-    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 

-    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

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

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

-    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 

-    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

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

-    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

-    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

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

-    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 

- 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
 

-    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

-    Comprehensive architectural framework for real time optimization and operation
-    Hybrid modelling, which combines machine learning and first principles modelling
-    Practical use cases depicting the problems, latency in monitoring in drilling wells in real time

-    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

-    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
 

-    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
 

-    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.

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

-    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

-    Utilizing Digital twin and ML technologies to create solid digital cores, lower operational costs, optimize processes, and enhance real-time decision making
-    Creating operational backbones across value chains
-    Sharing real-life examples of the ROI of integrating these technologies