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Digital geoscience

Thanks to our people's skills, combined with the supercomputer, we can learn about subsoil in more and more detail.

Technology

On hardware with unique functions like our supercomputer, we can use highly advanced software to study subsoil, through a virtual ecosystem of geology and geophysics that can explore and model fields at any moments. Uniting scientists and engineers we have developed algorithms for seismic data processing, which can build 3D geological models in higher resolutions and much more quickly. To increase efficiency we automatise the interpretation phase and integrate seismic data with that on the chemical and physical properties of rocks. Another resource we are using is machines that can independently learn from experience and provide solutions, through machine learning and artificial intelligence. We want to apply these technologies to reconstructing stratigraphic sequences and developing a virtual assistant that can automatically describe the features of a potential basin. Lastly, by reconstructing the geological history of sedimentary basins, we can simulate the generation and migration processes of hydrocarbons to define the most interesting areas for build-ups.

Context

For energy companies, exploration is an increasingly demanding task. Onshore fields are mostly already well known and producing, so new resources have to be found offshore. But working on the seabed is complicated, relying on greater investment to manage risks. That is all the more true once you consider that the areas of operation are increasingly found in isolated areas without supporting infrastructure. To optimise the times and efficiency of exploration work we have come up with new algorithms for processing seismic data and recreating subsoil sequences very quickly and precisely. Furthermore, developing digital geoscience allows us to simulate the interactions of fluids with rocks of different sizes, from well to basin. This provides us with a representation of the subsoil that lets us reduce the number of operations to identify and produce hydrocarbons. Fewer operations means lower costs, time saved and fewer risks, be they environmental, industrial or financial. This is how we discovered Zohr in Egypt and Coral in Mozambique.

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Subsoil in 3D: seismic imaging - #Geopassion | Eni Video Channel

Technological challenge

The main goal of all our digital geoscience technologies is simple yet demanding: going faster. By speeding up our processing of the seismic and geological data we get from subsoil surveys, we not only shorten times of modelling fields, but also increase their resolution, because we can use new and increasingly powerful algorithms. To reach this goal, we focus on integrating and standardising data from the whole Upstream chain, within the special architecture of our supercomputer. From the great raft of information we gather, we develop useful simulators of our own, which rely on all the geological and geophysical skills our specialists working around the world. It is a challenging road, but thanks to these continuous efforts we can count on excellence in operations and make a difference. Both hardware and software are constantly updated to remain in step with the latest technological developments. This lets us support a “fast-track” approach in which exploration, engineering, development and supply are also improved. This speeds up the launch of projects and reduces costs and risks.

Industrial integration

Digital geoscience is one of the most powerful instruments in the service of our business. It is our interdisciplinary approach and integration of multiple industrial segments that lets us feed these technologies constantly, combined with skills in geology, IT, engineering, mathematics, physics and geophysics. Plus, as always, the experience of our technicians in the field. By pooling these staff and providing them with the supercomputer in Ferrera Erbognone, we have developed a “fast-track” approach, in which the assessment and simulation phases for fields are overlaid with the experimental and operating phases. The data gathered contributes in turn to optimising the theoretical and experimental models, allowing for continuous improvement of the whole system. One example of an application of this approach is Enhanced Oil Recovery (EOR), which is our technique of injecting fields with special fluids that separate residual hydrocarbons from the rocks in which they are embedded, to increase the proportion recovered. Already in the production phase, we screen for the best EOR technologies, estimate their yield on the basis of 3D field modelling and do analysis in the laboratories to assess the mechanisms for injecting into porous rock. All this is before the test and on-site application phases.

Environmental impact

Becoming more efficient when exploring means carrying out fewer operations in search of new resources. If you consider the environmental impact of extraction work, lowering the number of interventions in the field means more sustainability overall. Needless to say, this goes hand in hand with cuts in operating costs and the final price of the energy we put on the market. At the same time, increasing the productivity of fields lets us prolong their useful life, saving us from developing new ones. A further step forward in saving the environment is using fibre-optic sensors for monitoring production limits in real time, reducing the impact on marine fauna of machine learning, predictive scope, alternative seismic tools and high-frequency tools. One very interesting contribution of digital geoscience to the environment is in capturing and permanently storing CO2. By improving our knowledge of how sediments are buried, how gas migrates to the subsoil and the interactions of carbon dioxide in rocky systems, both homogeneous and fractured, we gain a very particular set of skills, with which we can reuse spent fields in future as great CO2 traps.