18-21 March 2019

Houston, USA

Day One
Tuesday, March 19

Day Two
Wednesday, March 20

Chair Opening Remarks

  • David Fulford Sr. Staff Reservoir Engineer, Exploration Evaluation, Apache

Opening Keynote: Taking an Enterprise-Wide Approach to Data-Driven Upstream Decision-Making


  • Showing the transformative impact that data analytics can have to meet business needs and add value
  • Prioritizing Data, Technology, Culture and Digital to deliver sustainable and cost effective solutions
  • Embedding analytics into the strategic core of all upstream IT and Engineering workflows
  • Overcoming cultural and technical challenges of integrating analytics into dayto- day activities to enable system-wide change
  • Exploring the successes, challenges and learnings of Equinor’s case study for tool implementation

Embedding Data Analytics into Core Business Processes and Decisions

Upskilling the Business-Side to Take the Lead on Analytics Initiatives

  • Detlef Hohl Chief Scientist Computation & Data Science, Shell


  • Engaging engineers in analytics to overcome the shortage of subject matter experts needed to qualify the data
  • Understanding the core skills required and developing recruitment strategies to facilitate future training
  • Reviewing training programs and skill development initiatives
  • Integrating engineers and data teams for cross-function learning and mentoring
  • Developing data literacy initiatives to ensure upstream groups can leverage data to gain meaningful insights

Panel Discussion: Fostering IT, Data & Business Collaboration to Drive Maximum Value and Actionable Insights from Upstream Data

  • Stephen Taylor Reporting, Analytics & Data Management Manager, Devon Energy
  • Michael Yoho Innovation and Transformation, CNX
  • Amii Bean Engineering Tech Manager, Enervest Operating


  • Establishing core principles for successful collaboration across different departments and functions
  • Sharing different perspectives on specific IT and business challenges to reach consensus on new approaches to unify analytics
  • Implementing integrated workflows that facilitate and promote data sharing across different departments
  • Defining technology requirements for specific discipline analytics to develop data systems suitable for all needs

Morning Refreshments & Speed Networking


This Speed Networking session is the ideal opportunity to meet face-to-face with other industry leaders. Specifically designed to connect you with many new contacts, take this opportunity to share learning, strategies and insight, as well as enhance your connections.

It’s (Not) Hip to be Square: Re-thinking Model Design to Overcome Data Challenges

  • David Fulford Sr. Staff Reservoir Engineer, Exploration Evaluation, Apache


  • Trust but verify: data skepticism as a first-order principle
  • Predictive accuracy vs. mathematical rigor: Ensuring models are robust against outliers and tailored to achieve predictive goals
  • Bring your own model: algorithm design to integrate physics and statistics
  • Applying probabilistic programming and Bayesian methods to solve inverse model problems

Optimizing the Opportunity of Data Science in the Upstream Environment


  • Understanding what real value data science can add to upstream business and operations to take production to the next level
  • Integrating data science into IT systems
  • Exploring how data scientists can derive meaningful analysis from upstream data if they don’t have domain expertise
  • Encouraging citizen data science across the business
  • Establishing reporting lines and best positioning within the corporate structure
  • Embedding data science into strategic upstream functions so it becomes intrinsic to everyday business


Real-World Application Use Cases: 1st Round

Use Case 1 – The Creation of Intelligent Monitoring Systems for Improved Operational Decisions

  • Rob Klenner Principal Geoscientist, Energy & Environmental Research Center


  • Improving planning through multivariate statistics
  • Integrating data with different acquisition times and scales: High-acquisition frequency (e.g. production and pressure) and low acquisition frequency (e.g. seismic and well logs)
  • Analyzing data for real time wellbore changes and far field fluid movement
  • Exploring automation and closing the loop

Use Case 2 – Machine and Deep Learning for Real-Time Predictive Maintenance of Frac Pumps

  • Jay Parashar Principal Data Scientist - Advanced Analytics, Schlumberger


  • Adapting to smarter equipment maintenance processes through predictive anlalysis from preventative and reactive maintenance
  • Learning how Schlumberger supports thousands of Frac pumps globally by generating and leveraging tremendous amounts of sensor, operational and maintenance data
  • Exploring how the collected data is processed and analyzed by various Machine Learning and Deep Learning techniques for Real Time Anomaly detection

Afternoon Refreshments

Use Case 3 – A Journey from Unsupervised to Supervised Learning


  • Exploring the design journey of an AI solution pipeline
  • Discovering how this solution has learned the SMEs interaction with the tool to understand the business use case
  • Overcoming limited amounts of clean tagged data by transitioning from an unsupervised learning system to a fully supervised and predictive solution
  • Informing decisions for completion design through a high-resolution Frac pumping job assessment tool

Keynote: Gaining Buy-In and Building Trust to Fully Leverage the Power of Data Analytics


  • Why focusing on the people is the key success factor to making this work
  • Leadership opportunities and challenges
    • Overcoming cultural barriers to adopting new technologies and new ways of working
    • How to engage the organization in trusting in new tools, models and outputs
    • The importance of communication
  • Believing that data is a critical asset
  • Building trust in the data
  • How to get started with analytics

Chair’s Closing Remarks

  • David Fulford Sr. Staff Reservoir Engineer, Exploration Evaluation, Apache

End of Day One