Tutorial 3: The extended Theis solution in 3D

We provide an extended theis solution, that incorporates the effectes of a heterogeneous conductivity field on a pumping test. It also includes an anisotropy ratio of the horizontal and vertical length scales.

In the following this extended solution is compared to the standard theis solution for well flow. You can nicely see, that the extended solution represents a transition between the theis solutions for the effective and harmonic-mean conductivity.

Reference: Müller 2015

import numpy as np
from matplotlib import pyplot as plt
from anaflow import theis, ext_theis_3d
from anaflow.tools.special import aniso

time_labels = ["10 s", "10 min", "10 h"]
time = [10, 600, 36000]                  # 10s, 10min, 10h
rad = np.geomspace(0.05, 4)              # radial distance from the pumping well in [0, 4]
var = 0.5                                # variance of the log-conductivity
len_scale = 10.0                         # correlation length of the log-conductivity
anis = 0.75                              # anisotropy ratio of the log-conductivity
KG = 1e-4                                # the geometric mean of the conductivity
Kefu = KG*np.exp(var*(0.5-aniso(anis)))  # the effective conductivity for uniform flow
KH = KG*np.exp(-var/2.0)                 # the harmonic mean of the conductivity
S = 1e-4                                 # storage
rate = -1e-4                             # pumping rate
L = 1.0                                  # vertical extend of the aquifer

head_Kefu = theis(time, rad, S, Kefu*L, rate)
head_KH = theis(time, rad, S, KH*L, rate)
head_ef = ext_theis_3d(time, rad, S, KG, var, len_scale, anis, L, rate)
for i, step in enumerate(time):
    label_TG = "Theis($K_{efu}$)" if i == 0 else None
    label_TH = "Theis($K_H$)" if i == 0 else None
    label_ef = "extended Theis 3D" if i == 0 else None
    plt.plot(rad, head_Kefu[i], label=label_TG, color="C"+str(i), linestyle="--")
    plt.plot(rad, head_KH[i], label=label_TH, color="C"+str(i), linestyle=":")
    plt.plot(rad, head_ef[i], label=label_ef, color="C"+str(i))

plt.xlabel("r in [m]")
plt.ylabel("h in [m]")
ylim = plt.gca().get_ylim()
plt.gca().set_xlim([0, rad[-1]])
ax2 = plt.gca().twinx()