Koloma Adopts GeoComputing RiVA for Hydrogen Exploration

Koloma Adopts GeoComputing RiVA for Hydrogen Exploration

Koloma Inc., a natural hydrogen exploration company, has selected GeoComputing Group’s RiVA private cloud platform to support its data-driven exploration and analysis workflows. The integration aims to address systemic challenges in energy exploration, including massive data volumes and high deployment costs. By leveraging high-performance computing, Koloma intends to identify and derisk areas with natural hydrogen potential globally. This partnership focuses on utilizing high-throughput architecture to accelerate the processing of geologic research and data analysis required to unlock this primary energy source.

RiVA Platform Integration for Natural Hydrogen Analysis

Koloma implemented the RiVA platform to overcome specific operational hurdles such as poor system performance, complex environments, and dispersed teams. The platform's high-throughput architecture is designed to accelerate processing times, enabling tasks that previously required days to be completed in hours. According to Jerome Bellian, PhD, VP of Emerging Plays at Koloma, the technology allows the company to apply traditional oil and gas data and applications to the emerging natural hydrogen industry to make informed decisions quickly.

The RiVA platform provides a specialized geoscience computing environment, removing the need for Koloma to build and support its own costly infrastructure. Richard Haynie, Head of IT at Koloma, noted that the platform allows a lean IT team to remain focused on business enablement while scaling operations. GeoComputing currently has more than 200 petro-technical applications deployed on RiVA, including proprietary solutions and tools from industry service providers, which are tested and integrated to optimize the end-user experience for exploration and production (E&P) activities.

Infrastructure Resilience and E&P Workflow Efficiency

The RiVA private cloud is positioned as a high-performance alternative to standard on-premise or public cloud offerings. Beyond accelerating day-to-day production workflows, the platform includes a fully integrated disaster recovery solution to maintain business continuity and data resilience. John Creevan, CEO of GeoComputing, stated that the platform is intended to minimize risks and costs while maximizing the accessibility and output of fuel sources, whether traditional or alternative.

The deployment focuses on the identification and harnessing of subsurface natural hydrogen, which Koloma describes as a sustainable energy source requiring minimal land, water, and energy for extraction. By utilizing the RiVA landscape, Koloma aims for a superior return on investment through improved exploration outcomes. The platform's ability to handle massive data volumes and provide direct engineering support for both IT and geoscientists is central to the execution of Koloma's data-driven strategy for identifying untapped hydrogen resources in the United States and internationally.

Key Takeaways

  • Koloma is using the RiVA platform to reduce data processing times from several days to a few hours.
  • GeoComputing's RiVA platform currently supports over 200 deployed petro-technical applications for the E&P sector.
  • The RiVA private cloud includes a fully integrated disaster recovery solution to ensure operational data resilience.

EnergyInsyte's Take

In our view, this partnership signals a critical shift in how emerging energy sectors leverage legacy oil and gas expertise. By applying traditional E&P computing workflows to natural hydrogen, Koloma is effectively reducing the "innovation tax" typically associated with new energy sources. This suggests that the viability of natural hydrogen will depend less on new software and more on the ability to scale existing high-performance computing (HPC) architectures. We believe the move to a private cloud over public options reflects a strategic priority for data sovereignty and specialized performance that generic cloud providers cannot currently match for complex geoscience workloads.

Source: BUSINESSWIRE

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