-    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 

-    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 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

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

-    Growing in-house teams composed of data and Machine Learning specialists
-    Utilizing customizable tools to support operations 
-    Preparing the next generation of petroleum engineers for the Machine Learning-era

-    Enabling real-time data visibility and analysis remotely from static and dynamic assets
-    Reducing onsite staffing and cost using Machine Learning algorithms 
-    Building the next generation of intelligent Oil and Gas assets

-    Reviewing tools, techniques, and strategies to learn from incident data
-    Standardizing reporting formats and identifying if they can be used as seeds for future incidents 
-    Discussing practical experience in learning from accidents 

-    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

-    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

-    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

-    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 

-    Determining properties of newly drilled wells
-    Predicting well logs before drilling 
-    Developing data-driven well logs for existing and newly drilled wells