to the surface. The participants will understand workflows of solving entire system using commercial software tools. Gas lift is the most forgiving lift method in that it continues to work over a wide suboptimal operating range without showing any symptoms of malfunctions. Single well-based optimization practices help improve the situation, but the entire field-based approaches are essential for improved production performance.
This course will help develop a solid foundation in all forms of lift and the concepts of the selection process to maximize production and return on investment. Half Day or 1 Day (The course length may be adjusted to meet the learning level of the target audience.) This class helps ensure a broad view of artificial lift, particularly when in-house expertise is limited to one-or two-lift systems. This course is for production and field operations engineers, junior and senior petroleum engineers and field technicians as well as geoscientists and reservoir engineers who wish to understand the implications of production systems on their field reservoirs. CEUs (Continuing Education Units) are awarded for this half-day or 1-day course.
In this hands-on course, the participants will learn some of the techniques and workflows applied to artificial lift and production while reviewing code and practicing. The focus will be on the development of data-driven models while reviewing the underlying artificial lift principles. After introducing data science and analytics techniques, the course will discuss some business use cases that are amenable to data-driven workflows. Two or three problems will be presented during the training. For each case there will be a demonstration of the solution of such a problem using a data analysis technique with Python code deployed in the Google-cloud.
Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of researchers and practitioners have concentrated their efforts on providing the next generation of tools that incorporates these technologies for the petroleum industry. Data-driven analytics have become an integrated part of many new technologies used in our daily lives such as smart automatic transmissions in cars, the detection of explosives within airport security systems, smooth rides in complex subway systems, and the prevention of fraud in credit card use. They are extensively used to predict chaotic stock market behavior, and are increasingly being used to optimize financial portfolios. A large amount of data is routinely collected during production operations in shale assets. The collected data can be utilized to gain a competitive advantage in optimizing production and increasing recovery.
This course will play a crucial role for the enthusiasts of engineering application of Artificial Intelligence and Machine Learning technology in Reservoir Simulation and Modeling. It covers the scientific and realities foundation of Artificial Intelligence and Machine Learning and its true application in Reservoir Engineering. If you are interested to be knowledgeable with the most up-to-date and accurate AI and Machine Learning technology? This class will get you there!