AquaNRG develops diagnostic and predictive models, data analytics, and digital screening tools for carbon capture, utilization, and storage (CCUS) by integrating biogeochemical reactions and reactive transport modeling. Their technology addresses the need for accurate geochemical modeling to comply with EPA Class VI well requirements and optimize CO2 storage strategies.
Funding
Funding not disclosed
Founders
Product
Problem
Carbon capture, utilization, and storage (CCUS) projects require accurate geochemical modeling to meet EPA Class VI well requirements and optimize CO2 storage. Existing solutions lack comprehensive integration of biogeochemical reactions and reactive transport modeling, hindering effective prediction and management of long-term CO2 storage performance.
Solution
AquaNRG offers next-generation digital screening, diagnostic, and predictive tools for CCUS, built on interdisciplinary science and AI. Their solutions integrate biogeochemical reactions and reactive transport modeling to provide accurate geochemical modeling, helping clients design and optimize effective CCUS strategies. AquaNRG's tools facilitate compliance with EPA Class VI well requirements and ISO 27914 guidelines, ensuring the long-term performance and safety of geologic CO2 storage. The platform incorporates a comprehensive kinetics and thermodynamic database for biogeochemical reactions, enabling detailed analysis of fluid-rock interactions under various conditions.
Target Audience
AquaNRG's primary customers are organizations involved in carbon capture, utilization, and storage projects, including energy companies, industrial facilities, and research institutions.
Features
- CCUS-BATCH: Web application and on-premise software for simulating batch (0D) biogeochemical reactions, integrable with existing reservoir simulators via API.
- CCUS-RTM: Web application and on-premise software for building 1D, 2D, and 3D reactive transport models (RTMs) of coupled fluid flow, solute transport, and biogeochemical reactions.
- High-performance computing and massively parallel multi-node architecture in the cloud.
- Deep learning modeling for fast prediction of CO2 storage problems, including a pre-trained neural network model for simulating fluid flow and solute transport.
- Multiscale reactive transport modeling to predict physical-chemical trapping mechanisms in geologic CO2 storage.
- Capabilities include multiphase flow models, solute transport models, and reactive transport models with various processes (adsorption, dissolution, microbially-mediated reactions).
- Integration of kinetics and thermodynamics of microbiological pathways, synthetic or natural, into CCUS modeling.
- Cement-brine-CO2 models for wellbore cement integrity analysis.