LOGO

Request for services ? We are here to help you!

contato@kognitus.com.br

Contact us

Challenges for risk assessment using Physics-based simulations

In the O&G, Energy, and Mining industries, current risk assessment methods using physics-based simulations rely on a simplistic best- and worst-case scenarios approach or Monte Carlo derived methods requiring thousands of simulations.
Moreover, to keep up with the evolution of knowledge through a project’s life cycle, simulation methods require a very labor-intensive effort, which can hamper updating the models with the most recent data and information

Alterna
Alterna_figura adicional2

Coupling AI and Physics-based simulations for more accurate and efficient risk analysis

ALTERNA® can be coupled to the best-in-class physics-based simulators to provide quantitative risk analysis in a time frame that remains consistent with operational projects, while preserving accuracy.
The process starts with defining significant uncertainties by associating the user’s expertise and ALTERNA®’s expert systems. Next, state-of-the-art techniques sample the uncertain space, generating a learning database of physics-based simulation results that support the training of ML algorithms. Then, cutting-edge statistical tools interactively produce accurate risk and sensitivity analyses

A new milestone for risk assessment in the O&G and Energy industries

ALTERNA® module for O&G exploration: comprises connectors to state-of-the-art simulators (Petromod and TemisFlow) and expert systems to focus on significant geological uncertainties. ALTERNA® provides robust regional risk assessment using few simulations compared to other approaches. Through a full sensitivity analysis and a systematic quantification of input uncertainties, ALTERNA® delivers more efficient portfolio management and helps prioritize efforts to further de-risk the petroleum systems.

ALTERNA® module for reservoir simulation: aims to build a digital twin of geological reservoirs for petroleum and geothermal production, and CO2 and natural gas storage. Based on state-of-the-art multidimensional machine learning methods, the new module paves the way to flexible, robust, and accurate assisted history matching (model calibration), field development, and production optimization.

Visit our Case Studies section and contact us to discuss your technical and business challenges.