Abstract: Can Q Explain Observations Made from a VSP?, by Hamish Wilson, Scott W. Peters, and Robert W. Wiley; #90105 (2010)

Search and Discovery.com

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)

...). Recent developments in ML models for time series analysis have introduced innovative approaches such as Convolutional Neural Networks LSTM (CNN-LSTM...

Deep Convolutional Neural Networks for Seismic Salt-Body Delineation

Search and Discovery.com

Conditioning Stratigraphic, Rule-Based Models with Generative Adversarial Networks: A Deepwater Lobe, Deep Learning Example; #42402 (2019)

Honggeun Jo, Javier E. Santos, Michael J. Pyrcz

Search and Discovery.com

... trend model, parameterized by gradients, orientations, mean, and standard deviation. Our deep learning-based, local data conditioning workflow consists...

Novel application of machine learning assisted fault interpretation to delineate earthquake risk from saltwater disposal in the Midland Basin

Niven Shumaker, Mohammed Afia

International Meeting for Applied Geoscience and Energy (IMAGE)

... seismic survey using a 3D convolutional neural for edge pixels in a 2D data array. Lines that pass through network (Abubakar et al. 2022). Fault points...

Chapter Nine: Inversion and Interpretation of Impedance Data

Rebecca B. Latimer

AAPG Special Volumes

... as layer blocks described by acoustic impedance and, optionally, time. This blocky model is broadband because of the assumption of layers with sharp...

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...

Strong Foundations, Deep Integration, Infinite Possibilities

James Lowell, Peter Szafian, Nicola Tessen

GEO ExPro Magazine

... years and is effective for building a conceptual model of the geology and QC’ing the individual faults and overall interpretation whilst picking...

Auto-identification and Real-time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing

Mingze Zhao, Yue Li, Yuyang Liu, Bin Yuan, Siwei Meng, Wei Zhang, He Liu

Unconventional Resources Technology Conference (URTEC)

... identification and real-time warning method of multiple types of events during multi-stage fracturing. A new intelligent identification model is developed...

Assessing and processing three-dimensional photogrammetry, sedimentology, and geophysical data to build high-fidelity reservoir models based on carbonate outcrop analogues

Ahmad Ramdani, Pankaj Khanna, Gaurav Siddharth Gairola, Sherif Hanafy, and Volker Vahrenkamp

AAPG Bulletin

... was 75 × 28 m. The grid size of the model was made as fine as 10 × 10 cm, while the time step was every 0.1 ns to avoid numerical dispersion for both 50...

Robust Event Recognition in Real-Time Hydraulic Fracturing Data for Live Reporting and Analysis

Samid Hoda, Jessica Iriarte

Unconventional Resources Technology Conference (URTEC)

..., transparent (white box), and extremely performant model that easily accommodates operating constraints. In turn, this enables real-time reporting...

Spatial statistical analysis and geomodelling of banana holes using point patterns and generative adversarial networks

Rayan Kanfar, Charles Breithaupt, Tapan Mukerji

International Meeting for Applied Geoscience and Energy (IMAGE)

... GANs and the Diffusion model. Spatial statistical analysis and deep generative models are explored for Banana holes for the first time. Spatially...

Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018)

Haibin Di, Zhen Wang, Ghassan AlRegib

Search and Discovery.com

...Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018) Haibin Di, Zhen Wang, Ghassan AlRegib Deep Convolutional Neural...

Backscatter analysis of vehicle-generated noise using symmetric autoencoders

Sanket Bajad, Pawan Bharadwaj

International Meeting for Applied Geoscience and Energy (IMAGE)

... among the CCNs within Xr ? To understand this, we assume a convolutional model with a point noise source. We write each CCN as a discrete convolution...

AVA on communication cables: Principles and Examples from the North Sea

Ran Bachrach, Ali Sayed, Andrea Murineddu, Pilar Di Martino

International Meeting for Applied Geoscience and Energy (IMAGE)

...as the 𝑣 𝑥 amplitude vanishes at zero-angle while increasing at larger angles of incidence. The synthetic response of S-DAS was computed in the common shot doma...

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...

Facies-induced bias in machine learning-enhanced seismic lithology (inversion)

Hongliu Zeng, Bo Zhang, Mariana Olariu

International Meeting for Applied Geoscience and Energy (IMAGE)

... tests on the subject. A geologically realistic model is used to quantitively demonstrate the methods to reduce the bias. A field-data test...

Deep-Learning-Based Prediction of Post-Fracturing Permeability Field for Development Strategy Optimization in Unconventional Reservoirs

Jiehao Wang, Yunhui Tan, Baosheng Liang, Xinhui Min, Chaoshun Hu, Chao Zhao, Yuyu Wang, Mike Li, Yuguang Chen, Gerardo Jimenez, Shahzad Khan

Unconventional Resources Technology Conference (URTEC)

... number of wells and geological scenarios. A deep-learning-based model (Artificial Learning Fracture, or ALF) was developed to accelerate the hydraulic...

Optimal control of geological carbon storage using multi-physical monitoring and deep reinforcement learning

Kyubo Noh, Andrei Swidinsky

International Meeting for Applied Geoscience and Energy (IMAGE)

... DRL agents coupled with timelapse AVO, time-lapse gravity, and geostatistical reservoir simulators show that: 1) the trained DRL policy outperforms...

Comparative Algorithm Machine Learning Approaches for Predictive Analysis of Well Log Data: A Case Study in the Central Sumatra Basin

Nungga Saputra, Hafid Rizki Nur Rohman, Puspa Alifya, Patria Ufaira Aprina

Indonesian Petroleum Association

... within this time frame. The KNN model delivered accurate predictions based on the outcomes and metrics test. TABLE 3 METRICS CROSS VALIDATION/TEST...

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...

Adapting Music Recognition Technology for Tops Picking and Quality Control

Alan Lindsey, Morgan Cox, Aaron Hugen

Unconventional Resources Technology Conference (URTEC)

... to the time domain, allowing for audio techniques to be applied. Fingerprints of these audio files were then generated and cataloged in a database...

Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction

Tao Zhao, Haibin Di, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... a convolutional autoencoder as the SSL model to reconstruct the original seismic data. The training samples are 2D seismic patches randomly extracted from...

Abstract: Innovative QCs for More Effective 4D Processing; #90187 (2014)

Cyril Saint Andre, Benoit Blanco, Christian Hubans, and Benoit Paternoster

Search and Discovery.com

... problem in the framework of the 1D convolutional model. This attribute and the following developments were initially detailed by Cantillo (2012) [3...

Earth Model Building in Real-Time with an Automated Machine Learning Framework - A Midland Basin Example

Altay Sansal, Muhlis Unaldi, Edward Tian, Gareth Taylor

Unconventional Resources Technology Conference (URTEC)

...Earth Model Building in Real-Time with an Automated Machine Learning Framework - A Midland Basin Example Altay Sansal, Muhlis Unaldi, Edward Tian...