Application of Assisted History Matching Workflow to Shale Gas Well Using EDFM and Neural Network-Markov Chain Monte Carlo Algorithm

Sutthaporn Tripoppoom, Wei Yu, Kamy Sepehrnoori, Jijun Miao

Unconventional Resources Technology Conference (URTEC)

..., 23-25 February. Hagan, M.T., and Menhaj, M., 1994. Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5...

Applications of Machine Learning for Estimating the Stimulated Reservoir Volume (SRV)

Ali Rezaei, Fred Aminzadeh, Eric VonLunen

Unconventional Resources Technology Conference (URTEC)

... decade. The literature contains a wide variety of models, including supervised and unsupervised models. A comprehensive overview of neural networks...

Integrated Shale Gas Reservoir Modeling

C. Mike Du, Xu Zhang, Y. Zee Ma, Peter Kaufman, Brad Melton, Sherif Gowelly

AAPG Special Volumes

... fracturing induced fracture networks in shale gas reservoirs as a dual porosity system: International Oil and Gas Conference and Exhibition, June 810, 2010...

Statistical Analysis of Estimated Ultimate Recovery: Comparing Machine Learning and Traditional DCA Methods in the Eagle Ford and Bakken

Palash Panja, Carlos Vega-Ortiz, Milind Deo, Brian McPherson, Rasoul Sorkhabi

Unconventional Resources Technology Conference (URTEC)

... success in the realm of time series forecasting, leveraging the potential of Artificial Neural Networks (ANN) to construct proxy models(Davis et al...

Machine Learning for Estimating Rock Mechanical Properties beyond Traditional Considerations

Yiwen Gong, Mohamed Mehana, Ilham El-Monier, Feng Xu, Fengyang Xiong,

Unconventional Resources Technology Conference (URTEC)

... network. Proc., SPE Hydrocarbon Economics and Evaluation Symposium. doi:10.2118/68593-MS Alcocer, Yuri and Rodrigues, Patricia. 2001. Neural networks...

Detailed petroleum system insights using deep learning: A case study from the Scarborough Gas Field, offshore Australia

Scotty Salamoff, Julian Chenin, Benjamin Lartigue, Nguyen Phan, Paul Endresen

International Meeting for Applied Geoscience and Energy (IMAGE)

... petroleum system elements by labeling and training networks on associated elements proven by exploration well data. The Scarborough seismic survey within...

Synthetic-data-driven deep learning method for elastic parameter inversion

Shuai Sun, Luanxiao Zhao, Huaizhen Chen, Zhiliang He, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

... adversarial networks with conditional controls are trained by synthetic datasets (SDDCGANs) to establish the relationship between the pre-stack AVO...

Stochastic inversion method based on a priori information of compression-sensing divided-frequency waveform indication

Ying Lin, Siyuan Chen, Guangzhi Zhang, Baoli Wang, Minmin Huang

International Meeting for Applied Geoscience and Energy (IMAGE)

... neural networks to achieve seismic waveform classification and identification of seismic phases. Cai et al. (2018) proposed a new semi-supervised K...

Expert Systems for Gas Production Prediction from Hydraulically Fractured Horizontal Wells based on Different Hydraulic Fracture Representations; #41152 (2013)

Nithiwat Siripatrachai, Kanin Bodipat, and Turgay Ertekin

Search and Discovery.com

... Difficulty in characterization of hydraulic fracture Time constraints in modeling efforts toward optimized field development Artificial Neural Networks...

Exploration and Development based on RTH Technology and AI

Gennady Erokhin, Mariia Erokhina, Kirill Safran, Alexandre Iakovlev

Unconventional Resources Technology Conference (URTEC)

..., the higher the learning rate. Optimization of all calculations in RTH approach using neural networks on graphics accelerators is the further path...

3D GPR data mel-frequency cepstral coefficients features for effective CNN classification of urban utilities

Jide Nosakare Ogunbo, Sang Hun Baek, Sang-Wook Kim

International Meeting for Applied Geoscience and Energy (IMAGE)

... misclassifications because of the nonuniqueness inherent in the restrictive geometrical extent. Therefore, the Convolutional Neural Network classification of 3D GPR...

Integrated Geomechanical Reservoir Characterization Approach to Study Migration and Accumulation of Hydrocarbons in Llanos Basin, Colombia, #40871 (2012)

Valentina Baranova, Azer Mustaqeem, Friso Brouwer, David Connolly

Search and Discovery.com

... of supervised Neural Networks. These technologies are applied to various seismic data sets in the Llanos Basin to better understand the regional stress field...

ABSTRACT: Reducing Dry Hole Risk with Artificial Intelligence

William W. Weiss, Robert A. Balch, Tonjun Ruan, Ronald Broadhead, and Visveswaran Subramaniam

Fort Worth Geological Society

... for use as inputs to a neural network that was trained to correlate the input attributes with the first years oil production. The neural network...

Extracting Lithofacies from Digital Well Logs Using Artificial Intelligence, Panoma (Council Grove) Field, Hugoton Embayment, Southwest Kansas: Abstract

Martin Dubois, Geoffrey Bohling, Alan Byrnes, Shane Seals

Tulsa Geological Society

... reservoirs like the Panoma Field is impractical by traditional methods. In this study, a neural network implemented in the Excel add-in Kipling.xla...

Abstract: Artificial Neural Network Model of Hydrocarbon Migration and Accumulation, by C. Wu, H. Liu, and X. Mao; #90091 (2009)

Search and Discovery.com

Abstract: Neural Network Analysis of Seismic Attributes and Facies at Deep Basin Tight Gas Exploration of WCSB; #90211 (2015)

Derrick McClure, Fenglin Xia, Peter Luxton, and Curtis Booth

Search and Discovery.com

...Abstract: Neural Network Analysis of Seismic Attributes and Facies at Deep Basin Tight Gas Exploration of WCSB; #90211 (2015) Derrick McClure...

Abstract: Hydrocarbon Saturation Prediction from Full-Stack Seismic Data Using Probabilistic Neural Network; #90254 (2016)

Islam Ali Mohamed

Search and Discovery.com

...Abstract: Hydrocarbon Saturation Prediction from Full-Stack Seismic Data Using Probabilistic Neural Network; #90254 (2016) Islam Ali Mohamed AAPG...

Abstract: Integration of Seismic Attributes and Well Logs for Prediction of Reliable Porosity Cube: A Case Study; #91204 (2023)

Rimsha Rauf

Search and Discovery.com

... that combines seismic attribute and probabilistic Neural Network (PNN) to find a suitable relationship for predicting porosity. The flow of the approach...

Stratigraphic Controls on Mississippian Limestone Reservoir Quality through Integrated Electrofacies Classification and Seismic Constrained Spatial Statistics, Barber County, Kansas; #41924 (2016)

Niles W. Wethington, Matthew J. Pranter

Search and Discovery.com

.... This is accomplished through combining and weighting several input variables. Neural Networks are able to effectively construct complex decision boundaries...

A hybrid machine learning model for improving regression of mineral composition estimation using well logging data

Xiaojun Liu, Kezhen Hu, Stephen E. Grasby, Benjamin Lee

International Meeting for Applied Geoscience and Energy (IMAGE)

... compositions by combining a convolutional neural network (CNN) and XGBoost algorithm. The selected inputs are preprocessed into a square matrix of format...

Large Language Model Powered Well Productivity

Sohrat Baki, Serkan Dursun

Unconventional Resources Technology Conference (URTEC)

... Models (NLMs) The advent of neural networks revolutionized language modeling. Neural Language Models (NLMs) brought significant improvements over SLMs...

Fluid Prediction from 3-D Seismic Data in Deepwater Sandstone Reservoirs: Applications from Cocuite Gas Field, Veracruz Basin, Southeastern Mexico

Fouad, Khaled, Jennette, David C., Soto-Cuervo, Arturo

GCAGS Transactions

... and probabilistic neural networks. Second, we calibrate gas saturation from a suite of AVO seismic forward models that vary fluid character between full...

Deep learning based microearthquake location prediction at Newberry EGS using physics-informed synthetic dataset

Zi Xian Leong, Tieyuan Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

... probability distribution: Proceedings of the IEEE International Conference on Neural Networks (ICNN’94), 1, 55–60, doi: https://doi.org...

Perceptual quality-based model training under annotator label uncertainty

Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

... in neural networks: arXiv preprint, doi: https:// doi.org/10.48550/arXiv.2209.08425. Prabhushankar, M., K. Kokilepersaud, Y.-Y. Logan, S. T. Corona, G...

Automatic well-log baseline correction via deep learning for rapid screening of potential CO2 storage sites

Misael M. Morales, Carlos Torres-Verdín, Michael Pyrcz, Murray Christie, Vladimir Rabinovich

International Meeting for Applied Geoscience and Energy (IMAGE)

... coefficients, 𝓢 𝑿 , for a randomlyselected well. Deep convolutional U-Net neural networks have been widely used for computer vision and signal...