The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Management
- Data Science & Engineering Analytics
Filters
Journal
- Loading...
Conference
- Loading...
Publisher
- Loading...
Theme
- Loading...
Author
- Loading...
Concept Tag
- Loading...
Source
- Loading...
File Type
- Loading...
Site News
Goto
No map tile layer
| Layer | Fill | Outline |
|---|
Map layers
| Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
|---|
| Fill | Stroke |
|---|---|
Collaborating Authors
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.
artificial lift dr, artificial lift system, asia government, asset and portfolio management, data Science, discipline, engineering analytic production, foundation, gas injection, gas lift, India government, investment, operation artificial lift, operation data analytic, Overview, participant, rajan chokshi, software, trainee, Upstream Oil & Gas, workflow
SPE Disciplines:
This course is designed to give trainees an overview of various artificial lift solutions and related production optimization concepts. After introducing participants to the need for an artificial lift system, training will focus on each of the following lift methods: Gas lift, Reciprocating Rod Lift, Progressing Cavity Pumping, Hydraulic Pumping, Electrical Submersible Pumping, Plunger and Capillary System. For each lift type, the course covers main components, application envelope, relative strengths and weaknesses. Animations, field cases, and example-calculations are used to reinforce concepts. A unique feature of this course is discussion on digital oil field as applicable to lift optimization.
artificial lift dr, artificial lift system, asia government, data Science, discipline, engineering analytic production, India government, Interdependence, operation data analytic, operation full field optimization, optimization, participant, progressing cavity pump, pumping, rajan chokshi, reservoir, Upstream Oil & Gas
SPE Disciplines: Production and Well Operations > Artificial Lift Systems > Progressing cavity pumps (0.91)
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.
Artificial Intelligence, artificial lift solution, artificial lift system, asia government, cloud computing, data analysis technique, data mining, different data analysis technique, India government, operation artificial lift, operation full field optimization, participant, production optimization concept, rajan chokshi, significant interdependence, surface installation, technology awareness, Upstream Oil & Gas, Use case
SPE Disciplines:
Technology:
- Information Technology > Data Science (1.00)
- Information Technology > Artificial Intelligence (1.00)
- Information Technology > Cloud Computing (0.71)
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.
ai-based reservoir simulation, analytic, Artificial Intelligence, completion data science, data mining, data Science, data-driven reservoir modeling, discipline, Engineering application, machine learning, numerical reservoir simulation, petroleum data analytic, reservoir modeling, reservoir simulation, Upstream Oil & Gas
SPE Disciplines:
Technology:
Artificial Intelligence and Machine Learning is revolutionizing many industries. This technology is becoming an important point of competitive differentiation in the upstream oil and gas industry. Since optimization of production and enhanced recovery is the important issue for the petroleum industry, companies are realizing that reality and actual field measurements play much more important role in success of decision making than traditional assumptions, interpretations, and preconceived notions. "Data" representing the actual field measurements, can provide much needed insight. Petroleum Data Analytics provides a set of tools and techniques for both conventional and unconventional resources to extract patterns and trends from data and construct predictive models to assist decision making and optimization.
ai-based reservoir simulation, analytic, application, Artificial Intelligence, completion data science, data Science, data-driven reservoir modeling, discipline, Engineering application, machine learning, modeling, neural network, numerical reservoir simulation, petroleum data analytic, reservoir modeling, reservoir simulation, Upstream Oil & Gas
SPE Disciplines:
Technology:
Save or update focal point
Save a search / focal point to access it again.
Thank you!