Application of Deep Learning Neural Networks to Petrophysical Property Prediction: Case Study from the Powder River Basin

Cameron Snow

Unconventional Resources Technology Conference (URTEC)

...Application of Deep Learning Neural Networks to Petrophysical Property Prediction: Case Study from the Powder River Basin Cameron Snow URTeC: 3702980...

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Hanyan Zhang, Max T. Kasumov, Deepak Devegowda, Mark E. Curtis

Unconventional Resources Technology Conference (URTEC)

... of image classification and segmentation, especially with the use of convolutional neural networks (CNN) and their variants. Although computationally...

Internal multiple elimination with an inverse-scattering theory guided deep neural network

Zhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

..., China; 3Modeling and Imaging Laboratory, EPS, University of California, Santa Cruz, USA SUMMARY Deep neural networks can automatically mine specific...

Advanced Imaging and Inversion for Oil Production Estimates in Unconventional Resource Plays

Greg M. Johnson, Paul Miller, David Phillips

Unconventional Resources Technology Conference (URTEC)

... to the distance of the inferred fracturing away from the borehole. Using neural networks to predict Oil production from advanced imaging seismic...

Use of Neurosimulation in Well Placement for the Development of a Hydrocarbon Field

Hector Emilio Barrios Molano

Asociación Colombiana de Geólogos y Geofisicos del Petróleo (ACGGP)

... and consume large amounts of time. This technique forms a bridge between hard-computing and softcomputing. Effectively mixing artificial neural networks...

Unsupervised compensation of spiral-shaped drone magnetic survey using a recurrent convolutional autoencoder

Brett Bernstein, Yaoguo Li, Richard Hammack, Colton Kohnke

International Meeting for Applied Geoscience and Energy (IMAGE)

... desired features from data without the need to generate training labels. Autoencoders may be combined with recurrent neural networks to better capture...

Predicting Reservoir Quality Using Linear Regression Models and Neural Networks

K.M. Love, C. Strohmenger, A. Woronow, K. Rockenbauch

AAPG Special Volumes

...Predicting Reservoir Quality Using Linear Regression Models and Neural Networks K.M. Love, C. Strohmenger, A. Woronow, K. Rockenbauch 1997 47 60 AAPG...

The impact of the synthetic seismic data generation method on automated AI-based horizon interpretation

F. Vizeu, J. Zambrini, A. Canning

International Meeting for Applied Geoscience and Energy (IMAGE)

... 3D seismic data for training neural networks with great variability and show its performance for training a CNN. Introduction In recent years...

Bottom-Hole Flowing Pressure Calculation in Deviated Multiphase Flow Gas Wells Using Artificial Neural Network (ANN) - A Case Study in the Tunu Gas Field, Total E&P Indonésie

Haniyyah Hasna, Dadik Hendra Kusuma

Indonesian Petroleum Association

... neuron, organized as uses within the network are interconnected, so is similar with biological neuron systems. Commonly, neural networks are adjusted...

Transfer Learning with Multiple Aggregated Source Models in Unconventional Reservoirs

J. Cornelio, S. Mohd Razak, Y. Cho, H-H. Liu, R. Vaidya, B. Jafarpour

Unconventional Resources Technology Conference (URTEC)

... techniques to approximate functional input-output correlations. A popular data driven model are neural networks. These are designed to emulate neurons...

Petrophysical Characterization of a Clastic Reservoir in the Middle Magdalena Valley Basin in Colombia Using Artificial Neural Networks and Seismic Attributes, #20440 (2018).

Ursula Iturraran-Viveros and Andres Munoz-Garcia,

Search and Discovery.com

...Petrophysical Characterization of a Clastic Reservoir in the Middle Magdalena Valley Basin in Colombia Using Artificial Neural Networks and Seismic...

Application of Physics Informed Neural Networks to Compositional Modelling

Thelma Anizia Ihunde, Olufemi Olorode

Unconventional Resources Technology Conference (URTEC)

...Application of Physics Informed Neural Networks to Compositional Modelling Thelma Anizia Ihunde, Olufemi Olorode URTEC-208310-MS Application...

Deep Learning Applied to Fault Interpretation and Attribute Computation

Search and Discovery.com

Fracture Characterization of Carbonate Reservoir Using Integrated Sequential Prediction of Artificial Neural Network: Case Study of Salawati Basin Field

Bagus Endar B. Nurhandoko, Nana Djumhana, Khairil Iqbal, Isnaini Rahman, Susilowati, Yoga Hariman

Indonesian Petroleum Association

... to be done by combining seismic rock physics and well log data with the rules of statistics and artificial neural networks. The proposed method...

Regularization for full-waveform inversion by generative diffusion model with score distillation

Bingbing Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

...). In DDPMs, neural networks are trained using Bayesian variational inference, with the training loss function being the Evidence Lower Bound (ELBO...

An automatic velocity picking method based on object detection

Ce Bian, Weifeng Geng, Ping Yang, Pengyuan Sun, Guiren Xue, Haikun Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

...) to map the CMP gather and very fast simulated annealing (VFSA) for training. However, the above neural networks are too simple to approximate the real...

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)

...Estimation of mechanical properties of sandstones from petrographic characteristics using artificial neural networks (ANNs) Yasin Abdi, Bijan Yusefi...

A Siamese network-based full-wave inversion: Application on real data

Omar M. Saad, Randy Harsuko, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... two identical Convolutional Neural Networks (CNNs) with shared weights to ensure consistent feature extraction from observed and simulated data...

An Intelligent Rock Physics Approach for Predicting Permeability Distribution

Bambang Widarsono, Fakhriyadi Saptono, Patrick M. Wong, Suprajitno Munadi

Indonesian Petroleum Association

..., H.A., Fung, C.C. & Gedeon, T.D., 2000. A state-of-the-art review of neural networks for permeability prediction. APPEA Journal, v. 40, no. 1, pp...

ABSTRACT: MULTITHRESHOLDING SEGMENTATION APPLIED TO COAL PETROGRAPHY

Alejandro Restrepo, Astrid Blandón

The Society for Organic Petrology (TSOP)

.... La Jagua seam a) original image b) histogram of image. Multithresholding using Kohonen neural networks c) two partitions of gray level. d) three...

Fracture-cavity carbonate reservoir identification based on channel attention mechanisms

Liuxin Yang, Yongqiang Ma, Guangxiao Deng, Zhen Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks and channel attention mechanisms. We use seismic data and low-frequency impedance data to generate inputs of training...

Combining Sequence Stratigraphy with Artificial Neural Networks to Enhance Regional Correlation and Determination of Reservoir Quality in the Mississippian LimestoneŽ of the Mid-Continent, USA, #42091 (2017).

Elizabeth Elium, G. Michael Grammer, Matthew Pranter

Search and Discovery.com

...Combining Sequence Stratigraphy with Artificial Neural Networks to Enhance Regional Correlation and Determination of Reservoir Quality...

ABSTRACT: Automated Facies Estimation from Integration of Core, Petrophysical Logs, and Borehole Images; #90007 (2002)

Dr. Tanwi Basu, Robert Dennis, Dr. Debnath Basu, Waleed Al Awadi, John S. Isby, Edwin Vervest, Raja Mukherjee

Search and Discovery.com

... through a neural network system. This provides a facies database that can be used to predict facies from log measurements in adjacent uncored wells...

Automatic detection of SEG-Y sampling format errors using machine learning

Sahana Vinayak, Ray Abma, Sergey Fomel

International Meeting for Applied Geoscience and Energy (IMAGE)

... confusion, CSEG Recorder, 29. Walczak, S., and N. Cerpa, 2003, Artificial Neural Networks, Encyclopedia of Physical Science and Technology, 3rd ed.: 631...

An integrated workflow for deep learning-accelerated seismic modelling of the Groningen gas field, the Netherlands

Haibin Di, Vanessa Simoes, Zhun Li, Cen Li, Anisha Kaul, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... relative geologic time to constrain seismic facies classification using neural networks: First International Meeting for Applied Geoscience & Energy...