HARNESS OIL & GAS BIG DATA WITH ANALYTICS - BY Mr. Keith R Holdaway.
Oges presents 2-day online training for oil professionals on harnessing oil and gas big data with analytics in association with Mr. Keith R Holdaway. This course opens with a high-level introduction to AI, machine learning and deep learning concepts. Introduction to some data-driven techniques commonly used to optimize drilling/completions strategy,and well performance. Will also touch on subsurface datasets to improve reservoir characterization.
You have all heard of the marketing terms: Big Data, AI, ML and DL.Other terms are digital twins and supervised or unsupervised methods. But, when do you use these algorithms? Which algorithm is best? How can I scale and repeat workflows according to some process or methodology?
Are these the questions you are seeking answers to? If yes,then this course is definitely what you have been looking for!
The overview on Day 1 and the Day 2 case studies provide these answers.
About the Instructor
Mr. Keith Holdaway - has been working in the O&G patch since 1979 as a geophysicist, and over the past 23 years, as a data scientist developing soft-computing models across the O&G vertical with upstream being the focus recently.
Follow the link below to know more about the course directly from Mr. Keith Holdaway -
Course Description
The Oil and Gas industry has seen persuasive arguments over the last decade to adopt a digitalization strategy as upstream business problems are too complex to continue in siloed disciplines with traditional deterministic and interpretive analysis workflows. It is important to address the global market pressures we are experiencing today to reduce Opex and optimize processes with data-driven analytical workflows. Production optimization and Condition Based Monitoring (CBM) across all upstream assets are increasingly important issues in the oilfield. Operators are now relying on harnessing more accurate and efficient information from the vast upstream siloed datasets to exploit their reservoirs in a timely and effective manner.
Data management is fundamental to any advanced analytical workflow. All soft computing techniques are reliant on a sound data management platform. It is critical to transpose raw disparate data into actionable knowledge via integration and aggregation workflows. With the advent of Big Data analytics in the upstream siloed engineering disciplines, a robust suite of data management steps and Exploratory Data Analysis visualizations provide critical insights to accelerate the business decision cycles more accurately and successfully.
This two-day course demonstrates successful analytical workflows implementing supervised and unsupervised Machine Learning (ML)and Deep Learning (DL) algorithms across the E&P value chain. We shall define the differences between Artificial Intelligence (AI), ML and DL. It is important to appreciate the power of commonly used supervised and unsupervised techniques. The course is devoted to real-world execution of these technologies. We shall focus on production optimization, forecasting and CBM across certain upstream assets.
Topics:
- Lift the mist by differentiating AI, ML and DL data-driven analytical workflows
- The importance of Exploratory Data Analysis and data management
- Application of supervised/unsupervised techniques in upstream
- Optimize drilling and completion strategies
- Streamline reservoir management: Production optimization, data-driven forecasting and stimulation
- Condition Based Monitoring to reduce Opex in upstream asset performance and survival analysis
Key Learning Objectives
- Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
- Fundamental concepts of Machine Learning and Deep Learning
- Difference between supervised, semi-supervised and unsupervised learning
- Machine Learning workflow and how to implement the steps effectively
- The role of performance metrics and how to identify their essential methods
- Upstream Value Propositions
Learning Level
Intermediate to Advanced
Course Length
2 Days, Total 6 Hours (3Hrs + 3Hrs)
Course Fee
100 USD for participants outside India.
INR 7500 + GST for Indian Participants.
Training Schedule
Date - 29th & 30th August, 20 (Sat & Sun)
Time
7 pm to 10 pm - India Standard Time (IST) - GMT +5:30
2:30 pm to 5:30 PM West Africa Time (WAT)
USA Time Zones
9:30 am to 12:30 pm - EST / EDT - GMT-4
8:30 am to 11:30 am - CST/CDT - GMT-5
7:30 am to 10:30 am - MST/MDT - GMT-6
6:30 am to 9:30 am - PST/ PDT - GMT-7
Take Away from Day 1
Day 1 opens the critical conversation of applying data-driven analytical models in three areas of the upstream.
1. What are the common soft-computing modeling practices in optimizing drilling, production and subsurface reservoir characterization?
2. Let’s discuss key Machine Learning and Deep Learning techniques that can help the geoscientist interpret the data in these upstream sectors.
3. Fully understand the difference between supervised and unsupervised learning and when best to use the ML/DL algorithms under each type of learning technique.
Are you confused by the wide array of ML/DL possibilities?
Day 1 sheds light on the data-driven approaches and helps you to decide when and how to get the most out of a digital transformation in E&P.
Take Away from Day 2
Day 2 gets into the details of what we covered in Day 1.
Walk through an anonymized case study in each of the three areas: drilling, production and subsurface.
The O&G industry is late to the digital transformation show, but Day 2 will lift the veil on successful applications. See where operators have operationalized ML/DL for business value, enabling geoscientists to become data scientists and accelerate their insights and be more productive.
At the end of this course you can immediately see value by applying ML/DL on your E&P datasets. Are you someone in E&P who is interested in using Python or vendor specific ML/DL software to be a key member of your company’s digitalization project? Then, this 2-day course will get you going with sound background understanding, illustrated by real case studies.
Why Attend?
Digital technologies integrated with data-driven insights provide operational transformations that can enhance production and reduce Opex with strategic decision-making.
Upstream data are collected in real-time, delivering rich spatial and temporal insights that can be harnessed by data-driven analytics. Operators and service companies must find a competitive advantage in their business models to optimize production and reduce Opex. AI technologies with ML and DL workflows are the cornerstones of a digital transformation for the future survival of O&G companies. Stay ahead of the curve by learning some practical use cases employing these technologies in the O&G industry.
Who Attends?
This course is designed for engineers and managers. Particularly for those who are tasked to optimize drilling and completion strategies, reservoir management and production optimization in operating and service companies. Asset performance analytics are discussed to maximize performance and maintenance scheduling as well as predict anomalies and degradation in equipment for artificial lift systems.
Hope to see you in the training on 29th August,20, click on the below link to register!