Adapun saran yang dapat diberikan pada penelitian ini adalah sebagai berikut:
1. Data sumur log yang lebih lengkap dan sudah dianalisis atau dilakukan pre-process dengan baik. Data log wajib memiliki data log Vs/DTSM dalam data sumur log tersebut.
2. Seismic gathers yang terkait dengan saran no.1 sehingga proses pre-stack inversion akan lebih ideal dalam melakukan perbandingan.
3. Penelitian selanjutnya diharapkan dapat menciptakan model arsitektur Neural Network yang lebih optimal
Universitas Pertamina - 48 lebih detail sehingga hasil prediksi lebih optimal.
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FORMULIR BIMBINGAN TUGAS AKHIR
FAKULTAS TEKNOLOGI EKSPLORASI & PRODUKSI
PROGRAM STUDI TEKNIK GEOFISIKA
Nama Mahasiswa : Loris Alif Syahputra NIM : 101116100
Nama Pembimbing : Waskito Pranowo, M.T. NIP : 116030
No. 1 Hari/Tanggal: Jum’at/20-03-2020
Hal yang menjadi perhatian:
1. Membuat Reflektifitas EEI
2. Tahapan-tahapan melakukan Inversi
Paraf Pembimbing:
No. 2 Hari/Tanggal: Jum’at/27-03-2020
Hal yang menjadi perhatian:
1. Menambahkan parameter filter tambahan dalam prediksi
2. Persiapan untuk seminar kemajuan TA
Paraf Pembimbing:
FORMULIR BIMBINGAN TUGAS AKHIR
FAKULTAS TEKNOLOGI EKSPLORASI & PRODUKSI
PROGRAM STUDI TEKNIK GEOFISIKA
Nama Mahasiswa : Loris Alif Syahputra NIM : 101116100
Nama Pembimbing : Waskito Pranowo, M.T. NIP : 116030
No. 1 Hari/Tanggal: Jum’at/10-04-2020
Hal yang menjadi perhatian:
1. Menyelesaikan tahapan inversi 2. Meningkatkan akurasi hasil prediksi
Paraf Pembimbing:
No. 2 Hari/Tanggal: Jum’at/17-04-2020
Hal yang menjadi perhatian:
1. Membandingkan hasil inversi dari data sumur dengan data prediksi
2. Konsultasi isi laporan TA
Paraf Pembimbing:
FORMULIR BIMBINGAN TUGAS AKHIR
FAKULTAS TEKNOLOGI EKSPLORASI & PRODUKSI
PROGRAM STUDI TEKNIK GEOFISIKA
Nama Mahasiswa : Loris Alif Syahputra NIM : 101116100
Nama Pembimbing : Waskito Pranowo, M.T. NIP : 116030
No. 1 Hari/Tanggal: Kamis/16-01-2020
Hal yang menjadi perhatian:
1.
Mengamati urutan parameter input untuk model ANN2.
Melihat parameter log dengan kontribusi terbesar dalam menurunkan nilai RMSEParaf Pembimbing:
No. 2 Hari/Tanggal: Kamis/23-01-2020
Hal yang menjadi perhatian:
1. Menentukan parameter-parameter log dengan nilai korelasi terhadap parameter
target (Vs)
2. Menghitung nilai relative error log input dengan parameter target (Vs)
Paraf Pembimbing:
FORMULIR BIMBINGAN TUGAS AKHIR
FAKULTAS TEKNOLOGI EKSPLORASI & PRODUKSI
PROGRAM STUDI TEKNIK GEOFISIKA
Nama Mahasiswa : Loris Alif Syahputra NIM : 101116100
Nama Pembimbing : Waskito Pranowo, M.T. NIP : 116030
No. 1 Hari/Tanggal: Kamis/30-01-2020
Hal yang menjadi perhatian:
1. Melihat RMSE pada data well train, perbaiki parameter atau arsitektur hingga stabil 2. Mencoba algoritma yang lebih ringan
3. Lakukan regresi linear antara parameter log dengan output
4. Membuat model awal menggunakan metode machine learning yang lebih sederhana / cepat sebagai parameter awal untuk prediksi Vs menggunakan ANN
Paraf Pembimbing:
No. 2 Hari/Tanggal: Kamis/23-01-2020
Hal yang menjadi perhatian:
1. Tambah jumlah data well log 1 + well log 2 untuk prediksi well log 3 & well 3 +
well 2 untuk prediksi well 1
2. Perbaiki arsitektur ANN
LAMPIRAN KODE SKRIP
#Libraryfrom keras.models import Sequential from keras.layers import Dense import matplotlib.pyplot as plt import pandas as pd
from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import MinMaxScaler import tensorflow as tf
from keras.regularizers import l1_l2 import numpy as np
#Pre-process the datasets ##WELL TRAIN DATASET
dataset = pd.read_excel('Poseidon11.xlsx')
dataset = dataset.dropna(how='any') #For rows that have any nan value on it
# IQR Rule
Q4 = dataset.quantile(0.25) Q6 = dataset.quantile(0.75) IQRS = Q6 - Q4
dataset = dataset[~((dataset < (Q4 - 1.5 * IQRS)) |(dataset > (Q6 + 1.5 * IQRS))).any(axis=1)]
x_train = dataset.iloc[:, [1,3,4,5,6]].values y_train = dataset.iloc[:,7].values
##WELL TEST DATASET
datasets = pd.read_excel('Poseidon21.xlsx')
datasets = datasets.dropna(how='any') #For rows that have any nan value on it
#IQR Rule
Q1 = datasets.quantile(0.25) Q3 = datasets.quantile(0.75) IQR = Q3 - Q1
datasets = datasets[~((datasets < (Q1 - 1.5 * IQR)) |(datasets > (Q3 + 1.5 * IQR))).any(axis=1)]
x_test = datasets.iloc[:, [1,3,4,5,6]].values y_test = datasets.iloc[:,7].values
#Normalizing the datasets
nor = MinMaxScaler()
x_train = nor.fit_transform(x_train) x_test = nor.transform(x_test)
# Inserting Vs Pred to DF using MLR
from sklearn.linear_model import LinearRegression regressor = LinearRegression()
regressor.fit(x_train,y_train)
y_pred1 = regressor.predict(x_train) y_pred2 = regressor.predict(x_test)
datasets.insert(0, "Vs Pred", y_pred2)
# Using ANN
x_train1 = dataset.iloc[:, [0,2,4,5,6,7]].values x_test2 = datasets.iloc[:, [0,2,4,5,6,7]].values
#Normalizing the datasets
nor = StandardScaler()
x_train1 = nor.fit_transform(x_train1) x_test2 = nor.transform(x_test2)
# #Creating ANN Architecture
lrelu = lambda x: tf.keras.activations.relu(x, alpha=0.1) model = Sequential () #intializer
model.add(Dense(12, input_dim=6, kernel_initializer='glorot_uniform',bias_initializer = 'glorot_uniform', activation= lrelu, kernel_regularizer=l1_l2(l1=0.01, l2=0.01), bias_regularizer=l1_l2(l1=0.01, l2=0.01))) #input layer
model.add(Dense(6, kernel_initializer='glorot_uniform',bias_initializer = 'glorot_uniform', activation =lrelu, kernel_regularizer=l1_l2(l1=0.01, l2=0.01), bias_regularizer=l1_l2(l1=0.01, l2=0.01))) #hidden layer
model.add(Dense(3, kernel_initializer='glorot_uniform',bias_initializer = 'glorot_uniform', activation =lrelu, kernel_regularizer=l1_l2(l1=0.01, l2=0.01), bias_regularizer=l1_l2(l1=0.01, l2=0.01))) #hidden layer
model.add(Dense(1, kernel_initializer='glorot_uniform',bias_initializer = 'glorot_uniform', activation = 'linear', kernel_regularizer=l1_l2(l1=0.01, l2=0.01), bias_regularizer=l1_l2(l1=0.01, l2=0.01))) #output layer
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error']) #Compiling the ANN
# Training ANN Model
model.fit(x_train1,y_train, batch_size=10, epochs =500, verbose = 1, validation_split = 0.2)