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Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at people in senior roles including CIOs, chief digital officers and other aspiring CxOs, as well as consultants and others involved in digital transformations, service delivery and strategic delivery. Overview By the end of this course, you will understand: The internal and external factors to consider while crafting digital strategy How IT strategy differs from digital strategy and how they can be integrated Creating a digital strategy that achieves the most value from digital Implementing and sustaining digital strategy Developing and nurturing digital capabilities for continual business innovation and value co-creation This course takes you on a digital strategy journey. Its iterative, eight-step model moves from ?vision? through to ?actions? and is about creating sustainable, digital momentum. You experience the four key capabilities to develop a holistic, digital capability framework: digital leadership, managing innovation and emerging technologies, risk management and structuring a digital enterprise. This class includes an exam voucher. Prerequisites Delegates attending this course must have successfully achieved the ITIL 4 Foundation Qualification; your certificate must be presented as documentary evidence to gain admission to this course. Although there is no mandatory requirement, ideally candidates should have at least two years professional experience working in IT Service Management. 1 - ITIL GUIDING PRINCIPLES TO ALL ASPECTS OF DIGITAL AND IT STRATEGY Focus on Value Start Where You Are Progress Iteratively with Feedback Collaborate and Promote Visibility Think and Work Holistically Keep It Simple and Practical Optimise and Automate 2 - LEVERAGE DIGITAL STRATEGY TO REACT TO DIGITAL DISRUPTION Digital Technology Digital Business Digital Organisation Digitisation Digital Transformation Business Strategy and Business Models Digital and IT Strategy Products Services Relationship Between Digital, IT Strategy and Components of ITIL SVS. 3 - RELATIONSHIP BETWEEN CONCEPTS OF DIGITAL AND IT STRATEGY, SERVICE VALUE SYSTEM AND SERVICE VALUE CHAIN Environmental Analysis External Analysis: PESTLE Internal Analysis: Four Dimensions of Service Management 4 - HOW AN ORGANISATION USES DIGITAL AND IT STRATEGY TO REMAIN VIABLE IN ENVIRONMENTS How an Organisation?s Viability is Related to Agile, Resilient, Lean, Continuous and Co-Creational it is How to Analyse the VUCA Factors and Address them in a Digital and IT Strategy Organisation?s Position in a Particular Market or Industry Digital Positioning Tool to Determine Appropriate Position for a Digital Organization 5 - EXPLAIN AND COMPARE THREE LEVELS OF DIGITAL DISRUPTION Ecosystem Industry/Market Organisational Influenced factors Achieving Customer/Market Relevance Achieving Operational Excellence Internal and External Focus Balanced Approach 6 - STRATEGIC APPROACHES BY DIGITAL AND IT TO ACHIEVE CUSTOMER/MARKET RELEVANCE AND OPERATIONAL EXCELLENCE How to Apply Approaches to Achieve Customer/Market Relevance Customer Journeys Omnichannel Delivery and Support Context-Sensitive Delivery and Support Customer Analytics Customer Feedback and 360ø Approaches How to Achieve Operational Excellence in the Four Dimensions of Service Management Understand the Financial Aspects of Digital and IT Strategy in Terms of the Following Financial Policies Portfolio Optimization Funding Projects, Products and Services Balancing Cost of Innovation and Operation Charging Models Assess Strategic Approaches for Digital Organizations 7 - RISKS AND OPPORTUNITIES OF DIGITAL AND IT STRATEGY Concept of Risk Management in the Context of a Digital Organisation Context of Digital and IT Strategy Identify Risk Assess Risk Concept of Risk Posture and Show How to Determine an Acceptable Balance Between Opportunity and Risk Explain the Concept of Innovation, Including its Key Elements and Techniques Apply Techniques to Develop and Maintain a Culture of Innovation 8 - STEPS AND TECHNIQUES INVOLVED IN DEFINING AND ADVOCATING FOR DIGITAL AND IT STRATEGY How to Use Digital Readiness Assessment to Perform Gap Analysis Between an Organisation?s Current and Desired Positions Approaches for Scraping Data from Dynamic Websites How to Define and Communicate a Vision and a Strategy How to Use Business Cases to Advocate for a Digital and IT Strategy 9 - IMPLEMENTATION OF A DIGITAL AND IT STRATEGY How to Define Operating Models for Digital Organisations Major Skills Required of Leaders in Digital Organisation Apply Approaches to Strategy Coordination and Implementation: Large-Scale Transformation Incremental Transformation Mergers and Acquisitions Individual Changes Approaches to POMs (Parallel Operating Models) How to Assess Success of a Digital and IT Strategy Typical Activities of a Digital Transformation Programme

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is people who are moving into a database role, or whose role has expanded to include database technologies. Developers that deliver content from SQL Server databases will also benefit from this material. Overview After completing this course, you will be able to: Describe key database concepts in the context of SQL Server Describe database languages used in SQL Server Describe data modelling techniques Describe normalization and denormalization techniques Describe relationship types and effects in database design Describe the effects of database design on performance Describe commonly used database objects This course is provided as an introductory class for anyone getting started with databases. It will be useful to programmers and other IT professionals whose job roles are expanding into database management. Students will learn fundamental database concepts through demonstrations and hands-on labs on a SQL Server instance. This material updates and replaces course Microsoft course 10985 which was previously published under the same title. Module 1: Introduction to databases Introduction to Relational Databases Other Databases and Storage Data Analysis SQL Server Database Languages Module 2: Data Modeling Data Modelling Designing a Database Relationship Modeling Module 3: Normalization Fundamentals of Normalization Normal Form Denormalization Module 4: Relationships Introduction to Relationships Planning Referential Integrity Module 5: Performance Indexing Query Performance Concurrency Module 6: Database Objects Tables Views Stored Procedures, Triggers and Functions

Duration 3 Days 18 CPD hours This course is intended for This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools. Overview After completing this course, students will be able to: - Identify independent and dependent variables and measurement levels in their own analytical work scenarios. - Identify variables of interest in relational database tables. - Choose a data aggregation level and data set design appropriate for the intended analysis and tool. - Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others. - Create stored procedures, views, and functions to modularize data retrieval code. This course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence. 1 - INTRODUCTION TO TSQL FOR BUSINESS INTELLIGENCE Two Approaches to SQL Programming TSQL Data Retrieval in an Analytics / Business Intelligence Environment The Database Engine SQL Server Management Studio and the CarDeal Sample Database Identifying Variables in Tables SQL is a Declarative Language Introduction to the SELECT Query Lab 1: Introduction to TSQL for Business Intelligence 2 - TURNING TABLE COLUMNS INTO VARIABLES FOR ANALYSIS: SELECT LIST EXPRESSIONS, WHERE, AND ORDER BY Turning Columns into Variables for Analysis Column Expressions, Data Types, and Built-in Functions Column aliases Data type conversions Built-in Scalar Functions Table Aliases The WHERE clause ORDER BY Lab 1: Write queries 3 - COMBINING COLUMNS FROM MULTIPLE TABLES INTO A SINGLE DATASET: THE JOIN OPERATORS Primary Keys, Foreign Keys, and Joins Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product Understanding Joins, Part 2: The INNER JOIN Understanding Joins, Part 3: The OUTER JOINS Understanding Joins, Part 4: Joining more than two tables Understanding Joins, Part 5: Combining INNER and OUTER JOINs Combining JOIN Operations with WHERE and ORDER BY Lab 1: Write SELECT queries 4 - CREATING AN APPROPRIATE AGGREGATION LEVEL USING GROUP BY Identifying required aggregation level and granularity Aggregate Functions GROUP BY HAVING Order of operations in SELECT queries Lab 1: Write queries 5 - SUBQUERIES, DERIVED TABLES AND COMMON TABLE EXPRESSIONS Non-correlated and correlated subqueries Derived tables Common table expressions Lab 1: Write queries 6 - ENCAPSULATING DATA RETRIEVAL LOGIC Views Table-valued functions Stored procedures Creating objects for read-access users Creating database accounts for analytical client tools Lab 1: Encapsulating Data Retrieval Logic 7 - GETTING YOUR DATASET TO THE CLIENT Connecting to SQL Server and Submitting Queries from Client Tools Connecting and running SELECT queries from: Excel PowerBI RStudio Exporting datasets to files using Results pane from SSMS The bcp utility The Import/Export Wizard Lab 1: Getting Your Dataset to the Client Additional course details: Nexus Humans 55232 Writing Analytical Queries for Business Intelligence training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the 55232 Writing Analytical Queries for Business Intelligence course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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About this Training Course Managed Pressure Drilling (MPD) offers opportunities to improve safety and efficiency in drilling, and it is increasingly recognised by operators and regulators. MPD aims to cost effectively resolve chronic drilling problems that contribute to non-productive time. This 3 full-day course provides participants with an understanding of the MPD techniques and equipment. The course starts with the design requirements for an MPD operation. It covers all MPD methods, including constant bottom hole pressure and mud cap drilling operations. It also includes all of the rig types - both onshore and offshore - and MPD. MPD equipment is presented in detail and the design aspects of the well are also examined. Training Objectives This course aims to provide participants with a good understanding of the various aspects of managed pressure drilling. It also enables participants to gain knowledge of planning MPD operations, type of equipment and the equipment requirements for both onshore and offshore and MPD operations. At the end of the course, participants will be able to: Understand the variations in MPD Design MPD operations for various techniques Understand the engineering requirements of MPD Define MPD equipment requirements Understand operational and well control aspects of MPD Target Audience This course is designed for engineers, managers and service providers who are about to use MPD techniques and are trying to better understand the associated complexities and challenges surrounding MPD. Trainer Your expert course leader was born and raised in Nijmegen, Netherlands. He holds an MSc in Drilling Engineering from The Robert Gordon University in Aberdeen and has over 39 years of oilfield experience. He started in the industry back in 1977 as a Mud logger before becoming a wellsite drilling engineer in 1980 working mainly in the Middle East and West Africa. In 1988, he joined Shell in in the Southern North Sea. In 1995, he joined Smedvig in Norway and worked on a number of deepwater wells as well as on underbalanced drilling operations. In 1999, he started Leading Edge Advantage in Aberdeen and worked in coiled tubing, underbalanced and managed pressure drilling. In 2004, he joined Weatherford as Regional Engineering Manager in Singapore and developed the first ultra deepwater MPD systems. In 2010, he joined SPT Group specializing in well control and hydraulics modelling. SPT Group was later acquired by Schlumberger in 2012. At Schlumberger, he was Drilling Adviser, with a focus on well control, relief well planning and MPD. During that time, he was also an instructor for NExT teaching numerous well engineering courses. In 2015, he joined Wild Well as Engineering Manager in Asia and in February 2017, he became an independent consultant concentrating on provision of drilling engineering training. He has written and presented numerous papers and authored several chapters in the SPE Advanced Technology books. He is an active committee member and speaker on numerous SPE Workshops around the world. 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

About this Virtual Instructor Led Training (VILT) This 2 half-day Virtual Instructor-Led Training (VILT) course will guide participants on the technoeconomic aspects of capture, utilization and geological storage of carbon dioxide. The VILT course will address the methods and techniques used in the technoeconomic assessment of Carbon Capture, Utilization & Storage (CCUS) projects. It will explore in detail the factors that affect the cost-effectiveness of current and emerging technologies for CO2 capture, transport and geological storage, including monitoring and verification. Given that the successful deployment of CCUS may require economic incentives, technical and economic drivers such as technological innovation, optimization, source sink matching and emerging opportunities will also be discussed. In addition, using several worked examples and case studies, this VILT course will explain the principles behind the analysis of the costs and opportunities of a CCS / CCUS project from source to sink and examines the possibilities of using carbon dioxide from an economic perspective. Training Objectives Upon completion of this VILT course, participants will be able to: Describe the economic considerations for CCS / CCUS projects Measure and calculate the cost-effectiveness of CCS / CCUS Identify the economic drivers for CCS / CCUS Understand the value of source to sink matching Outline the economic and environmental opportunities as well as challenges with using carbon dioxide injection in a range of applications Recognize niche opportunities for CO2 storage (coal seams, basalts, salt and others)   Target Audience This VILT course is ideally suited for a technical audience such as geoscientists, petroleum and chemical engineers as well as professionals such as economists, regulators, legal staff and managers wishing to learn more about the details of economic aspects and the basis for techno-economic analysis of Carbon Capture, Utilization and Storage projects. The VILT course is presented in an interactive workshop format, allowing for discussions. Participants should have: Basic background knowledge of CCUS technologies Experience with oil and gas, coal or other energy projects Basic understanding of the energy industry Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 2 half-day sessions comprising 4 hours per day, with 2 breaks of 15 minutes per day. The VILT course is presented in an interactive workshop format that allows discussion. Course Duration: 2 half-day sessions, 4 hours per session (8 hours in total). Trainer Your expert course leader received his B.Eng. in Chemical and Environmental Systems in 2002 from Tecnológico de Monterrey, Mexico, and his Ph.D. in Chemical Engineering in 2008 from the University of New South Wales (UNSW), in Sydney, Australia, at the UNESCO Centre for Membrane Science and Technology. His doctoral used computational fluid dynamics (CFD) to analyse the flows within membrane modules used for water treatment and desalination. He also worked on a desalination linkage project between the UNSW and the European Union, as part of Framework Programme 6. From 2009 to 2014, he worked for the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC), where he led the research into CO2 Transport Networks, co-led the development of a techno-economic model for the analysis of Carbon Capture and Storage (CCS) projects, and collaborated on several consultancy and feasibility studies conducted by CO2CRC for both Government and Industry. From 2014 to 2019, he held a CONACYT Research Fellowship at the Instituto Tecnológico de Sonora (ITSON) in Mexico, where he led collaborative research projects dealing with RO membrane biofouling (IHE-Delft), membrane modifications, solar energy use for desalination (CSIR-CSMCRI India) and CFD modelling of the hydrodynamics in membrane modules (UMP Malaysia). Since July 2019, he is a Research Fellow in the School of Chemical and Biomolecular Engineering at the University of Sydney, where his research focuses on finding ways to reduce the cost, energy use and environmental impact of technologies for providing clean energy and water. From 2015 to 2020, he was a Member of the Board of Directors of the Mexican Society of Membrane Science and Technology. He guest edited a special edition on CCS for the Journal 'Technologies' and is currently an Editorial Board member for the journal, 'Energies', a peer-reviewed open-access scientific journal. His research interests include improving the efficiency of osmotic membrane separation processes, modelling complex processes involving heat and mass transfer, and exploring the economic drivers of low emission technologies such as the Carbon Capture and Storage (CCS) chain. 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

About this Training Course In our pursuit to discover oil & gas in deeper horizons, wells are often drilled in a HPHT environment. To be considered a HPHT well, the downhole conditions will have pressures in excess of 10,000 psi (69 MPa) and 300° F (150° C). To drill these usually expensive wells successfully, the planning and execution phase has to be of an exceptionally high standard. Therefore, both operator and drilling/service contractor staff must be seamlessly aligned and work as a coherent team to reach and then harness the well objectives. This is particularly important when speciality services such as Managed Pressure Drilling (MPD) are being applied with crews and/or supervisors who are not intricately familiar with complicated well control incidents. As we seek to prevent costly non-productive time, attention will also be paid to enabling technologies like expandable solid tubulars, mud coolers and specialty mud. The 3 full-day course will explain the key characteristics and challenges of HPHT Planning and Well Control. This includes: Differences between HPHT and standard (conventional) wells and what this entails for well design. The challenges unique to HPHT and the impact of Pore Pressure Prediction (PPP). Static and Dynamic Equivalent Mud Density and the factors that influence the ultimate Bottom Hole Pressure (BHP). Control practices such as 'fingerprinting' to identify what's happening downhole. HPHT shut-in procedures and practices. Specific HPHT equipment and drilling tool requirements and advantages of Managed Pressure Drilling (MPD). Well control on/off bottom, bull-heading and dealing with kick-loss situations. Mud management, tolerance on mud properties and challenges in cementing. Case history on emergency control. Drills, team effort, checklists, human factor and 'getting everybody on board'. Training Objectives By the end of this course, participants will be able to: Recognize the main differences in planning/design between HPHT and standard (conventional) wells as well as the challenges that will have to be faced. Explain drilling and tripping operational challenges and practices as well as how to manage these effectively. Identify wellbore breathing (a.k.a. ballooning) and how to safely deal with this within the narrow window for mud density selection. Apply practices of consistent fingerprinting and how to develop procedures for this to benefit the rig team. Measure and understand bottom hole pressure and the effect of influencing factors such as temperature, rotation and flow rate. Understand the critical mud properties to alleviate barite sag, general mud density control techniques and specify the essentials in cementing. Manage losses and low fracture gradients with well bore strengthening methods. Execute sound HPHT shut-in procedures. Understand how MPD can assist in the safe and efficient drilling and hole cleaning of HPHT wells. Understand the use of MPD Influx Management Envelopes. Identify critical Early Kick Detection Systems (EKDS) and HPHT well control equipment. Execute safe secondary well control practices in a H2S environment, bull-heading, on and off bottom and handling of gas at surface, using MGS and hydrate mitigation measures. Target Audience The course is intended for: Office-based staff engaged in HPHT well planning and day-to-day operations Field-based rig staff working as front-line supervisors - from Assistant Drillers to Senior Toolpushers Field-based rig staff working for service companies supporting the execution of HPHT wells (MPD, mud and mudlogging services etc.) Trainer Your expert course leader has over 45 years of experience in the Oil & Gas industry. During that time, he has worked exclusively in the well engineering domain. After being employed in 1974 by Shell, one of the major oil & gas producing operators, he worked as an apprentice on drilling rigs in the Netherlands. After a year, he was sent for his first international assignment to the Sultanate of Oman where he climbed up the career ladder from Assistant Driller, to Driller, to wellsite Petroleum Engineer and eventually on-site Drilling Supervisor, actively engaged in the drilling of development and exploration wells in almost every corner of this vast desert area. At that time, drilling techniques were fairly basic and safety was just a buzz word, but such a situation propels learning and the fruits of 'doing-the-basics' are still reaped today when standing in front of a class. After some seven years in the Middle East, a series of other international assignments followed in places like the United Kingdom, Indonesia, Turkey, Denmark, China, Malaysia, and Russia. Apart from on-site drilling supervisory jobs on various types of drilling rigs (such as helicopter rigs) and working environments (such as jungle and artic), he was also assigned to research, to projects and to the company's learning centre. In research, he was responsible for promoting directional drilling and surveying and advised on the first horizontal wells being drilled, in projects, he was responsible for a high pressure drilling campaign in Nigeria while in the learning centre, he looked after the development of new engineers joining the company after graduating from university. He was also involved in international well control certification and served as chairman for a period of three years. In the last years of his active career, he worked again in China as a staff development manager, a position he nurtured because he was able to pass on his knowledge to a vast number of new employees once again. After retiring in 2015, he has delivered well engineering related courses in Australia, Indonesia, Brunei, Malaysia, China, South Korea, Thailand, India, Dubai, Qatar, Kuwait, The Netherlands, and the United States. The training he provides includes well control to obtain certification in drilling and well intervention, extended reach drilling, high pressure-high temperature drilling, stuck pipe prevention and a number of other ad-hoc courses. He thoroughly enjoys training and is keen to continue taking classes as an instructor for some time to come. 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

Would you like to get up to speed on the key debates on telecoms regulation or refresh your understanding of the broader range of regulatory issues? Thanks to our online Telecoms Masterclass, you will understand all you need to know about telecoms regulation in the EU and other regions when applicable and relevant.