Add localization example; publish PR's

examples/localization.py

@@ -0,0 +1,192 @@
+r"""
+Localization
+==========================
+
+This example follows contextually :ref:`sphx_glr_gallery_basicreservoir.py`,
+but uses several well doublets and compares ES-MDA with and without
+localization.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+import resmda
+
+# For reproducibility, we instantiate a random number generator with a fixed
+# seed. For production, remove the seed!
+rng = np.random.default_rng(2020)
+
+# sphinx_gallery_thumbnail_number = 1
+
+###############################################################################
+# Model parameters
+# ----------------
+
+# Grid extension
+nx = 30
+ny = 30
+nc = nx*ny
+
+# Permeabilities
+perm_mean = 3.0
+perm_min = 0.5
+perm_max = 5.0
+
+# ESMDA parameters
+ne = 100                  # Number of ensembles
+dt = np.zeros(10)+0.0001  # Time steps (could be irregular, e.g., increasing!)
+time = np.r_[0, np.cumsum(dt)]
+
+# Assumed sandard deviation of our data
+dstd = 0.5
+
+# Observation location indices (should be well locations)
+ox = (5, 15, 24)
+oy = (5, 10, 24)
+
+# Number of data points
+nd = time.size * len(ox)
+
+# Wells
+wells = np.array([
+    [5, 5, 180], [5, 12, 120],
+    [15, 10, 180], [20, 5, 120],
+    [24, 24, 180], [24, 17, 120]
+])
+
+
+###############################################################################
+# Create permeability maps for ESMDA
+# ----------------------------------
+#
+# We will create a set of permeability maps that will serve as our initial
+# guess (prior). These maps are generated using a Gaussian random field and are
+# constrained by certain statistical properties.
+
+# Get the model and ne prior models
+RP = resmda.RandomPermeability(nx, ny, perm_mean, perm_min, perm_max)
+perm_true = RP(1, random=rng)
+perm_prior = RP(ne, random=rng)
+
+
+###############################################################################
+# Run the prior models and the reference case
+# -------------------------------------------
+
+# Instantiate reservoir simulator
+RS = resmda.Simulator(nx, ny, wells=wells)
+
+
+def sim(x):
+    """Custom fct to use exp(x), and specific dt & location."""
+    return RS(np.exp(x), dt=dt, data=(ox, oy)).reshape((x.shape[0], -1))
+
+
+# Simulate data for the prior and true fields
+data_prior = sim(perm_prior)
+data_true = sim(perm_true)
+data_obs = rng.normal(data_true, dstd)
+data_obs[0, :3] = data_true[0, :3]
+print(data_obs.shape)
+
+
+###############################################################################
+# Localization Matrix
+# -------------------
+
+# Vector of nd length with the well x and y position for each nd data point
+nd_positions = np.tile(np.array([ox, oy]), time.size).T
+
+# Create matrix
+loc_mat = resmda.build_localization_matrix(RP.cov, nd_positions, (nx, ny))
+
+# QC localization matrix
+fig, ax = plt.subplots(1, 1, sharex=True, sharey=True, constrained_layout=True)
+ax.imshow(loc_mat.sum(axis=2).T)
+ax.contour(loc_mat.sum(axis=2).T, levels=[2.0, ], colors='w')
+for well in wells:
+    ax.plot(well[0], well[1], ['C3v', 'C1^'][int(well[2] == 120)])
+
+
+###############################################################################
+# ES-MDA
+# ------
+
+
+def restrict_permeability(x):
+    x[:] = np.clip(x, perm_min, perm_max)
+
+
+inp = {
+    'model_prior': perm_prior,
+    'forward': sim,
+    'data_obs': data_obs,
+    'sigma': dstd,
+    'alphas': 4,
+    'data_prior': data_prior,
+    'callback_post': restrict_permeability,
+    'random': rng,
+}
+
+
+###############################################################################
+# Without localization
+# ''''''''''''''''''''
+
+nl_perm_post, nl_data_post = resmda.esmda(**inp)
+
+
+###############################################################################
+# With localization
+# '''''''''''''''''
+
+wl_perm_post, wl_data_post = resmda.esmda(**inp, localization_matrix=loc_mat)
+
+
+###############################################################################
+# Compare permeabilities
+# ----------------------
+
+# Plot posterior
+fig, axs = plt.subplots(
+    2, 2, figsize=(6, 6), sharex=True, sharey=True, constrained_layout=True)
+axs[0, 0].set_title('Prior Mean')
+axs[0, 0].imshow(perm_prior.mean(axis=0).T)
+axs[0, 1].set_title('"Truth"')
+axs[0, 1].imshow(perm_true.T)
+
+
+axs[1, 0].set_title('Post Mean without localization')
+axs[1, 0].imshow(nl_perm_post.mean(axis=0).T)
+axs[1, 1].set_title('Post Mean with localization')
+axs[1, 1].imshow(wl_perm_post.mean(axis=0).T)
+axs[1, 1].contour(loc_mat.sum(axis=2).T, levels=[2.0, ], colors='w')
+
+for ax in axs.ravel():
+    for well in wells:
+        ax.plot(well[0], well[1], ['C3v', 'C1^'][int(well[2] == 120)])
+
+
+###############################################################################
+# Compare data
+# ------------
+
+# QC data and priors
+fig, axs = plt.subplots(
+    2, 3, figsize=(8, 5), sharex=True, sharey=True, constrained_layout=True)
+fig.suptitle('Prior and posterior data')
+for i, ax in enumerate(axs[0, :]):
+    ax.plot(time, data_prior[..., i::3].T, color='.6', alpha=0.5)
+    ax.plot(time, nl_data_post[..., i::3].T, color='C0', alpha=0.5)
+    ax.plot(time, data_obs[0, i::3], 'C3o')
+    ax.set_ylabel('Pressure')
+for i, ax in enumerate(axs[1, :]):
+    ax.plot(time, data_prior[..., i::3].T, color='.6', alpha=0.5)
+    ax.plot(time, wl_data_post[..., i::3].T, color='C0', alpha=0.5)
+    ax.plot(time, data_obs[0, i::3], 'C3o')
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Pressure')
+
+
+###############################################################################
+
+resmda.Report()