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Jesse Nyokabi's picture
Engineer, Quaise Energy Africa

Dispatch/Control Engineer Dispatching power on the grid. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐞𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐭𝐡𝐞 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐫𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐞𝐧𝐞𝐫𝐠𝐲.

  • Member since 2021
  • 42 items added with 389 views
  • Dec 15, 2022

Geothermal development projects rely heavily on very disparate types of reservoir data that elaborate on subsurface geological environments. This data influences the understanding of the economic potential of the project. Geothermal reservoirs are unique in the wide range of rock types in which they are found. The geothermal exploration process captures this varying data through standard subsurface and petrologic analysis, surface geology and geophysical surveys (gravity, magnetic, and MT), and heat flow data to obtain a similarly varied analysis. The analysis expands upon the parameters such as rock permeability, porosity, resistivity, conductivity, seismicity, fluid volume, and heat flow within the reservoir. Efficient visualization of reservoir characteristics provides an accurate analysis of the geothermal subsurface on which operators can make informed decisions regarding project development while also minimizing their investment risk. The ensemble learning paradigm is a new Computational Intelligence (CI) algorithm in which diverse expert opinions are strategically incorporated and smartly integrated to solve a problem. This new learning paradigm has its root in human sociology where final decisions are made by considering various opinions of a "committee of experts" to obtain an overall "ensemble" decision. It has its origin in human psychology which suggested that the judgment of a committee is superior to those of individuals given that "the individuals have an acceptable level of competence". The ensemble learning methodology is a close emulation of the human socio-cultural behavior of seeking several people's opinions before making any important decision. This study demonstrates the application of ensemble machine learning in the characterization of the properties of a geothermal reservoir, based on previous successful applications in various industries including oil and gas. This is achieved by training the machine to select an overall best hypothesis to improve model performance, reducing the risk of selecting a poor model in implementing Machine Learning in geothermal reservoir characterization.


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Jesse Nyokabi's picture
Thank Jesse for the Post!
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