{"id":779,"date":"2023-04-06T15:44:43","date_gmt":"2023-04-06T15:44:43","guid":{"rendered":"https:\/\/globalgoodplay.com\/?p=779"},"modified":"2023-04-13T09:16:09","modified_gmt":"2023-04-13T09:16:09","slug":"using-carbon-capture-and-storage-digital-twins-for-net-zero-strategies","status":"publish","type":"post","link":"https:\/\/globalgoodplay.com\/?p=779","title":{"rendered":"Using Carbon Capture and Storage Digital Twins for Net Zero Strategies"},"content":{"rendered":"

\"ImageCO2 capture and storage technologies (CCS) catch CO2 from its production source, compress it, transport it through pipelines or by ships, and store it\u2026\"Image<\/p>\n

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CO2<\/sub> capture and storage technologies (CCS) catch CO2<\/sub> from its production source, compress it, transport it through pipelines or by ships, and store it underground. CCS enables industries to massively reduce their CO2<\/sub> emissions and is a powerful tool to help industrial manufacturers achieve net-zero goals. In many heavy industrial processes, greenhouse gas (GHG) emissions cannot be avoided in the required time frame and must use CCS solutions, as in the cement, fertilizers, and chemical industries.<\/p>\n

Scenarios for reducing GHG emissions on a global scale highlight the role of CCS in the energy mix. To reach the 2\u00b0 C objective, according to the IEA\u2019s sustainable development scenario, more than 1,000 megatonnes (Mt) of CO2<\/sub> each year would have to be stored by 2030 and a large number of CCS installations must be deployed from now until 2050.<\/p>\n

Today, around 30 large-scale installations are operational, injecting around 40Mt of CO2<\/sub> per year. The development of this technology will grow rapidly in the coming decade but this promising solution has yet to prove that it can be industrialized at an acceptable cost.<\/p>\n

One of the key challenges for keeping CCS solutions economical is the cost of proving duration and reliability of storage using numerical modeling.<\/p>\n

Traditional simulators for carbon sequestration are time-consuming and computationally expensive. Machine learning models provide similar accuracy levels while dramatically shrinking the time and costs required.<\/p>\n

In the Accelerating Climate Change Mitigation with Machine Learning: The Case of Carbon Storage post, we explored the use of machine learning for modeling CCS on 2D homogeneous reservoirs. The model was limited to a single well for CO2<\/sub> injection, and the reservoir was not permitted to have a slope.<\/p>\n

This post presents a new approach to carbon capture and storage that is substantially close to what is needed in industrial settings. It is readily available for real-world applications using NVIDIA Modulus and NVIDIA Omniverse. This CCS approach works on high-resolution, two-meter digital twin simulations over large spatial domains, handles a varying number of injection wells, and considers dipping and heterogeneous reservoirs. Most importantly, this new CCS method handles multiple wells and their interactions.<\/p>\n

Data sources for physics-ML digital twins<\/h2>\n
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\"Diagram
Figure 1. The Nested FNO architecture enables insight from multiple flows of data for digital twins, such as 3D reservoirs, ranging from coarse to fine models and pressure and saturation predictions<\/em><\/figcaption><\/figure>\n<\/div>\n

This post specifically explores CO2<\/sub> injection into 3D basin-scale dipping saline reservoirs through multiple wells over 30 years.<\/p>\n

To generate the numerical simulations dataset for this multi-well problem, we used the local grid refinement (LGR) approach for reducing computational costs while ensuring high fidelity.<\/p>\n

Figure 1 shows four levels of grid refinement around each injection applied to simulate the near-well CO2<\/sub> plume migration and pressure buildup with high resolution. At level 0 (global), coarser grid cells are used to capture far-field pressure buildup and interactions between different injection wells. Going from levels 0 to 4, cell size is reduced by 80x on the x,y dimensions and 10x on the z dimension.<\/p>\n

This work considers a wide range of practical input parameters, including:<\/p>\n

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  • Reservoir conditions:<\/strong> Depth, temperature, and dip angle<\/li>\n
  • Injection schemes:<\/strong> Number of injection wells, rates, and perforation intervals<\/li>\n
  • Permeability heterogeneity:<\/strong> Mean, standard deviation, and correlation lengths<\/li>\n<\/ul>\n

    Each of these parameters was selected to cover the most practical cases for realistic CO2<\/sub> storage sites.<\/p>\n

    As the reservoir has a variable dip angle, CO2<\/sub> plumes tend to migrate up-dip due to buoyancy. The reservoir conditions such as initial hydrostatic pressure and temperature determine CO2<\/sub> and water fluid properties. Also, due to the presence of the dip angle, the hydrostatic pressure can vary significantly around each injection well, creating various fluid properties even in the same basin.<\/p>\n

    As this work shows, thanks to these digital twins governed by physics-informed machine learning (physics-ML), industrial manufacturers can cheaply determine the following:<\/p>\n

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    • Storage capacity, maximum safe pressure buildup, and maximum injection capacity<\/li>\n
    • Calculation of CO2<\/sub> gas saturation plume and footprint<\/li>\n
    • Calculation of CO2<\/sub> migration in dipping reservoirs<\/li>\n
    • Optimal well locations and injection rates<\/li>\n
    • Wide ranges of geostatistical variability, which requires a huge ensemble of evaluations at different points in parameter space<\/li>\n
    • Ways to alleviate the cost and time of evaluations when traditional numerical methods are prohibitive<\/li>\n
    • Efficient ways to explore engineering parameter space with an openly accessible web application that provides real-time predictions<\/li>\n
    • Industrial-scale projects using the Modulus and Omniverse platforms<\/li>\n<\/ul>\n

      4D simulation<\/h2>\n

      A recent paper, Accelerating Carbon Capture and Storage Modeling Using Fourier Neural Operators, proposes a nested Fourier neural operator (FNO) architecture to predict in the domain with local grid refinements.<\/p>\n

      The computational domain of the Nested FNO is a 3D space with time:<\/p>\n

      \"D=TtimesOmega\"<\/p>\n

      In this equation, \"T\" is the time interval of 30 years and \"Omega\" is the reservoir domain. A sequence of FNO models is used to predict the 3D reservoir domain consisting of subdomains. At each refinement level, the original FNO architecture is extended into 4D to produce outputs for pressure buildup and gas saturation in the 3D space-time domain.<\/p>\n

      The input for each model includes the following variables:<\/p>\n

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      • Permeability field<\/li>\n
      • Initial hydrostatic pressure<\/li>\n
      • Reservoir temperature<\/li>\n
      • Injection scheme<\/li>\n
      • Spatial\/temporal encoding<\/li>\n<\/ul>\n

        In CO2<\/sub> storage, pressure buildup travels significantly faster than gas saturation. Because of this, an FNO model is first used to predict the pressure buildup at level 0 and to capture the global propagation and the interaction between wells. Then level 0 is fed pressure buildup predictions around each injection well to the FNO models on level 1.<\/p>\n

        Each subsequent model takes the input on the domain together with the coarser-level prediction and outputs the predictions on the finer level. By giving the coarser-level prediction to the finer-level model as an input, the boundary is provided the conditions of the finer-level subdomain.<\/p>\n

        Carbon capture and storage results over 30 years<\/h2>\n
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        \"GIF
        Figure 2. Pressure evolution over 30 years of injection<\/em><\/figcaption><\/figure>\n<\/div>\n
        \n
        \"GIF
        Figure 3. Gas saturation prediction over 30 years of injection<\/em><\/figcaption><\/figure>\n<\/div>\n

        As shown in Figures 2 and 3, Nested FNO successfully captures all the complex processes of plume migration. Overall, Nested FNO displays great generalization with small overfitting: the average saturation error for the gaseous CO2<\/sub> plume is 1.2% for the training set and 1.8% for the testing set. This accuracy is sufficient for most practical applications, such as estimating sweep efficiencies as well as forecasting plume footprints for land acquisition or monitoring program design.<\/p>\n

        The relative pressure buildup error for the training and the testing set is 0.3% and 0.5%, respectively. Like the gas saturation, you can observe small overfitting for the training and testing set. This generalization is remarkable, considering the small training data size for this high-dimensional problem. Generalizability is achieved through a novel fine-tuning technique. For more information, see the original paper, Accelerating Carbon Capture and Storage Modeling.<\/p>\n

        In addition, Nested FNO offers real-time forecasts, where the inference speed is 700,000x faster compared to the state-of-the-art numerical solver. The fast inference enables many critical tasks for CCS decision-making that were prohibitively expensive.<\/p>\n

        For example, in the paper, a rigorous probabilistic assessment for maximum pressure buildup and CO2<\/sub> plume footprint is presented. This type of assessment can reduce uncertainties in capacity estimation and injection designs. However, it would have taken nearly 2 years with numerical simulators. Using Nested FNO, this assessment took only 2.8 seconds.<\/p>\n

        Real-time digital twins<\/h2>\n

        The trained Nested FNO can provide real-time predictions. It is hosted on a public GPU-based web application. You can construct any random combination of reservoir condition, injection scheme, and permeability field characteristics and obtain instantaneous predictions of gas saturation, pressure buildup, and sweep efficiency estimates.<\/p>\n

        The web app promotes equity in CO2<\/sub> storage project development and knowledge adoption. This especially benefits small- to mid-sized developers as well as communities that want an independent evaluation of projects being proposed. High-quality forecasts were previously unattainable for these important players.<\/p>\n

        Industry-ready CCS platforms<\/h2>\n
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