Quaise raises $134M to drill superhot rock with microwaves

Introduction

In the realm of advanced geothermal energy, Quaise Energy is pioneering a revolutionary approach to tapping into superhot rock deep underground using microwave technology. This tutorial will guide you through building a simulation model that mimics the core principles of Quaise's microwave heating process, focusing on the thermal dynamics and energy transfer mechanisms involved in deep geothermal drilling.

This hands-on project will teach you how to model heat propagation through rock formations using numerical methods, which is fundamental to understanding how microwave energy can be effectively transferred to create superhot rock conditions for geothermal energy extraction.

Prerequisites

• Basic understanding of Python programming
• Familiarity with numerical methods and differential equations
• Knowledge of heat transfer principles and thermal conductivity
• Python libraries: NumPy, Matplotlib, and SciPy

Step-by-Step Instructions

1. Set Up Your Python Environment

First, we need to install the required libraries. Open your terminal or command prompt and run:

pip install numpy matplotlib scipy

This ensures we have the necessary tools for numerical computation and visualization.

2. Import Required Libraries

Create a new Python file and start by importing the essential libraries:

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
import matplotlib.animation as animation

NumPy provides numerical operations, Matplotlib handles visualization, SciPy offers integration methods, and animation allows us to visualize the heating process over time.

3. Define Material Properties

Next, we'll define the thermal properties of the rock formation that Quaise targets:

# Rock properties for deep geothermal simulation
rock_density = 2700  # kg/m^3
rock_specific_heat = 800  # J/kg.K
rock_thermal_conductivity = 2.5  # W/m.K
rock_initial_temperature = 25  # °C
rock_melting_point = 1200  # °C
microwave_power_density = 100000  # W/m^3 (approximate for superhot rock)

These values represent typical properties of igneous rock at depth, where the microwave energy will be absorbed and converted to heat.

4. Create the Heat Transfer Model

We'll implement a simplified 1D heat equation model that accounts for microwave heating:

def heat_equation(T, t, L, dx, dt, k, rho, Cp, Q):
    # T: temperature array
    # L: length of domain
    # dx: spatial step
    # dt: time step
    # k: thermal conductivity
    # rho: density
    # Cp: specific heat
    # Q: heat source (microwave power)
    
    dTdt = np.zeros_like(T)
    
    # Apply finite difference method
    for i in range(1, len(T)-1):
        dTdt[i] = (k / (rho * Cp)) * (T[i+1] - 2*T[i] + T[i-1]) / dx**2 + Q / (rho * Cp)
    
    # Boundary conditions (insulated at both ends)
    dTdt[0] = 0
    dTdt[-1] = 0
    
    return dTdt

This function models how heat diffuses through the rock while accounting for the microwave energy source. The finite difference method approximates the continuous heat equation.

5. Set Up Simulation Parameters

Define the simulation domain and initial conditions:

# Simulation parameters
L = 1000  # meters (depth simulation)
dx = 10  # meters
T_initial = np.full(int(L/dx), rock_initial_temperature)  # Initial temperature

# Time parameters
max_time = 3600  # seconds (1 hour)
dt = 10  # seconds
time_points = np.arange(0, max_time, dt)

# Spatial grid
x = np.arange(0, L, dx)

# Microwave heating at depth
Q = np.zeros_like(T_initial)
Q[50] = microwave_power_density  # Apply microwave energy at 500m depth

This setup simulates a 1000-meter deep rock formation with microwave heating applied at a specific depth, mimicking Quaise's approach to targeted energy delivery.

6. Run the Simulation

Execute the numerical solution of the heat equation:

# Run simulation
T_history = []
T_current = T_initial.copy()

for t in time_points:
    dTdt = heat_equation(T_current, t, L, dx, dt, rock_thermal_conductivity,
                        rock_density, rock_specific_heat, Q)
    T_current = T_current + dTdt * dt
    T_history.append(T_current.copy())

The simulation runs through time steps, updating temperatures based on heat conduction and microwave energy input.

7. Visualize Results

Create plots to visualize the temperature evolution:

# Plot temperature profiles
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Temperature profile at different times
for i in [0, 10, 20, 30]:
    ax1.plot(x, T_history[i], label=f'Time {i*dt/60:.0f} min')

ax1.set_xlabel('Depth (m)')
ax1.set_ylabel('Temperature (°C)')
ax1.set_title('Temperature Distribution Over Time')
ax1.legend()
ax1.grid(True)

# Heat source visualization
ax2.plot(x, Q, 'ro', label='Microwave Source')
ax2.set_xlabel('Depth (m)')
ax2.set_ylabel('Power Density (W/m³)')
ax2.set_title('Microwave Energy Source')
ax2.grid(True)

plt.tight_layout()
plt.show()

This visualization shows how the microwave energy creates a temperature gradient that propagates through the rock, with the peak temperature increasing over time.

8. Analyze Temperature Distribution

Examine key metrics from the simulation:

# Analyze peak temperatures
peak_temperatures = [np.max(temp) for temp in T_history]
print(f'Maximum temperature reached: {np.max(peak_temperatures):.1f}°C')
print(f'Melting point reached: {np.max(peak_temperatures) > rock_melting_point}')

# Find where melting occurs
melting_depths = []
for i, temp in enumerate(T_history):
    if np.max(temp) > rock_melting_point:
        melting_depths.append(i * dx)
        break

print(f'Melting begins at depth: {melting_depths[0] if melting_depths else "None"} meters')

This analysis demonstrates how the microwave energy can create conditions suitable for superhot rock geothermal energy extraction.

Summary

This tutorial demonstrated how to build a simulation model that mimics the thermal dynamics of Quaise Energy's microwave-based geothermal drilling approach. By modeling heat propagation through rock formations with targeted microwave energy input, we've gained insight into how superhot rock conditions can be achieved at depth.

The simulation shows that microwave heating can create temperature gradients that exceed the melting point of rock, creating the conditions necessary for enhanced geothermal systems. This approach differs from traditional geothermal methods by targeting specific depth zones for energy input rather than relying on natural heat sources.

While this is a simplified model, it captures the essential physics of how microwave energy can be used to create superhot rock conditions. Real-world applications would require more complex models accounting for rock heterogeneity, fluid dynamics, and pressure effects, but this foundation provides a starting point for understanding the technology behind Quaise's innovative approach.