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data_preprocessing.py
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import h5py
import numpy as np
import torch
import matplotlib.pyplot as plt
plt.set_cmap('jet')
def prepare_data(Ksteps, data_dir, state_data, ctrl_data, yobs_data, cond):
#### Load pressure and saturation data
hf_r = h5py.File(data_dir + state_data, 'r')
mole = np.array(hf_r.get('Mole_frac_norm_slt')).transpose((3,2,1,0))
sat = np.array(hf_r.get('Sg_norm_slt')).transpose((3,2,1,0))
pres = np.array(hf_r.get('Psim_norm_slt')).transpose((3,2,1,0))
hf_r.close()
n_sample, steps_slt, Nx, Ny = mole.shape
print(mole.shape)
# plt.imshow(sat[0,0,:,:])
#### Load control data
hf_r = h5py.File(data_dir + ctrl_data)
bhp0 = np.array(hf_r.get('Pwf_norm_slt')).transpose((2,1,0))
rate0 = np.array(hf_r.get('Qinj_norm_slt')).transpose((2,1,0))
hf_r.close()
bhp = np.concatenate((bhp0,rate0),axis=1)
#### Load output data
hf_r = h5py.File(data_dir + yobs_data)
if cond =='RC':
Qrate_w = np.array(hf_r.get('Qpro_w_RC_norm_slt')).transpose((2,1,0))
Qrate_g = np.array(hf_r.get('Qpro_g_RC_norm_slt')).transpose((2,1,0))
else:
Qrate_w = np.array(hf_r.get('Qpro_w_norm_slt')).transpose((2,1,0))
Qrate_g = np.array(hf_r.get('Qpro_g_norm_slt')).transpose((2,1,0))
BHP_inj = np.array(hf_r.get('BHPinj_norm_slt')).transpose((2,1,0))
hf_r.close()
yobs = np.concatenate((Qrate_w,Qrate_g,BHP_inj),axis=1)
n_sample, num_well, steps_ctrl = bhp.shape
_, num_prod, _ = bhp0.shape
_, num_inj, _ = rate0.shape
Mole_slt = []
SAT_slt = []
PRES_slt = []
BHP_slt = []
Yobs_slt = []
indt = np.array(range(0,steps_slt-(Ksteps-1)))
print(indt)
for k in range(Ksteps):
indt_k = indt + k
if k ==1:
indt_del = indt_k - indt
indt_del = indt_del / max(indt_del)
mole_t_slt = mole[:, indt_k,:, :]
sat_t_slt = sat[:, indt_k,:, :]
pres_t_slt = pres[:, indt_k,:, :]
num_t_slt = sat_t_slt.shape[1]
if k < Ksteps-1:
bhp_t_slt = np.swapaxes(bhp[:,:, indt_k],1,2)
yobs_t_slt = np.swapaxes(yobs[:,:, indt_k],1,2)
Mole_slt.append(mole_t_slt)
SAT_slt.append(sat_t_slt)
PRES_slt.append(pres_t_slt)
if k < Ksteps-1:
BHP_slt.append(bhp_t_slt)
Yobs_slt.append(yobs_t_slt)
return Mole_slt, SAT_slt, PRES_slt, BHP_slt, Yobs_slt, num_t_slt, Nx, Ny, num_well, num_prod, num_inj
def train_split_data(Mole_slt, SAT_slt, PRES_slt, BHP_slt, Yobs_slt, num_t_slt, Nx, Ny, num_well, num_prod, num_inj, n_channels, device):
num_all = Mole_slt[0].shape[0]
split_ratio = int(num_all/100)
num_run_per_case = 75
num_run_eval = 100 - num_run_per_case # 25 cases
mole_t_train = np.zeros((num_run_per_case*split_ratio, num_t_slt, Nx, Ny))
sat_t_train = np.zeros((num_run_per_case*split_ratio, num_t_slt, Nx, Ny))
pres_t_train = np.zeros((num_run_per_case*split_ratio, num_t_slt, Nx, Ny))
bhp_t_train = np.zeros((num_run_per_case*split_ratio, num_t_slt, num_well))
yobs_t_train = np.zeros((num_run_per_case*split_ratio, num_t_slt, 2*num_prod+num_inj))
mole_t_eval = np.zeros((num_run_eval*split_ratio, num_t_slt, Nx, Ny))
sat_t_eval = np.zeros((num_run_eval*split_ratio, num_t_slt, Nx, Ny))
pres_t_eval = np.zeros((num_run_eval*split_ratio, num_t_slt, Nx, Ny))
bhp_t_eval = np.zeros((num_run_eval*split_ratio, num_t_slt, num_well))
yobs_t_eval = np.zeros((num_run_eval*split_ratio, num_t_slt, 2*num_prod+num_inj))
num_train = num_run_per_case*split_ratio*num_t_slt
shuffle_ind_train = np.random.default_rng(seed=1010).permutation(num_train)
num_eval = num_run_eval*split_ratio*num_t_slt
shuffle_ind_eval = np.random.default_rng(seed=1010).permutation(num_eval)
STATE_train = []
BHP_train = []
Yobs_train = []
STATE_eval = []
BHP_eval = []
Yobs_eval = []
for i_step in range(len(SAT_slt)):
for k in range(split_ratio):
ind0 = k * num_run_per_case
mole_t_train[ind0:ind0+num_run_per_case,...] = Mole_slt[i_step][k*100:k*100+num_run_per_case,...]
sat_t_train[ind0:ind0+num_run_per_case,...] = SAT_slt[i_step][k*100:k*100+num_run_per_case,...]
pres_t_train[ind0:ind0+num_run_per_case,...] = PRES_slt[i_step][k*100:k*100+num_run_per_case,...]
if i_step<len(SAT_slt)-1:
bhp_t_train[ind0:ind0+num_run_per_case,...] = BHP_slt[i_step][k*100: k*100+num_run_per_case,...]
yobs_t_train[ind0:ind0+num_run_per_case,...] = Yobs_slt[i_step][k*100: k*100+num_run_per_case,...]
# dt_train[ind0:ind0+num_run_per_case,...] =indt_d_slt[k*100: k*100+num_run_per_case, :, :]
# Eval set
ind1 = k*num_run_eval
mole_t_eval[ind1:ind1+num_run_eval,...] = Mole_slt[i_step][k*100+num_run_per_case:k*100+100,...]
sat_t_eval[ind1:ind1+num_run_eval,...] = SAT_slt[i_step][k*100+num_run_per_case:k*100+100,...]
pres_t_eval[ind1:ind1+num_run_eval,...] = PRES_slt[i_step][k*100+num_run_per_case:k*100+100,...]
if i_step<len(SAT_slt)-1:
bhp_t_eval[ind1:ind1+num_run_eval,...] = BHP_slt[i_step][k*100+num_run_per_case: k*100+100,...]
yobs_t_eval[ind1:ind1+num_run_eval,...] = Yobs_slt[i_step][k*100+num_run_per_case: k*100+100,...]
Mole_t_train = mole_t_train.reshape((num_run_per_case*split_ratio*num_t_slt, 1, Nx, Ny))
SAT_t_train = sat_t_train.reshape((num_run_per_case*split_ratio*num_t_slt, 1, Nx, Ny))
PRES_t_train = pres_t_train.reshape((num_run_per_case*split_ratio*num_t_slt, 1, Nx, Ny))
if i_step<len(SAT_slt)-1:
BHP_t_train = bhp_t_train.reshape((num_run_per_case*split_ratio*num_t_slt, num_well))
Yobs_t_train = yobs_t_train.reshape((num_run_per_case*split_ratio*num_t_slt, 2*num_prod+num_inj))
# DT_train = dt_train.reshape((num_run_per_case*4*num_t_slt, 1))
Mole_t_eval = mole_t_eval.reshape((num_run_eval*split_ratio*num_t_slt, 1, Nx, Ny))
SAT_t_eval = sat_t_eval.reshape((num_run_eval*split_ratio*num_t_slt, 1, Nx, Ny))
PRES_t_eval = pres_t_eval.reshape((num_run_eval*split_ratio*num_t_slt, 1, Nx, Ny))
if i_step<len(SAT_slt)-1:
BHP_t_eval = bhp_t_eval.reshape((num_run_eval*split_ratio*num_t_slt, num_well))
Yobs_t_eval = yobs_t_eval.reshape((num_run_eval*split_ratio*num_t_slt, 2*num_prod+num_inj))
# DT_eval = dt_eval.reshape((num_run_eval*4*num_t_slt, 1))
### shuffle train and eval samples
if n_channels==3:
STATE_t_train = torch.tensor(np.concatenate((Mole_t_train, SAT_t_train, PRES_t_train),axis=1), dtype=torch.float32).to(device)
STATE_t_eval = torch.tensor(np.concatenate((Mole_t_eval, SAT_t_eval, PRES_t_eval),axis=1), dtype=torch.float32).to(device)
else:
STATE_t_train = torch.tensor(np.concatenate((Mole_t_train, PRES_t_train),axis=1), dtype=torch.float32).to(device)
STATE_t_eval = torch.tensor(np.concatenate((Mole_t_eval, PRES_t_eval),axis=1), dtype=torch.float32).to(device)
STATE_t_train = STATE_t_train[shuffle_ind_train, ...]
if i_step<len(SAT_slt)-1:
BHP_t_train = torch.tensor(BHP_t_train[shuffle_ind_train, ...], dtype=torch.float32).to(device)
Yobs_t_train = torch.tensor(Yobs_t_train[shuffle_ind_train, ...], dtype=torch.float32).to(device)
# DT_train = DT_train[shuffle_ind_train, ...]
STATE_t_eval = STATE_t_eval[shuffle_ind_eval, ...]
if i_step<len(SAT_slt)-1:
BHP_t_eval = torch.tensor(BHP_t_eval[shuffle_ind_eval, ...], dtype=torch.float32).to(device)
Yobs_t_eval = torch.tensor(Yobs_t_eval[shuffle_ind_eval, ...], dtype=torch.float32).to(device)
# DT_eval = DT_eval[shuffle_ind_eval, ...]
STATE_train.append(STATE_t_train)
STATE_eval.append(STATE_t_eval)
if i_step<len(SAT_slt)-1:
BHP_train.append(BHP_t_train)
BHP_eval.append(BHP_t_eval)
Yobs_train.append(Yobs_t_train)
Yobs_eval.append(Yobs_t_eval)
return STATE_train, BHP_train, Yobs_train, STATE_eval, BHP_eval, Yobs_eval
def save_data_to_file():
return