Post Migration Processing of Seismic Data

Dashuki Mohd.

Geological Society of Malaysia (GSM)

... or multiples. The basis for deconvolution is the convolutional model (Robinson, 1984). In the convolutional model, a seismic trace is viewed...

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

... tomogram inverted from the synthetic ray path in (C). (F) The 2-D zero-offset convolutional reflection model calculated using a 120-Hz Ricker wavelet...

Deep-learning application of salt geometry detection in deep water Brazil

Ruichao Ye, Anatoly Baumstein, Kirk A. Wagenvelt, Erik R. Neumann

International Meeting for Applied Geoscience and Energy (IMAGE)

... a novel workflow based on a deep convolutional neural network for automatically detecting salt geometry from a seismic image. By developing...

Reservoir Modeling With Deep Learning

Search and Discovery.com

Abstract: FaciesNet: Machine Learning Applications for Facies Classification in Well Logs;

Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed, Irene Espejo

Search and Discovery.com

... information, facies stacking pattern, and geologic correlations, FaciesNet. Our proposed model incorporates decoding and encoding deep convolutional...

Seismic data augmentation for automatic faults picking using deep learning

Nam Pham, Sergey Fomel

International Meeting for Applied Geoscience and Energy (IMAGE)

... these newly generated data for training a convolutional neural network for faults picking, as the training data will resemble the field test data...

2D isotropic and vertical transversely isotropic RTM using SEG Hess VTI Model

Richa Rastogi, Abhishek Srivastava, Monika Gawade, Nithu Mangalath, Laxmaiah Bathula, Bhushan Mahajan, Suhas Phadke

International Meeting for Applied Geoscience and Energy (IMAGE)

...2D isotropic and vertical transversely isotropic RTM using SEG Hess VTI Model Richa Rastogi, Abhishek Srivastava, Monika Gawade, Nithu Mangalath...

GeoMind: An intelligent earth model building tool

Saleh Al Saleh, Ewenet Gashawbeza, Mustafa Marzooq, Hussam Banaja, Husain Al Shakhs, Jianwu Jiao

International Meeting for Applied Geoscience and Energy (IMAGE)

...GeoMind: An intelligent earth model building tool Saleh Al Saleh, Ewenet Gashawbeza, Mustafa Marzooq, Hussam Banaja, Husain Al Shakhs, Jianwu Jiao...

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)

... the numerical model and tied to an underlying finite element mesh. Both the low-frequency strain as created by an opening (or closing) fracture and the high...

Enhancing Lithology Classification through a Deep Learning Framework

P. Zhang, T. Gao, R. Li

Unconventional Resources Technology Conference (URTEC)

..., more data typically improves model accuracy but also increases costs, this research optimizes the utility of existing and common logs. To leverage...

Jointly data and model driven pre-stack inversion of elastic and anisotropy parameters in HTI media

Xin Zhang, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

...Jointly data and model driven pre-stack inversion of elastic and anisotropy parameters in HTI media Xin Zhang, Jianhua Geng Jointly data and model...

Unconventional Reservoir Microstructural Analysis Using SEM and Machine Learning

Amanda S. Knaup, Jeremy D. Jernigen, Mark E. Curtis, John W. Sholeen, John J. Borer IV, Carl H. Sondergeld, Chandra S. Rai

Unconventional Resources Technology Conference (URTEC)

... specifically Convolutional Neural Networks (CNN), are being used for pixel labeling and feature identification using the CNN U-Net. This network...

3D ultra high resolution seismic processing „ A case study from offshore USA

Bertrand Caselitz, Luca Limonta, Julien Oukili, Jonas Tegnander, Vicky Catterall

International Meeting for Applied Geoscience and Energy (IMAGE)

... reflections. The demultiple step, utilizing methods like convolutional 3D Surface Related Multiple Elimination (SRME) and 3D wave-equation multiple...

Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the well

Paige Given, Fantine Huot, Ariel Lellouch, Bin Luo, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, Kurt Nihei

International Meeting for Applied Geoscience and Energy (IMAGE)

... present a convolutional neural network (CNN) which takes inputted images from DAS arrays and accurately detects microseismic events. Our model is able...

3D seismic image-to-image translation

Xiaolei Song, Muhong Zhou, Lifeng Wang, Rodney Johnston

International Meeting for Applied Geoscience and Energy (IMAGE)

... by adopting two convolutional Bayesian layers as the network output layers to analyze the model uncertainties by calculating an uncertainty map from a local...

An integrated machine learning-based fault classification workflow

Jie Qi, Carolan Laudon, Kurt Marfurt

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network...

A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data

Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen

AAPG Bulletin

... at a batch size of 20. Figure 5. Optimum number of batch size (A) and dropout rate (B) for hybrid convolutional neural networks–long short-term memory model...

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...

Application of Machine Learning Methods to Assess Progressive Cavity Pumps (PCPs) Performance in Coal Seam Gas (CSG) Wells

Fahd Saghir, M. E. Gonzalez Perdomo, Peter Behrenbruch

Australian Petroleum Production & Exploration Association (APPEA) Journal

... of Convolutional Auto Encoders (CAE) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) methodologies to characterise...

Abstract: Towards the Identification of Coal Macerals through Deep Learning

Na Xu, Qingfeng Wang, Pengfei Li, Mark A. Engle

The Society for Organic Petrology (TSOP)

... are compared with the other three existing image segmentation methods, including K-means [4], Gaussian mixture model (GMM), [5] and convolutional neural...

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...

Seismic Facies Segmentation Using Deep Learning; #42286 (2018)

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

Search and Discovery.com

... selected a trained convolutional neural network (CNN) with the highest accuracy on the classification task. Then, we modified the final part...

Transfer Learning with Recurrent Neural Networks for Long-term Production Forecasting in Unconventional Reservoirs

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Unconventional Resources Technology Conference (URTEC)

... practical use. In this paper, a deep recurrent neural network (RNN) model is developed for robust long-term production forecasting in unconventional...

Fluid distribution modeling impact on estimating CO2 saturation in Cranfield: A capillary pressure equilibrium approach with invertible neural networks

Sohini Dasgupta, Arnab Dhara, Mrinal K. Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion strategy which uses a capillary pressure based rock physics model with invertible neural networks (INNs) to estimate CO2 saturation...

Convolution Neural Networks … If They can Identify an Oncoming Car, can They Identify Lithofacies in Core?; #42312 (2018)

Rafael Pires de Lima, Fnu Suriamin, Kurt Marfurt, Matthew Pranter, Gerilyn Soreghan

Search and Discovery.com

... drive our cars but also taste our beer. Specifically, recent advances in the architecture of deep-learning convolutional neural networks (CNN) have...