2 interesting days about the digital subsurface

2 interesting days about the digital subsurface

#DigEx 2019 kicks off on 30th and 31st of january in Oslo.

The program for #DigEx 2019 is getting finalized and is promising 2 interesting days with talks about the digital subsurface. Join us and sign up here!

The first day starts off with “setting the stage”. Speakers from Dig Science, Arundo Analytics and Pandion Energy are challenged to separate the hype around digitalization and artificial intelligence from reality. We also question what the enablers are for a culture change in an organization.

In the” digital perspectives” session, we will hear from authorities and companies in the digital transformation phase on their experiences, learnings and the way forward towards their digital vision. The NPD, Konkraft, Accenture and Google Norway have agreed to share their experiences.

In “enabling data sharing and new digital workflows” we hope to learn about the opportunities and challenges that come with the digitalization of data. New workflows are enabled across the value chain as a result of digitilization and sharing data – across companies and country borders. More data leads to a deeper understanding and a better foundation for analytics and machine learning – providing us with richer insight and better decision support. There will be presentations from Equinor, Cognite, Digital Norway and Bluware. The first day will be concluded by a panel discussion.

On day 2, we look at the bigger picture where Microsoft will give us some “digital perspectives” on the world and society while Schlumberger is more focused on exploration and production. After this we zoom in on the workflows in the E&P business with a session on “Data management and visualization” with contributions from Geodata, Cegal and KADME. This is followed by case studies of “automated interpretation” by Geo Teric, Lundin and Kalkulo, Ikon Sciences, Earth Science Analytics and a demo by RagnaRock Geo.

More “subsurface” applications of machine learning and artificial intelligence are shown by Aker BP and Cegal, Schlumberger, Hoolock Consulting and the NPD. In the end also, “oil companies” get the chance to share their digital strategies with the audience and to show how far they have come with digitalization.

#DigEx ProgramDAY 1

SETTING THE STAGE: EXPONENTIAL & DISRUPTIVE
“Visions & Frustrations” Kristin Dale & Per Avseth, Dig Science
Demystifying AI Ellie Dobson, Arundo Analytics
Partnering with AI. The Augmented Geoscientist, Fiction or Future? Kine Johanne Årdal, Pandion Energy
DIGITAL PERSPECTIVES
Digitalization within the Oil and Gas “Eco System” – Overall ambitions, status and challengesWalter Qvam, Konkraft
NCS 2016Alf Veland, NPD
Accenture Technology VisionJyoti Sharma, Accenture
Disrupt or be DisruptedJan Grønbech, Google Norge
ENABLING DATA SHARING & NEW DIGITAL WORKFLOWS
Digital Subsurface: Disrupting the WorkflowsTina Todnem, Equinor ASA
The pie that gets bigger by sharingVidar Furuholt, Aker BP & Per Arild Andresen, Cognite
Helge Dag Jørgensen, Digital Norway
Diederich Buch, Bluware
A Use-case of a Cross-vendor Reservoir Model Enrichment Workflow made Simpler and More Reliable with Industry-developed Data Transfer StandardsDavid Wallis, Energistics Consortium
#DigEx Program DAY 2
DIGITAL PERSPECTIVES
AI and the World – AI’s impact on business, leadership and societyKimberly Lein-Mathisen, Microsoft Norway
A New Data Ecosystem that Empowers a Cognitive Environment for Exploration & ProductionGuido van der Hoff, Schlumberger
DATA MANAGEMENT & VISUALIZATION
Cegal
Erlend Kvinnesland, Geodata
Insight Engines & Machine Learning PipelinesJesse Lord, KADME
AUTOMATED INTERPRETATION
The application of deep learning for more accurate fault delineation James Lowell, GeoTeric
RacknaRock – DemoMarkus Halvorsen, RagnaRock Geo
A data-driven workflow for 3D automatic seismic interpretationAina Bugge, Kalkulo AS, Lundin Norway, UiO – dept. of geoscience
Incorporating Uncertainty in Automated Seismic Interpretation: Geobody VolumetricsSteve Purves, Earth Science Analytics
Deep QI: A Machine Learning Approach to Quantitative Interpretation Ehsan Naeini, Ikon Science
SUBSURFACE
Using machine learning to predict pressure changes and thicknesses in a chalk reservoirAkerBP & Cegal
AI Automation and cloud compute enabling faster turn-arounds in E&P life cycleSteve Freeman, Schlumberger
Multiple seismic attributes and machine learning improve geological understandingTim Gibbons, Hoolock Consulting
1700 exploration wells: Machine learning from a petabyte of organic content images to answer the key questions – Age? Environment? Oxic or anoxic?Robert W. Williams, NPD
OIL COMPANY PRESENTATIONS
Cuillin projectDavid Wade, Equinor
Repsol – exponential and disruptive…Francisco Ortigosa, Director Geoscience Technology

 

 

 

 

X