•    Digitization and Digital Transformation: Trends for 2022 
•    Digitization and value creation: In need of a shift 
•    Most urgent priorities for oil and gas companies to achieve true digital transformation

Technology evolution is driving the shift to cleaner energy. The wealth of data generated will provide insight into supply chain visibility, help understand emissions, and abate options better.
-    What is the next level for Machine Learning that could transform the industry's outlook?
-    Is Machine Learning being implemented for the sake of it? How are energy companies aligning technology strategies and business strategies?
-    How to merge business processes, workflows, and contextualized data
-    Collaborating with demand-side services. What is the role will providers play in ensuring targets are met? What are the subsequent significant discussions on the horizon? 
 

-    Can predictive analysis reduce carbon emissions and help meet carbon reduction goals?
-    How is Machine Learning used to detect anomalies in offshore production platforms?
-    Leveraging Machine Learning and Artificial Intelligence to monitor asset behaviors and lifecycles

• Answering why Artificial Intelligence should be used in upstream and downstream deployments 
• Deploying Artificial Intelligence to solve the most significant challenges  
• Aggregating and making sense of data from the edge to core 

Including a welcome address from Sean Cahill.

Major priorities with Machine Learning are preparing data, developing a model to train it, and then deploying the model, but what are the components of a successful Machine Learning implementation project?
•    Determining the aspect of the value chain requires technological advancements
•    Making a business case and getting leadership buy-in
•    Integrating and matching vendors
•    Structuring and ensuring successful pilots

In recent years, the Oil and Gas sector made significant investments in its data analytics and Artificial Intelligence initiatives. However, recent studies show that these initiatives are stalling and have a low return on investment due to misalignment between business needs and the Artificial Intelligence solutions developed, data availability, access, and quality, slow adoption of resolutions by business segments, failure to scale and productionize, and decision makers’ inability to fully understand the value Artificial intelligence can add to their portfolios. This session highlights the success of BHP’s Artificial Intelligence initiatives within E and P and lessons learned from their journey.

The Oil & Gas industry presents unique challenges for developing and deploying valuable models. Heterogenous infrastructure at the edge, complex enterprise architectures, and organizational complexity mean it’s hard to drive a seamless, low-friction machine learning process. Learn how Domino’ end-to-end workflows (powered by Dell hardware) make Enterprise MLOps a reality.

Incredible risk and disruption is driving the need for companies to adapt and drive transformational and change efforts.  However, the track record and return on these investments are horrible.  Al will posit five specific reasons why these efforts fail with references to and examples from the topics being discussed at this conference (big data, machine learning, AI or analytics efforts) – with a goal for attendees to learn how to avoid these issues with the right approach.

-    Effects of complexity, principal-agent issues, and human cognitive biases on oil and gas megaproject outcomes
-    Machine learning for predictive analytics in oil and gas megaprojects
-    Quantitative cost and schedule risk forecasting and reduction in oil and gas megaprojects
-    Human cognitive biases and corrective measures using machine learning
-    The balance between professional expertise and machine learning

-    Blending compatible technologies for better risk analysis
-    Establishing shared truths around immutable records
-    Applying intelligent traceability through commodity lifecycles 
 

5-minute welcome from Andy Hock followed by a complimentary drinks reception and hors d'oeuvres.

-    Using deep learning to accelerate the process of seismic integration 
-    Understating subsurface geology 
-    Identifying potential plays
 

-    Understanding the dynamic resource requirements of Artificial Intelligence/Machine Learning based workloads
-    Helping organizations manage to compute resources such as Graphical Processing Units (GPU’s) to drive better resource allocation and increase cluster utilization
-    Applying advanced scheduling methods to dynamically set priorities and policies to orchestrate jobs better

-    Using artificial intelligence to read P&IDs and Isometrics of industrial plants to build a digital model "twin" of the plant
-    Creating master asset inventory or master tag list- valve, lines, equipment, I/O, control loops, and 3D models of plants
-    Digitalizing for management change 

-    How can incident report libraries be strengthened? What information shouldn’t be left out?  
-    What is a customized reporting system? 
-    How can collated data be used to develop risk prevention and mitigation strategies? 
-    Educating operators on the root causes of understand the root causes of hazards and equipment failure 

-    Shared examples of supply chain transformations highlighting advantages to bottom lines
-    Understanding the current supply chain challenges and risk mitigation techniques 
-    Creating procurement strategies to minimize region-specific shortages and supply disruptions 

•    Challenges facing the Artificial Intelligence and Machine Learning for improving oil and gas production 
•    Transitioning the existing workforce to have Artificial Intelligence full-scale process 
•    Machine Learning & Deep Learning solutions for upstream, midstream, and downstream 
•    How Machine Learning can boost & optimize the risk management process. 

- Unpacking Chevron’s data science history- the early years and beyond the Artificial Intelligence and Machine Learning hype in 2010
- Building sustainable Machine Learning operations
- Operationalizing Machine Learning- the creation of roles, partnerships between DS’s and MLE’s and standardizing pipeline delivery
- Understanding the MLOps maturity model- where is Chevron today, and where are we going?

-    Applications of Artificial Intelligence and Machine Learning for improving oil and gas production
-    Laredo's business case: Changing the approach to ESP operations
-    Creating a Machine Learning solution for ESP optimization and well production improvement   
-    Challenges and key lessons learned from building a Machine Learning solution for ESP optimization

-    How are business models affected by putting employees at the centre of technology changes?
-    Transitioning the existing workforce
-    Aligning people with technology- use cases
-    How is transformation redefining the future of work?
-    Ramping up efforts to close the skill gap