In the latest post, it was illustrated and discussed that integrating Data Analytics and Machine Learning (ML) (and Deep Learning (DL)) and other AI techniques and methods into the traditional workflows of the oil and gas industry is straightforward. This integration provides actionable insights that can be immediately used in practical applications.
An interesting paper that provides a comprehensive review of AI's role in the geothermal energy field in particular can be found here.
In geothermal energy, just like in the oil and gas industry, a quick search on Google Scholar or Semantic Scholar shows numerous industry and academic use cases and ongoing projects related to this topic and its technologies. The image above shows how the number of conference and journal papers, delving into geothermal energy and ML/DL, have been increasing particularly recently.
There are many opportunities to apply machine learning, deep learning, and other data analytics methods in the field of geothermal energy. The image below is an example of Descriptive Analytics, showing a map of the global distribution of Geothermal Gradient, and basic statistics regarding renewable energies, for selected cities.
In the geothermal energy industry, there is also an abundance of data including the subsurface geologic characterization, rock properties, vertical heat surveys including geologic features, etc., well data, sensor data, economic data, and more. The key is to identify the most suitable analytics or ML/DL workflow to address the specific challenges in geothermal energy.
Three examples of resent ML/DL-driven improvements in geothermal energy: according to Li et al. (2023), using ML for geothermal energy mapping significantly enhances exploration success by directly tackling the issue of site identification. In the same vane, Buster et al. (2021) and Xue et al. (2023) showcased how ML can be applied to optimize operations and model the thermo-economic aspects of geothermal systems, thereby improving efficiency and sustainability in resource management.
Key stakeholders involved in geothermal energy development, such as the Chilean Government, the CEGA (Universidad de Chile), and Los Alamos National Laboratory (LANL), have all attested to its unquestionable relevance and importance. The image below is courtesy of LANL.
DATAMATE/DataRobot, in partnership with Angel Solutions (SDG) and its trusted partners Geoloil PETROPHYSICS and MineaOil Ltd, comprises highly trained professionals; and they are prepared and equipped with the best AI-powered platform and tools to effectively handle and provide solutions for your data project needs, regardless of type or volume.
About:
GeolOil LLC: GeolOil is a software and consulting company specialized in petrophysics for oil & gas conventional and un-conventional reservoirs, CO2 storage, CO2 natural reservoirs, geothermal energy, hydro-geology, natural hydrogen reservoirs, and natural helium reservoirs. Our affordable software, listed by the official SPWLA software directory, has an intuitive graphical user interface, easy to use, with readable bold buttons and plenty of options.
MineaOil Ltd: MineaOil Ltd has been recognized as the Energy Consultancy and Training Company of the Year at the 2023 Corporate LiveWire Innovation & Excellence Awards. As a UK-based oil and gas consulting company, we specialize in reservoir and petroleum engineering, and Enhanced Oil Recovery (EOR) through various methods such as miscible gas injection, WAG, N2, CO2, and more.
DataRobot: At DataRobot, we move fast and reward hard work. We expect results but most of all, we love doing work we’re passionate about. We believe that AI will enhance every aspect of business transactions and human interactions to improve how we live, work, play and stay safe. Our vision? For all organizations to adopt Value-Driven AI as a core competency to improve how they run, grow, and optimize their business.
Feel free to reach out to Angel Solutions (SDG) directly or through our partners for prompt and reliable assistance.
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