Big Data and Post-Graduate Training in Upstream Oil & Gas

The future for subsurface analysis and graduate training: a call to universities to respond and adapt to industry needs.
This article appeared in Vol. 17, No. 2 - 2020


Upstream Oil & Gas: Big Data and Post-Graduate Training

Today’s upstream oil and gas subsurface geoscience and engineering technical communities are facing a new world as Artificial Intelligence (AI), data analytics and big data become an increasingly core part of the subsurface workflow. Not since the late 1980s have such challenges presented themselves to upstream oil and gas. Successful companies will be those that are flexible and adapt to the changes, which will also have a knock-on effect on universities that have developed petroleum-related geoscience and subsurface engineering graduate and research programs. 

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The challenges are exacerbated by the impact of the climate emergency, with some institutions shifting consciously away from carbon-related investment and research. Many larger company strategies are also moving away from coal and oil to cleaner gas and renewable fuels with no damaging emissions. All of this is being accompanied by reduced exploration investment and increasing focus on improving recovery efficiency in existing fields, providing a further dimension to the challenge. 

A Dramatic Challenge

The last 30 years has seen a growth and proliferation of petroleum-focused subsurface master’s training programs. These developed in response to increased demand and a conscious shift of companies to needing broader-based generalists with master’s level education. In parallel with this the number of sponsored applied Ph.D. studentships, which had flourished during the 1980s, dropped significantly. At the same time demand for specialist subsurface geological and engineering skills flattened out and, in some areas (e.g. biostratigraphy), began to decline. These changes fundamentally shifted the focus and balance of applied subsurface postgraduate education at many universities and institutions. 

Today we are facing what promises to be an equally dramatic challenge that is likely to have far-reaching effects on those universities that do not respond and adapt to industry needs. AI and data analytics are leading to profound changes to the nature and scale of the technical subsurface workforce in many operating and service oil and gas companies and as a result, on future employment demand. The growth in automation underpinned by data analytics and AI is already reducing demand for graduate level entries in many of the medium to large-size companies that have traditionally been the source of much employment in the recent past. The data analytics revolution is leading to a change in working practices with less need for dedicated asset teams constantly upgrading and rebuilding subsurface models and descriptions throughout the life of a field. The future looks very different, with much smaller asset teams and more automated workflows and model upgrades, where interventions and new work will be driven by deviations outside an uncertainty range defined by an asset’s data-driven models and descriptions. Larger companies will likely evolve to have small subsurface asset teams supported by larger centralized groups comprising experts, specialists and broadly experienced practitioners, organized to react to any alarms or deviations from a model-based subsurface description and reservoir performance prediction. This organizational model will likely be replicated by the major service providers, supporting smaller operating companies.

Rapidly Changing Demand

All of the above is leading to a profound change in the graduate education supply model. The numbers registered on master’s programs will probably continue to decline or at best level out for those well-recognized and established programs. Some programs may disappear completely if they cannot evolve and adapt to changing needs. Commensurately there will likely be increased demand for Ph.D. level recruitment in the areas of subsurface description and reservoir performance prediction. Models like the UK’s CDT (Centre for Doctoral Training) might evolve to produce more business-oriented and commercially-savvy students complementing their research expertise. In the immediate future, key skills gaps will be in subjects such as geomechanics and reservoir scale structural geology, as well as digital petrophysics, rock description and modeling. 

Successful universities will evolve their programs and offers to address the rapidly changing demand. A wider range of subsurface energy industry systems will become part of the mix and drive demand, including topics such as geothermal, carbon capture, ultilization and storage and radioactive waste disposal. Many of the skills needs in these non-petroleum areas are generic, being related to subsurface description and modeling. Partnerships and collaborations between universities and industry similar to the CDT model offer a possible route to sustained success.

Collaboration Needed

It is really important that higher education institutions adapt and evolve to these changing demands or many programs will disappear and the pipeline of appropriately skilled graduates will not meet the demand from a broader subsurface energy systems agenda. The next five years promise to be a fascinating time as these changes begin to take effect. Collaboration and partnerships are not an easy working model for what are often naturally competitive and reluctant-to-change higher education establishments and those more conservative oil and gas companies, but some universities are already beginning to adapt and evolve, and many larger companies are recognizing the changing organizational models and their impact.

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