About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations
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Python Unittest Course Summary. Testing plays a major role in software development. This course will explain Python Unit Testing using the unittest built-in module. We'll cover issues before going to the production itself and the basics of testing in Python. Location: Instructor-led, Online. Our Style: Hands-on, Practical Course. Group Size: Max 4 people per group. Qualification: PCWorkshops Unittest Certification Duration: 1-Day, 10am-5pm Unittest topics UnitTest Framework - Home UnitTest Framework - Overview UnitTest - Framework UnitTest - API UnitTest - Assertion UnitTest - Test Discovery UnitTest - Skip Test UnitTest - Exceptions Test UnitTest - Time Test UnitTest - Unittest2 UnitTest - Signal Handling UnitTest - Doctest UnitTest - Doctest API UnitTest - Py.test Module Nose Testing - Framework Nose Testing - Tools Included with Python Unittest Course Python Unittest Certificate on completion Python Unittest Videos Python Unittest Notes Python Unittest Examples Practical Python Unittest exercises
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About this Virtual Instructor Led Training (VILT) Electrification of the transportation sector will impact the power system in several ways. Besides the additional load, local impact on the grid needs to be managed by the grid operators. Simultaneously charging of many electric vehicles (EVs) might exceed the limits in specific locations. On the other hand, EVs can provide flexibility and other ancillary services that will help grid operators. This 3 half-day VILT course will provide a complete overview of integrating electric vehicles (EVs) into the power grid. It will cover the whole value chain from grid operations to the car battery. This includes the control room, possible grid reinforcement, demand side management and power electronics. This course will demonstrate the impact on the grid and solutions for a safe & cost-effective grid plan and operation, with examples of successful integration of EVs. The course will also provide vital knowledge about technology used for EVs such as power electronics, demand side management, communication and batteries. In this context, the focus will be on power electronics as it has the highest impact on the grid. The grid planning tool, pandapower, is introduced as an open source tool for power system modelling. The set-up of the training course allows for discussion and questions. Questions can be formulated by the participants upfront or during the training. This course is delivered in partnership with Fraunhofer IEE. Training Objectives At the end of this course, the participants will: Understand the charging options for EVs and its impact on the grid and batteries Identify system services for EVs with regards to voltage quality at the point of common coupling Discover what are the 'grid friendly' and grid supporting functions in EVs Uncover the different applications, standards and data researched on EVs Examine the application of a grid planning tool (pandapower) for power system modelling Be able to develop code snippets with pandapower Apply and execute a code example for power system modelling with pandapower Target Audience EV and grid project developers and administrators Power grid operators and planners EPC organisations involved in grid development EV/ battery manufacturers and designers EV transport planners and designers Government regulators and policy makers Training Methods The VILT will be delivered online in 3 half-day sessions comprising 4 hours per day, with 2 x 10 minutes breaks per day, including time for lectures, discussion, quizzes and short interactive exercises. Additionally, some self-study will be requested. Participants are invited but not obliged to bring a short presentation (10 mins max) on a practical problem they encountered in their work. This will then be explained and discussed during the VILT. A short test or quiz will be held at the end of every session/day. Trainer Our first course expert is Head of Department Converters and Electrical Drive Systems at Fraunhofer IEE and Professor for Electromobility and Electrical Infrastructure at Bonn-Rhein-Sieg University of Applied Sciences. He received his engineering degree in automation in 2008 by the THM Technische Hochschule Mittelhessen (FH Giessen-Friedberg). Afterwards he studied power engineering at University of Kassel and received his diploma certificate in 2010. In 2016 he received the Ph.D. (Dr.-Ing.) from the University of Hannover. The title of his dissertation is Optimized multifunctional bi-directional charger for electric vehicles. He has been a researcher at the Fraunhofer IEE in Kassel since 2010 and deals with power converters for electric vehicles, photovoltaics and wind energy. His current research interests include the bidirectional inductive power transfer, battery charger and inverter as well as new power electronic components such as SiC MOSFETs and chokes. Additionally, our key expert is Chairman of the IEEE Joint IAS/PELS/IES German Chapter and a member of the International Scientific Committee of the EPE Association. Our second course expert is deputy head of energy storage department at Fraunhofer IEE. Prior to this he was the Director of Grid Integration department at SMA Solar Technology AG, one of the world's largest manufacturers of PV power converters. Before joining SMA, our course expert was manager of the Front Office System Planning at Amprion GmbH (formerly RWE TSO), one of the four German transmission system operators. He holds a degree of electrical engineering of the University of Kassel, Germany. In 2003 he finished his Ph.D. (Dr.-Ing.) on the topic of wind power forecasting at the 'Institute of Solar Energy Supply Technology' (now Fraunhofer IEE) in Kassel. In 2004 he started his career at RWE TSO with main focus on wind power integration and congestion management. Our course expert is chairman of the IEC SC 8A 'Grid Integration of Large-capacity Renewable Energy (RE) Generation' and has published several papers about grid integration of renewable energy source and forecasting systems on books, magazines, international conferences and workshops. Our third course expert is Research Associate at Fraunhofer IEE. He is actively working on different projects related to the integration of electric vehicle charging into the electric distribution grid. The focus of this work concerns time series based simulations for grid planning and operation in order to investigate the effect of a future rollout of electric vehicles and charging infrastructure on economics e.g. costs for grid reinforcement. He completed his master degree (MSc.) in Business Administration and Engineering: Electrical Power Engineering at RWTH Aachen University, Germany. Our trainers are experts from Fraunhofer Institute for Energy Economics and Energy System Technology (Fraunhofer, IEE), Germany. The Fraunhofer IEE researches for the national and international transformation of energy supply systems POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations