import os
import logging
import numpy as np
from openpnm.topotools import trim, extend
from openpnm.network import Network
from pathlib import Path
from pandas import read_table, DataFrame
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
[docs]
def network_from_statoil(path, prefix):
r"""
Load data from the \'dat\' files located in specified folder.
Parameters
----------
path : str
The full path to the folder containing the set of \'dat\' files.
prefix : str
The file name prefix on each file. The data files are stored
as \<prefix\>_node1.dat.
network : Network
If given then the data will be loaded on it and returned. If not
given, a Network will be created and returned.
Returns
-------
Project
An OpenPNM Project containing a Network holding all the data
Notes
-----
The StatOil format is used by the Maximal Ball network extraction code of
the Imperial College London group
This class can be used to load and work with those networks. Numerous
datasets are available for download from the group's
`website <http://tinyurl.com/zurko4q>`_.
The 'Statoil' format consists of 4 different files in a single
folder. The data is stored in columns with each corresponding to a
specific property. Headers are not provided in the files, so one must
refer to various theses and documents to interpret their meaning.
"""
net = {}
# Parse the link1 file
path = Path(path)
filename = Path(path.resolve(), prefix+'_link1.dat')
with open(filename, mode='r') as f:
link1 = read_table(filepath_or_buffer=f,
header=None,
skiprows=1,
sep=' ',
skipinitialspace=True,
index_col=0)
link1.columns = ['throat.pore1', 'throat.pore2', 'throat.radius',
'throat.shape_factor', 'throat.total_length']
# Add link1 props to net
net['throat.conns'] = np.vstack((link1['throat.pore1']-1,
link1['throat.pore2']-1)).T
net['throat.conns'] = np.sort(net['throat.conns'], axis=1)
net['throat.radius'] = np.array(link1['throat.radius'])
net['throat.shape_factor'] = np.array(link1['throat.shape_factor'])
net['throat.total_length'] = np.array(link1['throat.total_length'])
filename = Path(path.resolve(), prefix+'_link2.dat')
with open(filename, mode='r') as f:
link2 = read_table(filepath_or_buffer=f,
header=None,
sep=' ',
skipinitialspace=True,
index_col=0)
link2.columns = ['throat.pore1', 'throat.pore2',
'throat.pore1_length', 'throat.pore2_length',
'throat.length', 'throat.volume',
'throat.clay_volume']
# Add link2 props to net
cl_t = np.array(link2['throat.length'])
net['throat.length'] = cl_t
net['throat.conduit_lengths.throat'] = cl_t
net['throat.volume'] = np.array(link2['throat.volume'])
cl_p1 = np.array(link2['throat.pore1_length'])
net['throat.conduit_lengths.pore1'] = cl_p1
cl_p2 = np.array(link2['throat.pore2_length'])
net['throat.conduit_lengths.pore2'] = cl_p2
net['throat.clay_volume'] = np.array(link2['throat.clay_volume'])
# ---------------------------------------------------------------------
# Parse the node1 file
filename = Path(path.resolve(), prefix+'_node1.dat')
with open(filename, mode='r') as f:
row_0 = f.readline().split()
num_lines = int(row_0[0])
array = np.ndarray([num_lines, 6])
for i in range(num_lines):
row = f.readline()\
.replace('\t', ' ').replace('\n', ' ').split()
array[i, :] = row[0:6]
node1 = DataFrame(array[:, [1, 2, 3, 4]])
node1.columns = ['pore.x_coord', 'pore.y_coord', 'pore.z_coord',
'pore.coordination_number']
# Add node1 props to net
net['pore.coords'] = np.vstack((node1['pore.x_coord'],
node1['pore.y_coord'],
node1['pore.z_coord'])).T
# ---------------------------------------------------------------------
# Parse the node2 file
filename = Path(path.resolve(), prefix+'_node2.dat')
with open(filename, mode='r') as f:
node2 = read_table(filepath_or_buffer=f,
header=None,
sep=' ',
skipinitialspace=True,
index_col=0)
node2.columns = ['pore.volume', 'pore.radius', 'pore.shape_factor',
'pore.clay_volume']
# Add node2 props to net
net['pore.volume'] = np.array(node2['pore.volume'])
net['pore.radius'] = np.array(node2['pore.radius'])
net['pore.shape_factor'] = np.array(node2['pore.shape_factor'])
net['pore.clay_volume'] = np.array(node2['pore.clay_volume'])
net['throat.cross_sectional_area'] = ((net['throat.radius']**2)
/ (4.0*net['throat.shape_factor']))
net['pore.area'] = ((net['pore.radius']**2)
/ (4.0*net['pore.shape_factor']))
network = Network()
network.update(net)
# Use OpenPNM Tools to clean up network
# Trim throats connected to 'inlet' or 'outlet' reservoirs
trim1 = np.where(np.any(net['throat.conns'] == -1, axis=1))[0]
# Apply 'outlet' label to these pores
outlets = network['throat.conns'][trim1, 1]
network['pore.outlets'] = False
network['pore.outlets'][outlets] = True
trim2 = np.where(np.any(net['throat.conns'] == -2, axis=1))[0]
# Apply 'inlet' label to these pores
inlets = network['throat.conns'][trim2, 1]
network['pore.inlets'] = False
network['pore.inlets'][inlets] = True
# Now trim the throats
to_trim = np.hstack([trim1, trim2])
trim(network=network, throats=to_trim)
return network
