Using Deep Learning and Distributed Machine Learning Algorithms to Forecast Missing Well Log Data; #42234 (2018)

Chijioke Ejimuda, Emenike Ejimuda

Search and Discovery.com

... model. The model accuracy was very low (about 10%). However, currently we are using auto encoder and convolutional neural network ResNet deep...

Magnetotelluric inversion using supervised learning trained with random smooth geoelectric models

Lian Liu, Bo Yang, Yixian Xu, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... frequencies and observation stations, noise, and the model equivalence regarding its resolution (Backus & Gilbert 1967; Parker 1983). Geophysicists...

Strike-slip fault skeletonization based on deep learning cascade ant tracking method

Zhipeng Gui, Junhua Zhang, Rujun Wang, Yintao Zhang, Chong Sun, Mei Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Then, utilizing advantages of ant tracking, fault skeletonization also called as fault thinning is operated. Model test and real data application show: (1...

Label-defect tolerance ability of deep learning inversion networks and its applications

Yang Ping, Xu Hunqun, Liu Di, Tao Chunfeng, Wang Chengxiang, Yue Changqing

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion accuracy. Through model testing, we confirm that Deep Learning (DL) networks have the ability to tolerate labels’ defects, which we name...

Artificial intelligence techniques to the interpretation of geophysical measurements

Desmond FitzGerald

Petroleum Exploration Society of Australia (PESA)

... SUMMARY Integration of geology and geophysics thinking requires a common earth model, that accommodates, with errors, all the features from...

Evaluation of AI-enhanced processing for automated passive seismic detection and location

Aaron Booterbaugh, Evgeny Naumov

International Meeting for Applied Geoscience and Energy (IMAGE)

... model and provide access to the rich datasets collected by both public arrays and Nanometrics’s private installations. CONVOLUTIONAL NEURAL NETWORK...

ABSTRACT: Deep forest cover classification of consecutive landsat imageries over Borneo

Azalea Kamellia Abdullah, Mohd Nadzri Md Reba, Nur Efarina Jali, Sikula Magupin, Diana Anthony

Geological Society of Malaysia (GSM)

... learning image classification algorithms such as Convolutional Neural Networks (CNN) attains higher accuracy mapping with low human interruption. Deep...

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)

...) (Yan et al., 2021), the Temporal Convolutional Network (TCN) (Lea et al., 2016), and the transformer model (Vaswani et al., 2017). The efficacy of LSTMs...

Background noise suppression for DAS-VSP data using attention-based deep image prior

Yang Cui, Umair bin Waheed, Yangkang Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... classical (such as wavelet and curvelet) and dictionary-learning techniques, rely on distinguishing signal components in the transform domain, albeit...

Machine-learning based arrival-picking in continuous DAS recordings „ Application to the Utah FORGE EGS project

Nepomuk Boitz, William Tegtow, Serge Shapiro

International Meeting for Applied Geoscience and Energy (IMAGE)

... a Convolutional Neural Network (CNN). In contrast to geophone-based picking methods, we suggest to train CNNs only on data from a specific dataset to best...

Seismic imaging uncertainty using deep learning predicted Greens functions

Han Liu, Anar Yusifov, Muhong Zhou, Linda Hodgson

International Meeting for Applied Geoscience and Energy (IMAGE)

... velocity model. We used U-net architecture (Ronneberger et al., 2015) with 5 layers, and each layer has 2 convolutional layers. Ensemble models were...

Prediction of porosity in CO2 sequestration tight reservoirs based on multi-source and multi-scale data fusion

Ping Lu, Peixue Jiang, Ruina Xu, Huaqing Xue, Haojie Li, Lei Yu, Jiashu Han

International Meeting for Applied Geoscience and Energy (IMAGE)

... it difficult for the reservoir porosity model to simultaneously consider prediction accuracy and generalization performance. Therefore, this study focuses...

A two-stage deep learning workflow for automated seismic inversion

Haibin Di, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... of four major steps, (i) large-scale structural model construction, (ii) initial property model estimation via a multi-task convolutional neural...

Adapting Music Recognition Technology for Tops Picking and Quality Control

Alan Lindsey, Morgan Cox, Aaron Hugen

Unconventional Resources Technology Conference (URTEC)

... stratigraphy due to either faulting or unconformities. This can lead to false positive marker identifications. Convolutional Neural Networks (CNNs) CNNs...

Deep Convolutional Neural Networks for Seismic Salt-Body Delineation

Search and Discovery.com

Unlocking the Potential of Unlabeled Data in Building Deep Learning Model for Dynamometer Cards Classification

Ramdhan Wibawa, Rosyadi Rosyadi, Maulirany Nancy, Muhammad Awqi Gibran, Supriono Hariyadi

Indonesian Petroleum Association

... for building a Convolutional Neural Network (CNN) model using state of the art Self-Supervised Learning (SSL). The need to identify moretypes of failure has been...

Seismic super resolution method for enhancing stratigraphic interpretation

Chengbo Li, Qingrong Zhu, Baishali Roy

International Meeting for Applied Geoscience and Energy (IMAGE)

... 𝒎 and employ the convolutional model to illustrate the method. Assuming the wavelet is stationary within a given interval, the seismic can...

Reservoir prediction using graph-regularized deep learning

Kaiheng Sang, Nanying Lan, Fanchang Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of these explicit formulas are based on strong approximation to the underground media properties, such as convolution model, Aki-Richard approximate...

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

...Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images; Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott...

Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023)

Fatemeh Saberi, Esmael Makarian, Ayub Elyasi, Tomomewo Stanley Olusegun

Search and Discovery.com

...Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023) Fatemeh Saberi, Esmael Makarian...

Accelerating innovation with software abstractions for scalable computational geophysics

Mathias Louboutin, Philipp Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, Felix J. Herrmann

International Meeting for Applied Geoscience and Energy (IMAGE)

...), Julia (Bezanson et al., 2017), and Matlab, to domain-specific languages (DSLs), including RVL (Padula et al., 2009), Firedrake (Rathgeber et al., 2016...

Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

Miguel Corrales, Muhammad Izzatullah, Matteo Ravasi, Hussein Hoteit

International Meeting for Applied Geoscience and Energy (IMAGE)

... originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model...

Pushing the limit of 5D interpolation using deep learning

Yangkang Chen, Hang Wang, Chao Li, Omar M. Saad

International Meeting for Applied Geoscience and Energy (IMAGE)

... prepare the initial model in the COP domain instead of the CMP domain is that, in a COP domain with certain offset values, the travel times for adjacent...

Integrated well data and 3D seismic inversion study for reservoir delineation and description

Qazi Sohail Imran, Numair Ahmad Siddiqui, Abdul Halim Abdul Latif, Yasir Bashir, Almasgari Abdalsalam, Abduh Saeed Ali, Muhammad Jamil

Geological Society of Malaysia (GSM)

... because of its bandlimited nature. A plausible broader band frequency is difficult to build when as the model (known as an a priori model) building...

Orogenic gold prospectivity mapping using machine learning

Mike McMillan, Jen Fohring, Eldad Haber, Justin Granek

Petroleum Exploration Society of Australia (PESA)

... developed a new algorithm for mineral prospectivity mapping using a VNet deep convolutional neural network and applied it to finding gold at the Committee...