Applying deep learning for identifying bioturbation from core photographs

Eric Timmer, Calla Knudson, and Murray Gingras

AAPG Bulletin

.... Salakhutdinov, 2014, Dropout: A simple way to prevent neural networks from overfitting: Journal of Machine Learning Research, v. 15, no. 56, p. 1929–1958...

Three common statistical missteps we make in reservoir characterization

Frank Male and Jerry L. Jensen

AAPG Bulletin

... that evaluates the log-transformed response variable, such as R2 values and artificial neural networks. Figure 6. (A) The least squares linear regression...

GEO India 2008 Conference and Exhibition, September 16-19, 2008, New Delhi, India - Abstracts, 90081 (2008)

Search and Discovery.com

Unconventional Multi-Variate Analysis: A Non-Linear Review of the Most Relevant Unconventional Plays in the U.S.; #80428 (2014)

Roderick Perez

Search and Discovery.com

... • Linear • Principal Component Analysis • Non-Linear • Neural Networks Copyright © 2014, Drilling Info, Inc. All right reserved. All brand names...

Applying Machine Learning Methods to Study Compartmentalization in Complex Reservoirs Based on Static Pressure Information; #42473 (2019)

Jose Victor Contreras

Search and Discovery.com

..., p. 78-82. Kiviluoto, K., 1996, Topology Preservation in Self-Organizing Maps: Proceeding of International Conference on Neural Networks (ICNN), p...

Sequence-Stratigraphic, Petrophysical, and Multicomponent Seismic Analysis of a Shelf-Margin Reservoir: San Andres Formation (Permian), Vacuum Field, New Mexico, United States

Matthew J. Pranter, Neil F. Hurley, Thomas L. Davis

AAPG Special Volumes

... where porosity and estimated permeability data are available or can be determined, for example, using neural networks. The method is especially...

Extended Abstract: Uncertainity Analysis in The Sawan Static Reservoir Model and Optimization of Facies Using Neural Network Technology*; #90139 (2012)

Attique ur Rahman and Muhammad Ibrahim

Search and Discovery.com

...Extended Abstract: Uncertainity Analysis in The Sawan Static Reservoir Model and Optimization of Facies Using Neural Network Technology*; #90139...

Core Hardness Testing and Data Integration for Unconventionals

Elizabeth Ritz, Matt Honarpour, William F. Dula, Jack P. Dvorkin

Unconventional Resources Technology Conference (URTEC)

... the theory of neural networks to find connections between hardness and porosity, density, grain size, and rock type. Daniels et al. (2012) tested...

Probabilistic Facies Assignments in the La Luna Formation, Middle Magdalena Basin, Colombia, from Standard Well Logs Using Whole Core CT Scan Data as Initialization Input

Eric Eslinger, Maria Cantisano, Nelbett Marfisi, Roger M. Slatt, Zarith Pachon

Unconventional Resources Technology Conference (URTEC)

... Valle Medio de Magdalena, Colombia, Reporte Final, for EcoPetrol, 84 p. Perlovsky, L. I. and McManus, M., 1991, Maximum Likelihood Neural Networks...

Integration of Downhole Geophysical and Lithological Data from Coal Exploration Drill Holes

Brett J Larkin

Petroleum Exploration Society of Australia (PESA)

... one or two were based on using neural networks (Chang et al., 2000; Cram et al., 1995). Even though these The Australasian Exploration Geoscience...

Artificial intelligence techniques to the interpretation of geophysical measurements

Desmond FitzGerald

Petroleum Exploration Society of Australia (PESA)

...-solving mechanism in artificial intelligence. In particular, Convolutional Neural Networks (CNN) take a series of input grids, multiplies each input...

The Geosciences DeVL Experiment: new information generated from old magnetotelluric data of The University of Adelaide on the NCI High Performance Computing Platform

Nigel Rees, Ben Evans, Graham Heinson, Dennis Conway, Rui Yang, Stephan Thiel, Kate Robertson, Kelsey Druken, Bruce Goleby, Jingbo Wang, Lesley Wyborn, Hoël Seillé

Petroleum Exploration Society of Australia (PESA)

... of magnetotelluric data: Computers & Geosciences, 113, 94-105. Manoj, C. and Nagarajan, N., 2003, The application of artificial neural networks to magnetotelluric...

Full-waveform inversion with time-dependent receiver-extension: An efficient inner loop optimization

Mustapha Benziane, Romain Brossier, Ludovic Métivier, Serge Sambolian

International Meeting for Applied Geoscience and Energy (IMAGE)

... of ICNN’95 - International Conference on Neural Networks, vol. 4, 1942–1948. Leung, Y.-W., and Wang, Y., 2001, An orthogonal genetic algorithm...

Quantification of Recovery Factors in Downspaced Shale Wells: Application of a Fully Coupled Geomechanical EOS compositional Simulator

Saurabh Sinha, Deepak Devegowda, Bhabesh Deka

Unconventional Resources Technology Conference (URTEC)

... Journal 14 (3): 423–30. doi:10.2118/110845-PA. Kennedy, J, and R Eberhart. 1995. “Particle Swarm Optimization.” Neural Networks, 1995. Proceedings...

Deep learning-based 3D microseismic event direct location using simultaneous surface and borehole data: An application to the Utah FORGE site

Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... main components: a single Convolutional Neural Network (CNN) for feature extraction, an encoder-decoder transformer for reasoning features altogether...

Stratigraphic Modeling Using Common-Sense Rules

Ulf Nordlund

Special Publications of SEPM

... 247 266 KOSKO 8 1992 Neural networks and MASTERS T 1993 Press p 279 NORDLUND U 1990 platforms Geology Compuler modelling the internal v 18 p...

Outcrop to Subsurface Reservoir Characterization of the Mississippian Sycamore/Meramec Play in the SCOOP Area, Arbuckle Mountains, Oklahoma, USA

Benmadi Milad, Roger Slatt

Unconventional Resources Technology Conference (URTEC)

... in the Anadarko Shelf to automatically classify the lithofacies using convolutional neural networks. Our work in this study concentrates on outcrop...

Well Spacing Optimization for Permian Basin Based on Integrated Hydraulic Fracturing, Reservoir Simulation and Machine Learning Study

Leizheng (Kevin) Wang, Alexander Y. Sun

Unconventional Resources Technology Conference (URTEC)

... ensemble regression or neural networks (Rastogi, Agarwal, Lolon, Mayerhofer, & Oduba, 2019; Schuetter, Mishra, Lin, & Chandramohan, 2019) methods have...

New Exploration Technology for South Texas

Perry O. Roehl , Emil Y. Ostrovsky , Alexander Weinberg

GCAGS Transactions

... of forecasting such as "Pattern Recognition" and "Neural Networks." The geological expert applies his or her talents to the unbiased results of TF...

Understanding Unconventional Reservoirs With an Integrated Physics and Machine Learning Based Methods … Case Studies and Novel Approaches From Tight Gas and Tight Oil Reservoir

Gaurav Sharma, Breandan Gaffney, Jaron Van Dijken, Jim Le, Marshall Doig

Unconventional Resources Technology Conference (URTEC)

... (LSTM) based neural networks. Area overview 1. Kaybob Duvernay The Duvernay is a late Devonian-aged formation covering a vast area of the western Canadian...

Physics-Informed Deep Learning Models for Improving Shale and Tight Forecast Scalability and Reliability

Kainan Wang, Lichi Deng, Yuzhe Cai, Guido Di Federico, Keith Ramsaran, Mun-Hong Hui, Hussein Alboudwarej, Christian Hager, Yuguang Chen, Xian-Huan Wen

Unconventional Resources Technology Conference (URTEC)

... workflow. Deep Learning Model Training We train the proxy models with Recurrent Neural Networks (RNN). Instead of a vanilla RNN, we incorporate various...

Integration of Sedimentology and Stratigraphy in a 3D Static Model: Example from the Echinocyamus Formation in the Block X, Talara Basin, NW Perú; #20099 (2011)

José Daudt, José Rejas, Eris Gabriel, Jorge Galloso, Christian Huapaya, Cristian Grobav

Search and Discovery.com

... Methodology (FZI), associated with NNW (Neural NetWorks). The input data (basic petrophysical analysis) were initially constrained by the stratigraphic...

Finding the Key Drivers of Oil Production Through SAS Data Integration and Analysis

Beau Rollins, Mathew Herrin

Unconventional Resources Technology Conference (URTEC)

... evaluation included decision trees, gradient boosting decision trees, neural networks, and stepwise multiple logistic regression. The effective...

A Deep Learning Approach to Predicting Remaining Useful Life for Downhole Drilling Sensors Using Synthetic Data Generation

Aamir Bader Shah, Yu Wen, Jiefu Chen, Xuqing Wu, Xin Fu

Unconventional Resources Technology Conference (URTEC)

... Artificial Neural Networks. Sensors 21 (3): 932. doi:10.3390/s21030932...

Auto-identification and Real-time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing

Mingze Zhao, Yue Li, Yuyang Liu, Bin Yuan, Siwei Meng, Wei Zhang, He Liu

Unconventional Resources Technology Conference (URTEC)

... building a fracturing well database to analyze fracturing parameters and establish a real-time warning model using neural networks. This model is trained...