Abstract: Integrated Reservoir Characterisation with Three Dimensional Modeling in Thin Bed Low Resistivity Natural Gas Exploration

Yen Jun Lim, Shyh Zung Lo

Geological Society of Malaysia (GSM)

... neural network facies classification, machine learning well correlation, and automated fault interpretation. With the final geological model produced from...

Implementation of Denoising Diffusion Probability Model for Seismic Interpretation

Fan Jiang, Konstantin Osypov, Julianna Toms

International Meeting for Applied Geoscience and Energy (IMAGE)

... In this abstract, we show a novel machine learning-based diffusion model for seismic interpretation. In geophysics, reconstructing the subsurface structure from...

A deep learning workflow for petro-mechanical facies predictions in unconventionals

Noah R. Vento, Enru Liu, Mary Johns

International Meeting for Applied Geoscience and Energy (IMAGE)

... is a subset of machine learning using neural networks, to characterize facies using an unconventional dataset. A 1D U-Net is trained to predict PMFs...

Application of Unsupervised Machine Learning Techniques in Detailed Recognition of Gas Producing Subtle Submarine Channel System

Mariusz Łukaszewski

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Hence the idea of using self-learning techniques in search for seismic facies information that is not provided by any conventional seismic approach...

Application of Unsupervised Machine Learning Techniques in Detailed Recognition of Gas Producing Subtle Submarine Channel System; #42588 (2023)

Mariusz Łukaszewski

Search and Discovery.com

...Application of Unsupervised Machine Learning Techniques in Detailed Recognition of Gas Producing Subtle Submarine Channel System; #42588 (2023...

Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images;

Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott James, Weichang Li

Search and Discovery.com

... the robustness of using microresistivity image logs in a deep-learning method to classify facies as either vuggy or non-vuggy. AAPG Datapages/Search...

Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis

Fan Jiang, Alejandro Jaramillo, Steve Angelovich, Phill Norlund, Julianna Toms

International Meeting for Applied Geoscience and Energy (IMAGE)

... a group of frequencydependent data. A multi-channel, multi-scale convolutional neural network was then performed to train a series machine learning (ML...

Deep learning cross-basin identification of TOC-rich zones in shale formations

Adewale Amosu, Yuefeng Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

... bearing rocks. Several artificial intelligence techniques have been developed for predicting TOC. Machine learning models, such as support vector machines...

Machine Learning Based Stereoscopic Triple Sweet Spot Evaluation Method for Shale Reservoirs

Yuxuan Deng, Wendong Wang, Xianfei Du, Yuliang Su, Shibo Sun, Yan Zhang

Unconventional Resources Technology Conference (URTEC)

...., and G. Hinton, 2008, Visualizing data using t-SNE.: Journal of machine learning research, v. 9, no. 11. Xie, Y., C. Zhu, W. Zhou, Z. Li, X. Liu, and M...

Application of random forest algorithm to predict lithofacies from well and seismic data in Balder field, Norwegian North Sea

Hoang Nguyen, Bérengère Savary-Sismondini, Virginie Patacz, Arnt Jenssen, Robin Kifle, and Alexandre Bertrand

AAPG Bulletin

... to evaluate machine learning models on a limited data sample using a resampling procedure (Stone, 1974). One of the most common cross-validation methods...

Integrated Facies Modeling for Unconventional and Tight Reservoirs

Matt Campbell, Rachel Aisner-Williams, Taskin Akpulat, Jorge Estrada, Mark Kittridge, Mitch Pavlovic, Maksym Pryporov

Unconventional Resources Technology Conference (URTEC)

... through a machine-learning classification process to determine log-scale Petrophysical Rock Types (PRT) (Akpulat, 2019). Log-based PRTs provide high...

Interpretation of deep neural networks for carbonate thin section classification

Lukas Mosser, George Ghon, Gregor Baechle

International Meeting for Applied Geoscience and Energy (IMAGE)

... on quantitative petrographic thin section analysis (Ehrlich et al., 1991; Anselmetti et al., 1998; Baechle et al., 2004). More recently, machine learning (ML...

Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion

Sagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, Ilya Tsvankin

International Meeting for Applied Geoscience and Energy (IMAGE)

...Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion Sagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota...

Stratigraphic Control on Oil Field Performance in Clastic Reservoirs of the Norwegian Continental Shelf: An Insight from Machine-learning Techniques, #30612 (2019).

Kachalla Aliyuda, John A. Howell, Adrian Hartley

Search and Discovery.com

...Stratigraphic Control on Oil Field Performance in Clastic Reservoirs of the Norwegian Continental Shelf: An Insight from Machine-learning Techniques...

Transfer Learning Applied to Seismic Images Classification; #42285 (2018)

Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil

Search and Discovery.com

... the transfer learning technique in the seismic facies classification context, using the dataset pre-processing presented in Chevitarese et al. (2018a...

Technology Explained: Artificial Intelligence „ Its Use in Exploration and Production „ Part 2

Barrie Wells

GEO ExPro Magazine

... INTELLIGENCE – ITS USE IN EXPLORATION AND PRODUCTION PART 2 Machine Learning: SHUT TERSTOCK ­Magic or Mathematical Statistics? Dr Barrie Wells Conwy...

Comparison of unsupervised machine learning model to core-observed facies within the basinal shale Late Devonian Duvernay Formation in the Western Canada Sedimentary Basin

Elisabeth G. Rau, Stacy Atchley, David Yeates, Kathy Breen

International Meeting for Applied Geoscience and Energy (IMAGE)

...Comparison of unsupervised machine learning model to core-observed facies within the basinal shale Late Devonian Duvernay Formation in the Western...

Counterfactual uncertainty for high dimensional tabular dataset

Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

... of Electrical and Computer Engineering, Georgia Institute of Technology. SUMMARY With the advent of machine learning (ML) and deep learning in geophysics...

Rapid History Matching of Petroleum Production from Well Logs and 4D Seismic via Machine Learning Techniques in the Norne Field, Offshore Norway

Jones Ebinesan, Greg Smith, Ritu Gupta

Australian Petroleum Production & Exploration Association (APPEA) Journal

...Rapid History Matching of Petroleum Production from Well Logs and 4D Seismic via Machine Learning Techniques in the Norne Field, Offshore Norway...

Chancing Methods to Predict Porosity in a Middle Eastern Carbonate Reservoir from Full-Function Machine-Learning Neural Networks, Seismic Attributes and Inversions

Search and Discovery.com

An Integrated Deep Learning Solution for Petrophysics, Pore Pressure, and Geomechanics Property Prediction

Ehsan Zabihi Naeini, Sam Green, Marianne Rauch-Davies

Unconventional Resources Technology Conference (URTEC)

... such as porosity or URTeC 111 3 pore pressure. Machine learning improves on this by incorporating more information than using only the elastic properties...

New Heterolithic Opportunities - Getting the Most out of a Mature Heartland with Boomerang Methodology and Machine Learning application

Search and Discovery.com

Preliminary Prioritization on Steam Flood Injection in "Kasai" Heavy Oil Field Using Random Forest Regression Method

Muhammad Rafi, Muhammad Faiz, Auranisa Destya

Indonesian Petroleum Association

... to gross). The advantage of using this machine learning method is that it can process large amount of data, get feature selection for variable...

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton

Unconventional Resources Technology Conference (URTEC)

... on a more traditional machine learning workflow using multilinear regression and hand selected attributes. Introduction The objectives of this study...

3D seismic facies clustering through spectral decomposition using unsupervised ML

Orkhan Mammadov, Ilkin Karimli, Konstantin Matrosov, Ruslan Malikov, Izat Shahsenov

International Meeting for Applied Geoscience and Energy (IMAGE)

... decomposition using machine learning: Paper presented at the SPE Caspian Technical Conference and Exhibition, doi: https://doi.org/10.2118/217653-MS...