Drilling and Completion Anomaly Detection in Daily Reports by Deep Learning and Natural Language Processing Techniques

Hongbao Zhang, Yijin Zeng, Hongzhi Bao, Lulu Liao, Jian Song, Zaifu Huang, Xinjin Chen, Zhifa Wang, Yang Xu, Xin Jin

Unconventional Resources Technology Conference (URTEC)

...”, “grapple” and “bumper”, which are all fishing related tools, that means the model has learned the semantics of words. Convolutional neural network (CNN...

Using deep learning for automatic detection and segmentation of carbonate microtextures

Claire Birnie, Viswasanthi Chandra

International Meeting for Applied Geoscience and Energy (IMAGE)

... on Microsoft’s Common Objects in COntext (COCO) dataset. The resulting model accurately detects and separates a number of crystals observed within...

Modeling Distributed Fiber Optic Sensor Signals Using Computational Rock Mechanics

Christopher S. Sherman, Robert J. Mellors, Joseph P. Morris, Frederick J. Ryerson

Unconventional Resources Technology Conference (URTEC)

... fracture over time is indicated via a dashed line. We simulate six realizations of the hydraulic fracture model, beginning with a low-leakoff reference...

Research on first break picking based on deep learning for DAS-VSP data

Naijian Wang, Yinpo Xu, Yuxin Hou, Yingjie Pan, Mingxing Wang, Chun Zhang, Tianfu Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... network model, optimal sliding window and appropriate network parameters. 2) The linear regression model is used to determine the first break time...

Technical Article: Finding Subtle Traps with Seismic: Interpretative Criteria Clarified

A. Easton Wren

Petroleum Exploration Society of Australia (PESA)

... to what the section should look like. Progressive understanding of the seismic method introduced the concept of the convolutional model: this found...

Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China;

Huijun Wang, Shuangfang Lu, Guiping Zhao

Search and Discovery.com

...Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China; Huijun Wang...

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...

Application of Machine Learning to Facies Classification of Carbonate Core Images

Sharinia Kanagandran

Southeast Asia Petroleum Exploration Society (SEAPEX)

... machine learning to seismic data and managed to decrease interpretation time by up to 80% (Lomas, 2018). Moreover, published research regarding machine...

Abstract: Open-source Python Stack and Tools for Geoscientific Image Analysis and Interpretation -From research to deployment; #91204 (2023)

Mustafa Al Ibrahim

Search and Discovery.com

... Saudi Aramco Abstract Geoscientific images tend to be multi-dimensional in space, time, and data type. Increasingly, they are large in terms of size...

Effectiveness of dip-in DAS observations for low-frequency strain and microseismic analysis: The CanDiD experiment

David W. Eaton, Yuanyuan Ma, Chaoyi Wang, Kelly MacDougall

International Meeting for Applied Geoscience and Energy (IMAGE)

... deployed for these purposes (Boone et al. 2015; Becker et al. 2017; Bourne et al., 2021). Employing the principles of optical time-domain...

Solving seismic inverse problems by an unsupervised hybrid machine-learning approach

Mrinal K. Sen, Arnab Dhara

International Meeting for Applied Geoscience and Energy (IMAGE)

... carried out by iterative data fitting in which the model updates are evaluated by solving the corresponding physics-based forward modeling. Local...

Seismic fault proximity to production

Jesse Lomask, Toby Burrough, Allison Gilmore, Michael Pyrcz

International Meeting for Applied Geoscience and Energy (IMAGE)

... learning can be utilized to learn and model the relationships between well performance, proximity to faults and the various associated fault attributes...

Abstract: Real-time Bit Wear Prediction and Deployment Validation in Challenging Hard and Heterogeneous Sandstones using 3D Detailed and Simplified Physics-Based Progressive Wear Models; #91204 (2023)

Guodong (David) Zhan, William B. Contreras Otalvora, Xu Huang, Reed Spencer, John Bomidi

Search and Discovery.com

... wear model implements the equivalent single cutting element and reduces the simulation time by more than 90%. It is observed the physics and the learning...

Generating high-quality labels for deep learning CO2 monitoring using local orthogonalization

Shuang Gao, Sergey Fomel, Yangkang Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... at Austin SUMMARY This paper presents an innovative approach to generating structured labels from 4D time-lapse seismic data, a crucial step in enhancing...

Applying deep learning for identifying bioturbation from core photographs

Eric Timmer, Calla Knudson, and Murray Gingras

AAPG Bulletin

... to the convolutional layers to reduce model overfitting (Srivastava et al., 2014). Overfitting occurs when the neural network memorizes the data set...

Hydraulic fracture-hit detection system using low-frequency DAS data

Xiaoyu Zhu, Ge Jin, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

... detection convolutional neural network to detect fracture hits in real-time, as trained on synthetic datasets and successfully transferred to field data...

Convolutional Neural Networks for Semantic Segmentation of Micro-Pores in SEM Based Images of Shales

Search and Discovery.com

Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement

Kirill Gadylshin, Ilya Silvestrov, Andrey Bakulin

International Meeting for Applied Geoscience and Energy (IMAGE)

... Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network...

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)

... extracted from 23 well locations across the survey and used as inputs to the proposed DL model. Due to poor S/N ratio and the presence of residual...

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...

Amplitude enhancement of far-offset refractions via machine learning

Lurun Su, Han Wang, Jie Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... refractions using a physical model. Such methods are time-consuming, especially for 3D applications. In this study, we apply a machine learning method...

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...

Multiscale fault and fracture characterization methods

QiQi Ma, Taizhong Duan

International Meeting for Applied Geoscience and Energy (IMAGE)

...      (1) In the Equation (1), F, F-1 are the Fourier transform and inverse transform respectively; D(t) represents the seismic amplitude at time...

Predicting Coiled-Tubing Drilling Dynamics Using Transformers

Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen

Unconventional Resources Technology Conference (URTEC)

... tailored for CTD drilling time series data. This model leverages its ability to learn long-term dependencies and subtle patterns within the data...

Abstract: Automated Fault Detection from 3-D Seismic Using Artificial Intelligence „ Practical Application and Examples from the Gulf of Mexico and North Slope Alaska;

Andrew Pomroy, Zachary Wolfe

Search and Discovery.com

... in the realm of seismic attributes given its well established strengths in image pattern analysis and recognition. With this in mind, a Convolutional...