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


Coupling AI and Physics-based simulations for more accurate and efficient risk analysis
ALTERNAÂŪ can be coupled to the best-in-class physics-based simulators to provide quantitative risk analysis in a time frame consistent with operational projects while preserving accuracy.
The process starts with defining significant uncertainties. Next, state-of-the-art techniques sample the uncertain space, generating a database of simulation results that support the training of machine learning 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
ALTERNAÂŪ module for Basin and Petroleum System Modeling (PSM)
The ALTERNA PSM module comprises connectors to state-of-the-art simulators (Petromod and TemisFlow) and expert systems to focus on significant geological uncertainties. By applying a full sensitivity analysis and systematically quantifying input uncertainties, ALTERNA PSM helps prioritize efforts to further de-risk hydrocarbon charge, delivering more efficient exploration risk assessment and portfolio management.
ALTERNAÂŪ module for Forward Stratigraphic Modeling (FSM)
The ALTERNA FSM module comprises connectors to FSM simulators. By applying a complete sensitivity analysis and a systematic quantification of input uncertainties, ALTERNA FSM delivers a robust assessment of reservoir presence and quality, providing the basis for more efficient decisions in E&P.
ALTERNAÂŪ module for Reservoir Simulation
The new module under development 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, it will enable flexible, robust, and accurate assisted history matching (model calibration), field development, and production optimization.