Day Two: Wednesday 25 September

DAY 2 AGENDA: WEDNESDAY 25 SEPTEMBER

> Integrating Data Analytics > Data Analytics Technologies > Machine Learning & Deep Learning > Business Intelligence > Predictive Analytics & Remote Monitoring

8:50 Chair's Opening Remarks

Bill Fairhurst,President,Riverford Exploration

INTEGRATING DATA ANALYTICS: KEYNOTE PANEL DISCUSSION

9:00 Integrating Data Analytics Across All Multi-Silos & Functions To Make Smarter Decisions And Improve Operational Efficiency

Delivering strategic and technical insight to effectively leverage data analytics capabilities and provide complete clarity on key issues including:

  • At what level do you do analytics? Machine level, Node level or Edge Computing? Find out what works by company-size
  • Which software was implemented and what were the efficiency gains seen
  • What are the operator's 'best practices 'for gathering and interpreting data
  • Learn how these E&Ps are using data within their operations efficiently
  • Assess how methodologies from large companies can be applied to smaller organizations
  • Clarify the cost trade-offs and short and long-term value-gains

Moderated By: Bill Fairhurst, President, Riverford Exploration

Amii Bean, Manager - Engineering Techs,EnerVest Energy

George Payne, Controller, Perdido Energy

9:30 Question & Answer Session

INTEGRATING DATA ANALYTICS: TECHNOLOGY CASE STUDY

9:40 Utilizing The Latest Technology To Manage The Data After Different Sources Have Been Integrated:Understanding The Full Spectrum Of Possibilities On How To Use Production, Drilling & Completions Data

  • Extracting features from raw data and transforming them into data that can be used
  • Utilizing Feature Engineering to make more educated choices in order to understand the process
  • Going from raw data to features in order to think about the models and the problems needed to be solved
  • Selecting the right technology that can create a secure data environment

Dingzhou Cao, Data Science Manager, Occidental Petroleum

Kate Ruddy,Drilling Engineer, Occidental Petroleum

Yuchang Shen, Machine Learning Engineer, Occidental Petroleum

10:20 Question & Answer Session

10:30 Morning Refreshments In The Networking & Exhibition Area

EQUIPMENT RELIABILITY, INCREASED BANDWIDTH & FASTER PULLING TIME

ASSET RELIABILITY

11:10 Leveraging Real Time Data To Accurately Predict And Mitigate Production And Midstream Infrastructure & Equipment Failures

  • Optimizing, monitoring and making real-time decisions to proactively mitigate equipment failure
  • Determine when servicing needs to be carried out
  • Preventing catastrophic equipment failure through predictive analytics techniques
  • Learn how to accurately diagnose artificial lift failures

Bill Fairhurst, President,Riverford Exploration

11:40 Question & Answer Session

INCREASING BANDWIDTH: ROUNDTABLES

11:50 Strategies For Investing In Communications Infrastructure To Increase Bandwidth And Deliver Faster Pulling Time

  • Discuss models for projecting and categorizing well performance with details on well development, interference and interaction
  • Understand approaches to cutting the analysis time on reservoir stimulation using the latest machine learning and AI algorithms
  • Hear practical applications and the results of different techniques and algorithms for petrophysical analysis

12:20 Question & Answer Session

12:30 Networking Lunch In The Exhibition Area

PRODUCTION OPTIMIZATION

2:00 Implementing An Integrated Production Surveillance & Optimization System In An Unconventional Field Combining Hybrid Data & Physics Approaches

  • Review best practices and lessons learnt from implementing a digital oilfield platform for unconventional reservoirs
  • Emphasis on leveraging routinely measured data from a practical standpoint, with automated value-driven workflows
  • Discussion of reliable, consistent and scalable well performance analysis methods for unconventional reservoirs
  • Hear about practical combination of data-driven and physics-based techniques

Diego Molinari, Staff Reservoir Engineer/Product Owner, Occidental Petroleum

2:30 Question & Answer Session

REAL-TIME BUSINESS INTELLIGENCE (BI)

MACHINE LEARNING: E&P PANEL DISCUSSION

2:40 Sharing E&P Insights On The Real CapEx And OpEx Reduction Opportunities For Various Machine Learning, Deep Learning & Data Analysis Algorithms

  • Compare the value of leveraging supervised vs. unsupervised machine learning algorithms on cost savings and efficiencies
  • Understand how these operators are achieving business benefits and how they are collaborating across different business silos
  • Using design-based thinking to pinpoint which actions will drive the best business impact

Bill Fairhurst, President,Riverford Exploration

3:20 Question & Answer Session

3:30 Chair's Closing Remarks

3:40 Close of Conference

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