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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

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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.

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