The sheer speed and scale at which this needs to be achieved brings into question whether such lofty goal can be achieved when only broad plans have been outlined. A significant part of the net-zero 2050 plan outlined by the IEA require “subsurface solutions”, more specifically: more mining for minerals (e.g. for batteries), more geothermal energy (electricity + heating/cooling of megacities) and geological storage of CO2, to decarbonize industrial heat. With real world examples, I will argue in this presentation that pulling off this challenge requires building intelligent agents to address the speed and scale issue. The specs of these agents are that they should be able to reason in high-dimensional physical, chemical, and geological spaces about uncertainty, interwoven with geoscientific data acquisition and resource engineering operations. While partially observable Markov decision processes allow formulating such problems, I will outline how mixed-fidelity model approaches are needed to solve them for real world applications. Two cases are used to illustrate the need for these agents and how they can be employed in a real setting. The first case concerns closing the estimated $12 trillion gap in battery metals discoveries needed as outlined in the EIA goals. The second concerns the complexity of storing CO2 in saline aquifers and depleted reservoirs under conditions that prevent leakages or earthquakes.
