DAY 1: Wednesday, 12 April 2023

Wednesday, April 12, 2023
9:00 AM - 9:15 AM
Jim Claunch
9:15 AM - 10:00 AM

-    Understanding Machine Learning capabilities across upstream, midstream, and downstream operations
-    Addressing the impact of Machine Learning adoption on the new generation of engineers and scientists
-    Accelerating and scaling use across value chains 

Jim Claunch Lucas Green Summer Husband Neha Sahdev Srimoyee Bhattacharya
10:00 AM - 10:30 AM

-    Understanding the suitability of Machine Learning across Oil & Gas operations 
-    Reviewing current in-house technologies to achieve the same goals
-    Considering the current state of datasets

Srimoyee Bhattacharya
10:30 AM - 11:00 AM
11:00 AM - 11:40 AM

-    Exploring factors necessary to enable successful decisions 
-    Identifying opportunities in mature oilfields using data
-    Differentiating between cost optimization and abandonment operations

Sudhir Pai Amit Jain Anupam Singh
11:40 AM - 12:10 PM

-    Designing intelligent wells
-    Leveraging AI algorithms to achieve better production rates
-    SoftServe’s approach to artificial lift optimization

12:10 PM - 12:15 PM
Nick King
12:15 PM - 1:10 PM
1:10 PM - 1:30 PM

-    Machine Learning, its current status, and future learnings and opportunities
-    Progressing from idea to actualization 
-    Unlocking the power of predictive maintenance
-    Reviewing Aramco’s R & D projects in the upstream Oil & Gas sector 

Weichang Li
1:30 PM - 1:50 PM

-    Highlighting the limitations of traditional seismic inversion
-    Improving the accuracy and reducing cycle times through automation
-    Training datasets to predict lithology, porosity, and fluid type
-    Comparing results from a synthetic dataset and a Gulf of Mexico case study

Prasenjit Roy
1:50 PM - 2:10 PM

-    Seismic inversion as a bridge from geological knowledge to reservoir modelling
-    Challenges and uncertainties in seismic inversion
-    Conventional vs ML methods in quantifying uncertainty

Konstantin Osypov
2:10 PM - 2:30 PM

-    Operator and oilfield service company perspective on the impact of conventional methods on the economic attractiveness of wells 
-    Overcoming field challenges by turning them into opportunities 
-    Making a business case for Machine Learning as a solution compared to other technologies

Konstantin Osypov Prasenjit Roy Weichang Li
2:30 PM - 3:00 PM
3:00 PM - 3:15 PM

-    Maximizing natural gas-fired power plant dispatch production using Machine Learning and Artificial Intelligence 
-    Developing tailor-made advanced tools and facilitators overcoming training hurdles 
-    Increasing the profitability of combined cycle power generation plants 
-    Optimizing Day Ahead Energy Market Offers by comparing results generated by multiple RL agents using different algorithms

Ziad Katrib
3:15 PM - 4:00 PM

-    Planning for carbon-neutral operations and automated reporting 
-    Consolidating carbon emissions across value chains 
-    Understanding how Machine Learning can accelerate ESG obligations

Hatem Nasr Ph.D Uchenna Odi, PhD, MBA Susan Nash Robert Ward Sean Donegan
4:00 PM - 4:30 PM

-    Evaluating Machine Learning enabled technologies currently being used in CO2 storage 
-    Planning for low-carbon operations
-    Use case highlighting the application of Machine Learning in CO2 emission predictions 

Wenyi Hu
4:30 PM - 4:45 PM

- Untapped potential in HSE & Operations using Machine learning
- What Machine Learning can/can't do in HSE & Operations 
- Why Managers and Engineers synergy is crucial for successful implementation of new technology
- Real use cases and success stories with PySAFETY- PHA Solution and PyRISK - QRA solution

Manja Bogicevic
4:45 PM - 4:55 PM
Jim Claunch
5:00 PM - 6:00 PM
Time Zone: (UTC-05:00) Central Time (US & Canada) [Change Time Zone]

DAY 2: Thursday, 13 April 2023

Thursday, April 13, 2023
9:00 AM - 9:05 AM
Jim Claunch
9:05 AM - 9:35 AM

-    Harnessing the power of upstream, midstream, and downstream data to enhance business intelligence and provide actionable insights
-    Understanding how various internal teams collect and use their data
-    Identifying analytical tools and technology to facilitate Machine Learning projects

Bernardo Braunstein
9:35 AM - 10:05 AM

-    Discussing the industry’s challenges in adopting ML and the strategies to overcome them
-    Breaking barriers to digitalization, showcasing successful ML enterprise deployments
-    Understanding why ML is more than just coding

Catalina Herrera
10:05 AM - 10:35 AM

-    Understanding Chevron Phillips Chemical Company’s driver for the Rheometer implementation
-    Discussing project challenges and learnings
-    Building data-driven frameworks for predicting drilling fluid's behaviour
-    Standardizing properties by exploring machine learning algorithms
-    Use case highlighting the transition from pilot to full implementation

Brent Railey
10:35 AM - 11:05 AM
11:05 AM - 11:20 AM

-    Facilitating acceptance and transition to fully digitized and automated upstream operations
-    Ensuring correct system responses to gain trust from operators and production engineers
-    Demonstrating integrated machine learning and analytics enables full automation of artificial lift and other components of upstream operations

Krzysztof (Kris) Palka
11:20 AM - 11:55 AM

-    Demonstrating the application of DRL in inventory management and shipping scheduling.
-    Comparing the DRL approach with conventional reorder policy/optimization methods.
-    Using DRL to make robust decisions against disruptions in demand and supply.

Meng Ling Tina Zhao
11:55 AM - 12:25 PM

-    The value of time series data in monitoring your sensors, machines, and plants
-    Some examples of open-source software that are fulfilling the promise of Industry 4.0
-    How some companies are using time series solutions in their production-ready environments today

Brian Mullen
12:25 PM - 1:25 PM
1:25 PM - 2:10 PM

-    Discussing current supply chain complexities and uncertainties 
-    Modernizing Supply Chains with AI & Machine Learning for Resilience and End-To-End Optimization
-    Linking statistics and relationships- injecting accuracy through collaboration with subject matter experts

David Crawley Al Lindseth Rajeev Aluru Vikram Jayaram
2:10 PM - 2:40 PM

-    Understanding how Dow attracts and retains talent
-    Deploying Proofs of Concept within the areas of IoT, digital thread (sourcing data and data accuracy), as well as data analytics (machine learning and decision-making)
-    Implementing recruitment and compensation strategies for entrepreneurs’ software engineers, and people with operations experience

Alec Walker
2:40 PM - 3:10 PM
3:10 PM - 3:40 PM

-    Visualizing real-time mechanical device data (e.g., pump data, manufacturing, robotic systems) and predicting mechanical issues (via sensors) for your critical infrastructure
-    Getting started with prediction models 
-    Using Red Hat and partner tools to curate, capture, test, and deploy models from water pump sensor data 

Audrey Reznik Guidera
3:40 PM - 4:25 PM

- Learning how Machine Learning is deployed, the challenges, and opportunities
- Discussing competitive differentiators that drive agility
- Practical use cases of deployment

Amy Henry Inderpreet Jalli Anshumali Shrivastava Stephen T. Wong
4:25 PM - 4:30 PM
Jim Claunch
Time Zone: (UTC-05:00) Central Time (US & Canada) [Change Time Zone]