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The AAPG/Datapages Combined Publications Database
Showing 33,055 Results. Searched 195,364 documents.
Application of Common Contour Binning (CCB) and Back Propagation Neural Network (BPNN) for Oil Water Contact Prediction in Carbonate Reservoir (The Case Study at G404 Field)
Wahyu Tri Sutrisno, Septian Prahastudhi, Ayi Syaeful Bahri, Yulia Putri Wulandari
Indonesian Petroleum Association
... networks have an input, an output and generally a hidden layer. The number of inputs and outputs are determined by considering the characteristics...
2013
Feasibility of Moment Tensor Inversion From a Single Borehole Data Using Artificial Neural Networks; #42212 (2018)
Oleg Ovcharenko, Jubran Akram, Daniel Peter
Search and Discovery.com
... evaluation. We also investigate the effect of number of hidden layers and choice of optimization algorithm on the accuracy of inverted results. Our results...
2018
A cross-shape deep Boltzmann machine for petrophysical seismic inversion
Son Dang Phan and Mrinal K. Sen
AAPG Bulletin
... at the vertices interconnected via a hidden neuron layer at the center. Four different input data types, including a seismic amplitude and three...
2022
Fault zone deformation and displacement partitioning in mechanically layered carbonates: The Hidden Valley fault, central Texas
David A. Ferrill, Alan P. Morris, Ronald N. McGinnis, Kevin J. Smart, William C. Ward
AAPG Bulletin
... than the footwall (see for example, Figures 6B, D; 7). Layer dips in the hanging-wall damage zone are generally 10 or less and synthetic to the Hidden...
2011
Forecasting Coalbed Methane Resources by Artificial Neural Network; #80335 (2013)
Yuping Yang, Jianhua Zhong, and Gangshan Lin
Search and Discovery.com
... layer, the hidden layer and the output layer. The neurons of the input layer are the known index numbers of the samples, and the neurons of the output...
2013
Supervised Learning Applied to Rock Type Classification in Sandstone Based on Wireline Formation Pressure Data; #42539 (2020)
Jose Victor Contreras
Search and Discovery.com
..., hidden, and output layers, which are chained together and where each layer has several neurons. The loss function, which defines the feedback signal used...
2020
A three-component field study of the effect of the low-velocity layer on converted-wave seismic data
Dan Cieslewicz, Don C. Lawton
CSPG Special Publications
...A three-component field study of the effect of the low-velocity layer on converted-wave seismic data Dan Cieslewicz, Don C. Lawton 1998 217 220...
1998
ABSTRACT: NEURAL NETWORK-BASED COAL ACTIVITY SAFETY STANDARDIZATION ANALYSIS
Fei WU
The Society for Organic Petrology (TSOP)
... Input layer Hidden layer U1 Output layer O1 U2 O2 U3 Y O3 U4 Figure. 3. The 3-Layer BP Network Topology for Safety Standard Normalize safety...
2006
Hidden Structure Revealed by a Simple 3D Velocity Model - McAllen Ranch Field, Hidalgo County, Texas
Richard C. Bain
GCAGS Transactions
...Hidden Structure Revealed by a Simple 3D Velocity Model - McAllen Ranch Field, Hidalgo County, Texas Richard C. Bain Hidden Structure Revealed...
2010
ANN Method, A New Approach to Find Potential Bypass Zones in Mature Semberah Field, East Kalimantan
Gunna Satria H. Kusumah, Robhy C. Permana, Ridha S. Riadi, Yoseph R. Apranda
Indonesian Petroleum Association
... by the red blocks in Figure 9. ANN Model Optimization and Validation In order to acquire the optimal ANN model, several configurations of hidden layer...
2018
Artificial Neural Networks for Corrosion Rate Prediction in Gas Pipelines
Sumarni, Deden Supriyatman, Kuntjoro Adjie Sidarto, Rochim Suratman, Rinaldy Dasilfa
Indonesian Petroleum Association
...), four hidden layers and one output layer (contains one node). The output layer is corrosion rate (see Figure 1). Data Processing Data sets were...
2012
Predicting Brittleness for Wolfcamp Shales Using Statistical Rock Physics and Machine Learning; #42566 (2021)
Jaewook Lee, David Lumley
Search and Discovery.com
... to these variables for a more predictive model. In this study, we use the multi-layer feedforward neural network based on the Levenburg-Marquardt algorithm...
2021
Coalbed Methane Production Parameter Prediction and Uncertainty Analysis of Coalbed Methane Reservoir with Artificial Neural Networks
Harry Ramadhan Sumardi, Dedy Irawan
Indonesian Petroleum Association
... HIDDEN LAYER. TABLE 3B. NEURAL NETWORK DESIGN RESULT FOR DOUBLE HIDDEN LAYER. (a) 1 Hi dden La yer Neura l Network Archi tecture (Log‐Log) [9‐3‐1] [9‐4...
2016
Viable Solutions to Overcome Weaknesses of Deep Learning Applications in Production Forecasting: A Comprehensive Review
Y. Kocoglu, S. Gorell
Unconventional Resources Technology Conference (URTEC)
... hidden representations (ℎ 𝑡𝑡 ) of the previous layer (𝐴𝐴 𝑙𝑙 ) are taken as inputs of the next layer (𝐴𝐴 𝑙𝑙+1 ) at each time step as shown...
2022
Estimation of mechanical properties of sandstones from petrographic characteristics using artificial neural networks (ANNs)
Yasin Abdi, Bijan Yusefi-Yegane, Amin Jamshidi
Geological Society of Malaysia (GSM)
... layers of nodes: an input layer, a hidden layer, and an output layer. Different ANN models have been constructed using petrographic properties as inputs...
2021
Abstract: Image Processing of Seismic Lines: a Tool for Stratigraphic Interpretation, by E. L. Rossetti; #90933 (1998).
Search and Discovery.com
1998
Some Applications and Problems of the Seismic Refraction Technique in Civil Engineering Projects in Malaysia
B. K. Lim, S. J. Jones
Geological Society of Malaysia (GSM)
... surveys. Green (1962) showed that even if the velocity of each layer increases with depth, the intermediate layer would be hidden if the thickness...
1982
Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib-Asl
Unconventional Resources Technology Conference (URTEC)
... (ML) methods especially Artificial Neural Networks (ANNs). Cao et al. (2016) proposed an ANN (a hidden layer containing 15 neurons) model using 2...
2022
Fault displacement gradients on normal faults and associated deformation
Alan P. Morris, Ronald N. McGinnis, and David A. Ferrill
AAPG Bulletin
...-scale normal fault. Antonellini M. Aydin A. , 1994 , Effect of faulting on fluid flow in porous sandstones: Petrophysical properties : AAPG Bulletin...
2014
Prediction of Fracture Porosity from Well Log Data by Artificial Neural Network, Case Study: Carbonate Reservoir, DEPOK Field
Okok Wijaya, Pebrian Tunggal P, Ahmat Dafit Hasim, Depta Mahardika, Sungkono, Bagus Jaya S
Indonesian Petroleum Association
... ability of a biological brain. Neural network modeling consists of three main parameters, input layer, hidden layer and output layer. The hidden layer...
2015
Comparison of three Bayesian methods for lithofluid facies prediction using elastic properties
Jingfeng Zhang, Kevin Wolf, Anar Yusifov, Matt Walker, Pedro Paramo, Jeffrey Winterbourne, Reetam Biswas, Atish Roy, Qiang Liu, Xingchao Liu
International Meeting for Applied Geoscience and Energy (IMAGE)
... placed immediately below brine sand. Furthermore, if reliable geologic prior knowledge such as layer thickness and transition patterns is available...
2023
Abstracts: The Gasport Dolomite in the Vicinity of Hamilton, Ontario
Ernest S. Spurgeon
CSPG Bulletin
... Gasport Dolomite member was studied in the Hamilton area. In spite of the masking effect of dolomitization, the stratigraphic relations and results...
1965
Storage Tank: A Hidden Bomb in Oil and Gas Installation
Orig Setianto Hartoyo, Herry Suprapto
Indonesian Petroleum Association
...Storage Tank: A Hidden Bomb in Oil and Gas Installation Orig Setianto Hartoyo, Herry Suprapto IPA17-281-F PROCEEDINGS, INDONESIAN PETROLEUM...
2017
Innovative Deep Autoencoder and Machine Learning Algorithms Applied in Production Metering for Sucker-Rod Pumping Wells
Peng Yi, Xiong Chunming, Zhang Jianjun, Zhang Yashun, Gan Qinming, Xu Guojian, Zhang Xishun, Zhao Ruidong, Shi Junfeng, Liu Meng, Wang Cai, Chen Guanhong
Unconventional Resources Technology Conference (URTEC)
... dynamometer card are selected in the model. These handcrafted features for predicting the well production could lose important and hidden information about...
2019
Machine Learning Applications for Reservoir Pressure Trend Prediction in Potash Area of Delaware Basin
Olabode Thomas Ajibola, James Sheng, Phillip McElroy, Ebru Ulna
Unconventional Resources Technology Conference (URTEC)
... layer, hidden layers, and an output layer. Figure 1. ANN showing X1 to Xn inputs, W1 to Wn corresponding weights, b bias, and f activation function...
2022