Abstract: Machine Learning Receiver Deghosting - Shallow Water OBN Data Example; #91204 (2023)

Rolf Baardman, Rob Hegge, Jewoo Yoo

Search and Discovery.com

...: The proposed supervised ML-method uses a convolutional neural network (CNN) with a two-channel input layer (P and Vz) and an output layer containing the up...

Abstract: Neural Networks Facilitate Precise at - Bit Formation Detection Suitable for Deployment in Automated Drilling Systems; #91204 (2023)

Lucas Katzmann, Stefan Wessling, Matthew Forshaw, Joern Koeneke

Search and Discovery.com

... an alternative, data-driven solution using a multi-layer supervised machine learning model to identify such formation changes. Methods Analysis...

Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025)

Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu Liao

Search and Discovery.com

...Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025) Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu...

Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs

Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib-Asl

Unconventional Resources Technology Conference (URTEC)

... shale as the model was physically more complex than the one in ES–SAGD. However, the ANN models showed successful prediction performance after applying...

High-fidelity GPR image super-resolution via deep-supervised machine learning

Kai Gao, Carly M. Donahue, Bradley G. Henderson, Ryan T. Modrak

International Meeting for Applied Geoscience and Energy (IMAGE)

... migration images. To achieve this task, we adopt an attention-based residual convolutional neural network as the backbone (Bi et al., 2021), which uses...

Chapter 2: Basics of Reflection Seismology that Relate to Seismic Stratigraphy

Tom Wittick

North Texas Geological Society

... for those prospective signatures. The Convolutional Model Figure 2-4 is a cartoon showing the relationship between a lithologic column...

Basics of Reflection Seismic Technology

Abilene Geological Society

... for those prospective signatures. The Convolutional Model Figure 2-4 is a cartoon showing the relationship between a lithologic column...

Shaking up the Earth: The AI revolution in seismic interpretation

Ryan Williams

GEO ExPro Magazine

... for seismic interpretation is much the same despite the complex challenges. Geoteric AI seismic interpretation powered by multiple 3D convolutional neural...

Enhancement of the reliability of the ant-tracking algorithm via U-net and dual-threshold iteration

Seunghun Choi, Yongchae Cho

International Meeting for Applied Geoscience and Energy (IMAGE)

... squared error, root mean squared error) to determine the most effective for model training, and the Mean Squared Error function excelled in five...

Application of Artificial Intelligence for Depositional Facies Recognition - Permian Basin

Randall Miller, Skip Rhodes, Deepak Khosla, Fernando Nino

Unconventional Resources Technology Conference (URTEC)

... in the Permian Basin. Training sets of core facies were selected by a sedimentologist. A model was built using a convolutional neural network...

Lithofacies identification in cores using deep learning segmentation and the role of geoscientists: Turbidite deposits (Gulf of Mexico and North Sea)

Oriol Falivene, Neal C. Auchter, Rafael Pires de Lima, Luuk Kleipool, John G. Solum, Pedram Zarian, Rachel W. Clark, and Irene Espejo

AAPG Bulletin

... of convolutional neural networks (CNNs) using semantic segmentation architectures to automate the identification of common lithofacies from core images. Images...

Correlating Versus Inverting Vibroseis Records: Recovering What You Put into the Ground; #41577 (2015)

Glen Larsen, Paul Hewitt, Art Siewert

Search and Discovery.com

...) based on work of Allen et al. (1998). In effect, spiking the trace reduces it to a phase only operator. The usual vibroseis convolutional model is: x...

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...

Accelerate Well Correlation with Deep Learning; #42429 (2019)

Bo Zhang, Yuming Liu, Xinmao Zhou, Zhaohui Xu

Search and Discovery.com

... patterns (such as upward fining and coarsening) in neighboring wells and links them using a conscious or subconscious stratigraphic sequence model...

Identifying geologic facies through seismic dataset-to-dataset transfer learning using convolutional neural networks

Joseph Stitt, Adam Shugar, Rachael Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... with high and relevant levels of accuracy using deep convolutional neural networks to create the pretrained model. The high degree of similarity between...

Multiscenario-based deep learning workflow for high-resolution seismic inversion on Brazil presalt 4D

Yang Xue, Dan Clarke, Kanglin Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... model and 1D convolutional modeling. The training datasets are generated from scenario-based modeling with each group trained separately with a DL...

Boosting self-supervised blind-spot networks via transfer learning

Claire Birnie, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... networks that learn a pixel’s value based on neighbouring pixels, we propose to train a supervised model in a blind-spot manner such that the model learns...

SaltCrawler: AI solution for accelerating velocity model building

Engin Alkan, Yihua Cai, Pandu Devarakota, Apurva Gala, John Kimbro, Dean Knott, Gislain Madiba, Jeff Moore

International Meeting for Applied Geoscience and Energy (IMAGE)

...SaltCrawler: AI solution for accelerating velocity model building Engin Alkan, Yihua Cai, Pandu Devarakota, Apurva Gala, John Kimbro, Dean Knott...

Joint 3D inversion of gravity and magnetic data using deep learning neural networks

Nanyu Wei, Dikun Yang, Zhigang Wang, Yao Lu

International Meeting for Applied Geoscience and Energy (IMAGE)

... uses a supervised deep neural network, developed based on fully convolutional networks and further combined with a U-Net architecture. Two multi-model...

A strategy for acoustic impedance direct inversion in depth domain

Ruiqian Cai, Chengyu Sun, Shizhong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the depth-domain seismic data, the traditional convolutional model cannot be used to calculate the synthetic seismogram in depth domain. Therefore...

Deep learning decomposition for null and active space estimation for thin-bed reflectivity inversion

Kristian Torres, Mauricio D. Sacchi

International Meeting for Applied Geoscience and Energy (IMAGE)

... stochastic gradient descent with 400 epochs and a learning rate of 0.001. Relying on the convolutional model of the seismic trace, we attempt to recover...

Training data versus deep learning architectures in the seismic fault attribute computation

Bo Zhang, Yitao Pu, Zhaohui Xu, Naihao Liu, Shizhen Li, Fangyu Li

International Meeting for Applied Geoscience and Energy (IMAGE)

...” and predicted result of side outputs (o1-o4). The model generates a “fuse” set to combine the side outputs at different scales. It is common that one...

How Machine Learning is Helping Seismic Structural Interpreters in The Age of Big Data

Çağil Karakaş, James Kiely

GEO ExPro Magazine

... is a very time-consuming task, often leading to a simplified fault model, a geology-driven, machine-learning workflow can significantly improve...

Convolution model theory-based intelligent AVO inversion method for VTI media

Yuhang Sun, Yang Liu, Hongli Dong

International Meeting for Applied Geoscience and Energy (IMAGE)

... network technology and propose an intelligent seismic AVO inversion method founded on the convolutional model theory. The proposed method formulates...

Massive focal mechanism solutions from deep learning in west Texas

Yangkang Chen, Omar M. Saad, Alexandros Savvaidis, Fangxue Zhang, Yunfeng Chen, Dino Huang, Huijian Li, Farzaneh Aziz Zanjani

International Meeting for Applied Geoscience and Energy (IMAGE)

... to quantitatively pick the first-motion polarity using a pre-trained model from a rela- tively high-quality dataset. The fundamental principle of deep...