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Janet Watson Meeting 2018: A Data Explosion: The Impact of Big Data in Geoscience

27 February - 01 March 2018
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Event type:
Conference, Workshop
Organised by:
Geological Society Events, 2018 Year of Resources, Geological Remote Sensing Group, Geoscience Information Group, Petroleum Exploration Society of Great Britain (PESGB), Energy Group
The Geological Society, Burlington House
Event status:


Conference Partners

        GIG logo                       Petroleum Group

The rise of ‘Big Data’ has been characterized by a rapidly increasing availability and diversity of data that will play a role in shaping the future of Geoscience Research and the Hydrocarbon Industry. The Geoscience community has been slow to embrace the Big Data technologies that are revolutionizing other sciences such as the pharmaceutical industry and medical research. Where advances in data acquisition and interpretation technologies are being made in academic Geoscience, progress in uptake has been hampered by unstructured data, stored in silos.

This three day meeting brought together early career geoscientists with leading industry and academic experts to discuss the opportunities and challenges of Big Data and showcase advances in data collection and interpretation technology. It presented an opportunity to learn and collaborate between Geoscience and Computer Science on the subject of Big Data. This was an excellent forum for networking and an opportunity for graduate students and young professionals to present their research. The conference offered more experienced hydrocarbon geoscientists new research, ideas and concepts and the chance to add their experience to a panel discussion. 


Conference themes

  • Opportunities and Challenges associated with Big Data in Geoscience
  • Data standards, storage and security challenges
  • Novel Approaches to data collection
  • Technology Advances in interpretation and reservoir characterization, e.g. virtual geosciences.
  • The future role of Big Data in academic and industry Geoscience

The programme included virtual fieldtrips and software demonstrations of technology advances in Big Data on a variety of scales, from regional subsurface interpretation, to virtual fieldtrips and virtual outcrops, with special sessions on automated interpretation and artificial intelligence.

A panel discussion on ‘The Future of Big Data’ was held at the close of the second day.

Keynote Speakers

Liz Wild (Shell)

Dr Satyam Priyadarshy (Halliburton)

Nick Richardson (OGA)

Ed Parsons (Google)

Garry Baker (BGS Group)

Steve Garrett (Chevron)

John Thurmond (Statoil)

Eirik Larsen/Chris Jackson (Earth Science Analytics)

John Howell (University of Aberdeen)

A full programme can be downloaded from the 'Downloads' box on the right.


  • Nicole Duffin (Shell UK)
  • Caroline Gill (Shell UK)
  • John Howell (University of Aberdeen)
  • Helen Smyth (Halliburton)
  • Paul Duller (Tribal)


Day 1

KEYNOTE: Pixels in the cloud
Ed Parsons (Google)

GeoSocial: Exploring the usefulness of social media mining in the applied natural geohazard sciences 
Emma Bee (British Geological Survey)

Building Data Science Capability 
Ed Evans (NDB Upstream)

Getting Legacy BGS Stratigraphic Data Ready for the Big Data Revolution 
Mike Howe (British Geological Survey)

KEYNOTE: Data science for earth science – perspectives from industry 
Steve Garrett (Chevron)

Disparate E&P Big Data Impose Non-Desperate Machine Learning Methodologies 
Keith Holdaway (SAS Global O&G Domain)  

Big data in the Geoscience: A portal to physical properties

Mark Fellgett (British Geological Survey)

KEYNOTE: The Virtual Geoscience Revolution
John Howell (University of Aberdeen)

Virtual Fieldtrip - Zagros 
Richard Jones (University of Durham)

Putting data at your fingertips: Utilising data analytics in E&P Information Management
Christopher Frost (DataCo Global Limited)

Accessing Knowledge in Geoscience Text using Natural Language Processing
Richard Jones (University of Durham)

Day 2

KEYNOTE: The Role of Data Regulating the UK Oil & Gas Industry
Nick Richardson (OGA)

Over 120 years worth of hydrocarbon exploration, an example of how legacy data can address todays challenges.
Mark Fellgett (British Geological Survey)

For open source in situ stress data see:  Heidbach, Oliver; Rajabi, Mojtaba; Reiter, Karsten; Ziegler, Moritz; WSM Team (2016): World Stress Map Database Release 2016. GFZ Data Services, doi:10.5880/WSM.2016.001.

Digital Transformation in the North Sea
Stephen Roberts (The Oil and Gas Technology Centre)

KEYNOTE: Digitalization and Data in Field Development
Liz Wild (Shell)

Virtual Glaciers and Glaciated Landscapes
Derek McDougall (University of Worcester)

Improving access to UKCS Petrotechnical Data – the next step in a 20 year journey
Daniel Brown (Common Data Access Limited)

The worldwide field course: use of 3D outcrop imagery in training
Gary Nichols (RPS)

KEYNOTE: Big Data and the British Geological Survey
Garry Baker (British Geological Survey)

IGCP 648 Efforts to Compile Structured Data for Palaeogeographic Studies: some lessons learned
Bruce Eglington (University of Saskatchewan)

Data Mining and Visualization of Detrital Zircon Data: Assessment of Palaeogeographic and Geodynamic Setting Using Data from Laurentia
Dean Meek (University of Saskatchewan)

Day 3 

KEYNOTE: When Failure (a lot of failure) Becomes an Option - Machine and Deep Learning on Seismic Data 
John Thurmond (Statoil)

Big data - A boundaryless future?
Rhian Burrell (Osokey)

How machine learning systems can extract more qualified information from seismic acquisition and processing reports.
Henri Blondelle (AgileDD)

KEYNOTE: Machine Learning Assisted Petroleum Geoscience: Can a computer learn to map stratigraphic architecture and reservoir quality by training on data?
Eirik Larsen/Chris Jackson (Earth Science Analytics)

Making the case for Big Data petrography
Jenny Omma (Rocktype Ltd)

Ensemble Learning Approach to Lithofacies Classification Using Well Logs
Didi Sher Ooi (University of Bristol)

Python and AI basics coding workshop for Geoscientists

The objective of this short course was to demonstrate the flexibility and ease of coding in Python and AI ,coupled to the industry standard RokDoc GeoPrediction software, enabling the user to implement their own desired algorithms in Python. We started by teaching some basic well log operations using Python and the external interface to RokDoc. Then we extended this application to basic machine learning algorithms for facies classification using freely available Python libraries. The course was for entry level practitioners and involves hands on coding experiments, hence having some Python skills is an advantage but not essential. We were aiming for early stage professional Geoscientists and Engineers in line with conference objectives.

The instructors were Russell Taylor and Dr Ehsan Naeini of Ikon Science. 

Date: 1st March 2018 10-AM to 3PM.

Venue: Ikon Science, 1 The Crescent, Surbiton, London, KT64BN

Event sponsors