Hackathon Targets Geothermal Energy
Registration is open for the SPE Europe Energy GeoHackathon, which will be held in October and November. It will be preceded by 4-week online bootcamp sessions on data science and geothermal energy, which will begin on 2 October.
The SPE Europe Energy GeoHackathon aims at educating and disseminating knowledge on how data science applications can support geothermal energy developments and drive the energy transition. The event is organized by volunteers from different SPE sections in Europe and from International Technical Sections moved by the desire to make the energy transition happen in a “datafied” and sustainable way.
The hackathon will be preceded by 4-week online bootcamp sessions on data science and geothermal energy, which will begin on 2 October. Data scientists and industry experts will deliver weekly sessions to equip participants with the relevant technical knowledge to understand the challenge be ready for the hackathon.
Seismic inversion and facies classification are two important techniques to understand the subsurface and reservoir characteristics for geothermal energy development.
The main challenges in seismic inversion are the nonuniqueness of the solution, the low-frequency component in the data, and the prior information required. Often, multiple possible solutions can fit the same data, and the low-frequency content and prior information are often lacking. Facies classification in seismic data, on the other hand, involves grouping similar rock units on the basis of their seismic reflection pattern and underlying physical properties. The challenge that rock properties can vary significantly within a single facies, often below seismic resolution and masked by noise, making it difficult to accurately define and differentiate between them. Both techniques require careful interpretation and validation and a thorough understanding of the geological setting in order to make an accurate estimate of the formation properties.
Machine learning (ML) has made significant steps in establishing the relation between seismic data and the causative rock parameters. ML can predict at great speed causative rock parameters from seismic data, but much ambiguity remains.
The hackathon challenge involves ML seismic inversion of acoustic impedance for identifying reservoir properties for geothermal deployment. Bonus questions involve ML facies classification and ML seismic interpretation of horizons or units.
Data from the SCAN 2D seismic campaign will be used for this hackathon.
A data-driven prediction and ML classification of reservoir properties from seismic data and well logs can enable solutions for
- Interpreting large volume data sets obtained from seismic campaigns with computationally efficient work flows
- Identifying new features and facies using unbiassed artificial intelligence work flows contributing to geothermal field developments
- Speeding up the interpretation of the seismic data