The economic feasibility of harnessing deep-geothermal energy (i.e., 5 km or more) via Enhanced or Advanced Geothermal Systems is conditional on reducing the drilling costs significantly, which is unachievable using conventional mechanical rotary drilling. A game changer could lie among frictionless or contactless drilling technologies, including Plasma-Pulse Geo-Drilling (PPGD). PPGD uses microsecond-long highvoltage pulses to fracture the rock by inducing electric discharging inside it. If the resulting increase in the tensile pressure exceeds the rock’s tensile strength, PPGD fractures the rock. Intermittent research using lab experiments and numerical modeling over the last few decades has shown that PPGD can reduce drilling costs by around 20% compared to mechanical rotary drilling, while analytical estimations expect further reductions of up to 90%, assuming further research and development. Despite that, the physics underlining the PPGD process has yet to be fully understood to optimize the operating parameters and push the PPGD research from lab experiments to field scale testing. Further, PPGD feasibility in deep drilling for deep-geothermal investments requires systematic experimental investigations of the effect of these deep wellbore extreme conditions of temperatures and lithostatic and hydrostatic pressures on the PPGD performance. The numerical modeling and the lab experiments presented in this thesis contribute to bridging these knowledge gaps, as follows. First, Chapter 1 gives a general brief background on the motivation behind the PPGD research, the working principle of the PPGD, the advantages and the challenges, and the thesis outline. The content of this chapter is brief as more elaborations are given in the introduction sections of the following Chapters 2-4. Then, Chapter 2 investigates the impact of the pore characteristics (i.e., fluid, pressure, size, and shape) on the PPGD process. To this end, we build an electrostatic model to calculate the voltage gradient distribution across a given granite sample, thereby calculating the voltage gradient across a single pore. For PPGD to induce fracturing, these calculated voltage gradients must exceed the fluid’s dielectric strength under the defined pore size and pressure. The outcome of this chapter is an electric breakdown criteria to estimate the dielectric strength of any rock, and this criterion has been validated against experimental data. Next, Chapter 3 investigated the impact of temperature elevation up to 80C on the PPGD process, simulating the conditions at a depth of ∼3 km. To this end, we build an experimental protocol to test the impact of temperature elevation, lithostatic pressure elevation, and hydrostatic pressure elevation on the PPGD performance. The main outcome of this chapter is that temperature alone reduces the performance of the PPGD process linearly in granite when deionized water is used, and using an alternative oil-based mud could improve the performance at high temperatures. After that, Chapter 4 investigated the impact of lithostatic pressure elevation up to 150 MPa on the PPGD process, simulating the lithostatic pressure conditions at a depth of ∼5.7 km. Increasing the lithostatic pressure up to 40 MPa reduces PPGD performance (i.e., productivity decreases to around 60% and Specific energy increases by 75% from their baseline values), which is unfavorable for the PPGD process. Between the 40 and 80 MPa pressure values, the observed performance shows a constant value, as there is no change in the values of both the productivity and the specific energy for the given error bars. For lithostatic pressure values greater than 80 MPa, PPGD performance is enhanced significantly (i.e., productivity increased by around 60% above its baseline value and Specific energy decreased by 75%), which is favorable for the PPGD process. The effect of both temperature and lithostatic pressure has shown a tendency to enhance the performance of the PPGD process to exceed the baseline value at depths greater than 3.5 km. Finally, Chapter 5 summarizes the thesis in two sections; the first is for the numerical modeling work of Chapter 2, while the second is for the experimental lab work of Chapters 3 and 4. Each section highlights the modeling scheme or the experimental protocol, the main findings focusing on the implications, and concludes with an outlook for future research.