Full Main Conference Agenda

DAY 1 AGENDA - WEDNESDAY AUGUST 28, 2019

DAY 1: Focuses On Demonstrating Real-World Business Value When It Comes To Machine Learning Implementation

Featuring Case Studies On How To Justify Investment In Machine Learning And How To Identify Where Technology Can Provide The Greatest Value & ROI

> Machine Learning Implementation > Production Optimization Utilizing Machine Learning > Predictive Maintenance To Ensure Asset Reliability

8:50 Chair's Opening Remarks

Danny Durham, Director Global Upstream Chemicals, Apache Corporation

OPENING STRATEGIC LEVEL PANEL FOR DATA SCIENTISTS & HEADS OF TECHNOLOGY FROM E&PS ACROSS THE LOWER 48

9:00 Demonstrating Business Value And Understanding 'What's Possible' With Machine Learning & A.I. To Justify Investment In the Latest Machine Learning Innovations

  • What has worked for operators when it comes to implementing Machine Learning technology?
  • Where have operators found the most savings when using predictive analysis and machine learning?
  • Where are operators seeing the quickest pay-offs?
  • Understand the geographical differences between plays and how that impacts the implementation of Machine Learning technology

Alexander Klebanov, Sr. Data Scientist, Chesapeake Energy

Graeme Gordon, Sr. Geological Advisor, Hess

9:30 Question & Answer Session

HOW TO IMPROVE PERFORMANCE & PRODUCTION RATES THROUGH MACHINE LEARNING IMPLEMENTATION

OPERATOR PANEL SESSION: DELIVERING VALUE FROM MACHINE LEARNING

9:40 How To Effectively Demonstrate The Bottom Line Benefits And Current Level Of Innovation To Justify Investment In Machine Learning

A main topic of concern is evaluating the immediate-term payback of employing Machine Learning across the enterprise and getting buy-in from senior management when it comes to investment.  This session looks at the challenges of adoption and discusses strategies to improve acceptance of emerging technology applications;

  • Deploying Machine Learning and A.I. solutions to reduce your OpEx and CapEx whilst increasing performance
  • Motivating your company to change roles, processes and behaviours
  • Envisioning and implementing more effective and streamlined processes
  • Engaging in agile to look at currently available technologies, and the technology on the horizon

Dr. Sarita Salunke, Petrophysicist/Data Scientist, BP

Clayton Burrows, Reservoir Engineer, Apache Corporation

10:10 Question & Answer Session

TRUSTING THE LOGIC IN THE 'BLACK BOX'

10:20 Improving Operational Understanding Of The Black Box Nature of A.I. To Better Understand The Relationships Between Inputs & Outputs

AI can provide immediate impact for oil and gas companies - reduced expenses, increased productivity, improved work methods - but operators have been slow to adopt the technologies available due to security concerns, cost, lack of understanding about the benefits to be gained and an inability to understand what is going on inside the black box.  This session looks at how the black box works and how it can streamline the decision process without being a concern;

  • Understanding the convergence of what A.I. can achieve and what humans can understand about it
  • Change management and introducing A.I. technologies to existing drilling operations
  • Examine how the technology can reduce expenses and improve productivity and what the future holds in terms of technological innovation

Dr. Sarita Salunke, Petrophysicist/Data Scientist, BP

10:50 Question & Answer Session

11:00 Morning Refreshments In The Networking & Exhibition Area

MACHINE LEARNING VALUE - PERMIAN BASIN OPERATOR CASE STUDY

11:30 Understand How A Leading Operator In The Permian Has Incorporated Machine Learning & A.I. To Increase Operational Efficiency Reduce Costs And Achieve Employee Buy-In

  • Get to grips with the implications of implementing Machine Learning and AI on your business processes and on your workforce
  • Using AI for real-time insights and predictive operations to maximize efficiency
  • Overcoming concerns over A.I. taking jobs within your organization
  • Assess ways to see repeat failure patterns using Machine Learning

Bill Fairhurst, President, Riverford Exploration, LLC

12:00 Question & Answer Session

HOW TO GET OPTIMAL PRODUCTION RATES UTILIZING MACHINE LEARNING

OPTIMIZING PRODUCTIVITY CASE STUDIES

CASE STUDY 1 - SUBSURFACE OPTIMIZATION

12:10 Optimizing The Design Of Hydraulic Fracture Treatment To Gain The Best Economic Return On The Well Through Machine Learning

How Intelligent Software Systems Can Support Explorations, Reservoir Development And Seismicity Management Including:

  • Improving hydraulic fracturing performance and interpreting fracture geometry based on the data
  • Utilizing Machine Learning to formulate a three-dimensional analysis of interactions between hydraulic and natural fractures
  • Dynamic fracture volume estimation using flow-back data analysis

Christina Bernet, Sr. Reservoir Development Engineer, Bonanza Creek Energy

12:40 Question & Answer Session

12:50 Networking Lunch In The Exhibition Area

CASE STUDY 2 - COMPLETIONS AND WELL SPACING

1:50 Understanding How Machine Learning Can Optimize Completions Based On The Data In Order To Make Decisions On How Tightly Wells Can Be Spaced

This session delivers key completions specific lessons in order to obtain maximum value from your Machine Learning program and to demonstrate the value of data science and Machine Learning to optimize and improve completions decisions;

  • Learn how completions can be optimized based on data sets
  • Alternatives on how to frac wells once they have been drilled
  • Optimal spacing based on the data before reserves start to get lost and the optimum spacing requirements needed

Clayton Burrows, Reservoir Engineer - Competitive Intelligence & Operations Support, Apache Corporation

2:20 Question & Answer Session

CASE STUDY 3 - CYCLE-TIME FOR ARTIFICIAL LIFTS

2:30 Discover How Machine Learning Can Be Implemented To Establish The Optimum Frequency And Cycle-Time Of Artificial Lift Equipment: Future Trends & Cost Effective Innovations To Deliver Improvement & Operational Efficiency

  • Analyze production and well data to calculate the optimum time
  • Understand how to automate the opening of valves or auto start compressors
  • Use AI to calculate how many times a day you need to run your artificial lift

3:00 Question & Answer Session

CASE STUDY 4 - ARTIFICIAL LIFT ANOMALY DETECTION

3:10 Examine The Ability Of Machine Learning To Predict Anomalies On Artificial Lift Equipment To Pre-Empt Failure And Eliminate Unplanned Downtime:

A Cross Section Analysis Including Plunger, Gas, ESP, Rod Pump & Free Flow

  • A look at how equipment failures can be predicted using Machine Learning intelligence
  • Apply Machine Learning to detect scale, paraffin and corrosion build up on your artificial lift
  • Understand the ability of Machine Learning to predict anomalies

3:40 Question & Answer Session

3:50 Afternoon Refreshments In The Exhibition Area

PREDICTIVE MAINTENANCE TO ENSURE ASSET RELIABILITY TO SAVE COSTS

The last two sessions of the day look at utilizing Machine Learning to build a complete picture of well conditions by integrating critical production data including wellhead, line pressures, separators, compressors, storage tanks, and chemical tanks, to make informed and timely strategic and operational decisions and ensure asset reliability before failures occur;

ENSURING ASSET RELIABILITY

4:20 Leveraging Cutting-Edge Machine Learning Techniques And Techonolgy To Provide Predictive Maintenance Capabilities To Eliminate Equipment Failure Ahead Of Time

  • Learn from the data to understand operational states and failure modes of assets and uses this intelligence to warn of impending failures
  • Implementing Machine Learning technology to maximize the value of oilfield production assets to optimize production, find asset inefficiencies, and assure peak production recovery
  • Getting to grips with the diagnostic and predictive capabilities of Machine Learning to enable you to speed up root-cause analysis, define optimal processes and configure early warnings to monitor production

Graeme Gordon, Senior Geological Advisor, Hess

4:50 Question & Answer Session

PREDICTIVE MAINTENANCE STRATEGIES

5:00 Developing A Predictive Maintenance Strategy To Boost Production Rate & Revenue

Understand How A Predictive Maintenance Strategy Can Alert Operators To The Opportune Moment For A Graceful Shut Down For Maintenance Before Catastrophic Equipment Failure Occurs

  • Know exactly when to perform maintenance and on which equipment
  • Use the technology to detect failure tendencies and find failures before they cause a problem.
  • Understand which kind of machine learning models are the best fit for your organization

5:30 Question & Answer Session

5:40 First Day Of The Conference Ends With The Chair's Closing Remarks

Danny Durham, Director Global Upstream Chemicals, Apache Corporation

5:40 - 7:00 Networking Drinks In The Exhibition Area

DAY 2 AGENDA - THURSDAY AUGUST 29, 2019

DAY 2 Offers A Deep Dive Into Currently Available Technologies, And The Technology On The Horizon That Are Right For The Scale, Budget & Objectives Of Your Organization.  It Provides You With A Technology Road Map For The Short- And Long-Term In Line With Your Business Objectives

 > Getting Value Out Of The Data > Upgrading Legacy Systems > Understanding Machine Models > Improving Well Design Decisions

9:00 Chair's Opening Remarks

Danny Durham, Director Global Upstream Chemicals, Apache Corporation

TECHNOLOGY DISCUSSION PANEL

9:10 Learn How To Generate Real-Time Insights With AI, Machine Learning & Dashboards To Get The Most Out Of Your Existing & Future Operations: What Are The Leading Technologies Out There?

  • Economic considerations when going with a certain technology: Is it right for the scale of your organization?
  • Using Machine Learning for advanced process control in order to get the most out of your existing operation
  • How dashboards and machine learning can obtain a real-time operational pictures and rolling forecasts of your remote facilities
  • Understand the power of AI and how it can bring latent, hidden and unstructured knowledge to your fingertips
  • Showcasing cost and economics for new Machine Learning technology

Dr. Yuxing Ben, Staff Data Scientist, Anadarko
Clayton Burrows, Reservoir Engineer - Competitive Intelligence & Operations Support, Apache Corporation

9:50 Question & Answer Session

CREATING ORGANIZATIONAL DIGITAL COMPETENCE

SKILLS TRANSFORMATION WITHIN THE ORGANIZATION

10:00 Practical Steps On Getting Mind-sets And Skill-sets At The Right Level To Harvest The Benefits Of Applied A.I. & Machine Learning Across Core Operational Areas To Increase Production And Overall Efficiency

  • Talent and approaches for finding and executing high level opportunities
  • Non-data scientist roles needed to get big returns from Machine Learning
  • Training your existing workforce in data science vs. augmenting their efforts with data science professionals
  • Scaling data science across the enterprise

Colleen Graham, Data Science Program Manager GOM, Chevron

10:30 Question & Answer Session

10:40 Morning Refreshments In The Networking & Exhibition Area

CHANGE MANAGEMENT

11:10 Assessing New Opportunities For Change Management To Optimize The Impact Of Automation On Business Process

  • Achieving employee buy in
  • Using design-based thinking to pinpoint which actions will drive the best business impact
  • Tackling the exodus of talent: Using technology to capture knowledge of existing assets

Ryan Stalker, Change Management and Organizational Development, Williams

11:40 Question & Answer Session

UPGRADING LEGACY SYSTEMS

UPGRADING LEGACY INFRASTRUCTURE SYSTEMS

11:50 Analyzing How To Get Past The Old System To Connect To Your New Equipment & Application: Mixing Data Of Different Quality And Fidelity

  • Examine the cost of maintaining a legacy systems which can quickly become burdensome to smaller operations trying to keep budgets under control
  • The value of smart devices and edge intelligence - what have we learned so far?: Learn how to use Edge technology and provide better data across your enterprise
  • Dealing with uncertainty with the data

Luis Zorilla, Staff Electrical Engineer,ConocoPhillips

12:20 Question & Answer Session

12:30 Networking Lunch In The Exhibition Area

UNDERSTANDING MACHINE LEARNING MODELING

MACHINE MODELS

1:30 Understanding How To Get Your Machine Learning Models Started Enabling You To Build Complex Models On The Data Collected Leading To Better Decisions

  • Developing predictive models in terms of maintenance schedule
  • Dealing with uncertainty in the data
  • Monte Carlo Simulations - Running numerous simulations by perturbing your input to get a sense of how the output changes

David S. Fulford, Sr. Staff Reservoir Engineer & Leader Of Subsurface Analytics, Apache Corporation

2:00 Question & Answer Session

OPTIMIZING WELL DESIGNS USING MACHINE LEARNING

IMPROVING WELL DESIGN DECISIONS

2:10 How To Utilize AI And Machine Learning Software To Create Profitable Wells And Enhance The Overall Digital Well Planning Process

  • Comparing data patterns of a current well with previously drilled wells for analysis on optimal decision making
  • Establishing transparency and control over the data that is being ingested for analysis and the machine learning algorithms
  • Alternative on how to frac wells once they have been drilled

2:40 Question & Answer Session

2:50 Afternoon Refreshments In The Networking & Exhibition Area

GETTING OPTIMAL VALUE OUT OF YOUR DATA

COLLECTING, USING & PROFITING FROM THE DATA

15:00 Deploying Machine Learning To Reduce OpEx And Increase Performance:

Gathering high quality data in real-time to enable early detection of incidents, deterioration or deviation from normal operating parameters

  • Envisioning and implementing more effective and streamlined processes
  • Automating your operations for more reliable and quicker project execution
  • Defining the right digital 'toolkit' for your company's objectives

15:30 Question & Answer Session

15:40 Morning Refreshments In The Networking & Exhibition Area

MANAGING THE DATA

16:10 Implementing New Technologies To Manage The Data After Different Sources Have Been Integrated:

Understanding The Full Spectrum Of Possibilities On How To Use Production, Drilling & Completions Data

A Look At Feature Engineering And Understanding The Process Of Using Domain Knowledge Of The Data To Create The Features Needed To Make Machine Learning Algorithms Work

  • Extracting features from raw data and transforming them into data that can be used for a Machine Learning model
  • 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 models and the problems needed to be solved
  • Selecting the right technology that can create a secure data environment

16 :40 Question & Answer Session

16:50 Chair's Closing Remarks

Danny Durham, Director Global Upstream Chemicals, Apache Corporation

17:00 End Of Machine Learning & AI For Upstream Onshore Oil & Gas 2019

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