The Geophysical Case History of Rengasdengklok Area, North West Java

Basuki Puspoputro, Emir Lubis

Indonesian Petroleum Association

... No. 506.78.03. Robinson, E.A., 1983. Seismic velocity analysis and the convolutional model. IHRDC, Boston. Schlumberger, 1987. Well Seismic Service Processing...

Deep learning based microearthquake location prediction at Newberry EGS using physics-informed synthetic dataset

Zi Xian Leong, Tieyuan Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

...-velocity model to simulate physicsinformed synthetic MEQ events and corresponding acoustic waveforms. We introduce a deep learning-based method namely...

An integrated workflow of improving the accuracy of first arrivals picking via deep learning

Yitao Pu, Bo Zhang, Chenglin Wei, Yingyu Xu, Hongfei Liu

International Meeting for Applied Geoscience and Energy (IMAGE)

... learning. Firstly, we compute a probability image by applying a model, which is trained using the Historically nested U-Net (HUnet), to the seismic shot...

S-wave velocity prediction using a deep learning scheme and attention mechanism

Gang Feng, Wen-Qin Liu, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... several limitations, such as poor model generalization, inadequate exploration of logging curve patterns. In this study, a novel approach based on one...

Time-lapse seismic data shaping with transformer encoder neural networks

Jorge E. Monsegny, Daniel O. Trad, Don C. Lawton

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Alali et al. (2021) use convolutional neural networks to perform this filtering, while Alali et al. (2022) employ recurrent neural networks to shape...

Innovative Deep Autoencoder and Machine Learning Algorithms Applied in Production Metering for Sucker-Rod Pumping Wells

Peng Yi, Xiong Chunming, Zhang Jianjun, Zhang Yashun, Gan Qinming, Xu Guojian, Zhang Xishun, Zhao Ruidong, Shi Junfeng, Liu Meng, Wang Cai, Chen Guanhong

Unconventional Resources Technology Conference (URTEC)

.... The machine-learning model contains two neural networks: first, a deep autoencoder to extract the feature representations from all the dynamometer...

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...

Digital Innovation in Subsea Integrity Management

Ricky Thethi, Dharmik Vadel, Mark Haning, Elizabeth Tellier

Australian Petroleum Production & Exploration Association (APPEA) Journal

... (RNN) and convolutional neural network (CNN) based algorithms have been found (Sundararaman et al. 2018) to work well in developing time domain stress...

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...

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...

Stability criteria for seismic data interpolation artificial neural networks

Yiying Xing, Wenlong Wang, Florian Bossmann, Xuexin An

International Meeting for Applied Geoscience and Energy (IMAGE)

... select three artificial neural networks that have been successfully applied in seismic data interpolation Deep Convolutional Neural Network (DnCNN...

Sedimentology Of Prealpine Flysch Sequences, Switzerland

John F. Hubert

Journal of Sedimentary Research (SEPM)

.... Some of the flysch basins can be differentiated by the proportions of various rock fragments. The model for internal divisions in graded sandstones...

This thin section doesnt exist: On the generation of synthetic petrographic datasets

Ivan Ferreira, Luis Ochoa, Ardiansyah Koeshidayatullah

International Meeting for Applied Geoscience and Energy (IMAGE)

... a geological dataset that is indistinguishable from a real dataset, based on a survey made with domain experts and with an FID Score of 12.49. This model can...

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...

Automated Data and ML Pipelines to Accelerate Subsurface Digitalization

Raj Kannan, Vikas Jain

Unconventional Resources Technology Conference (URTEC)

... change over time rendering an ML model unable to perform as well as it did when it was first trained. Automated escalations to both domain experts...

Automated velocity modeling with domain transformations

Kevin Gullikson, Arnab Dhara, Ram Tuvi, Mrinal K. Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... the data from the shot gather domain to the tau-p domain, then predicting the time-domain velocity model using a U-Net, and finally converting from...

Seismic Data Compression by Variational Autoencoder With Hyperprior

Shirui Wang, Wenyi Hu, Aria Abubakar, Xuqing Wu, Jiefu Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

..., end-to-end approach. Our experimental analyses demonstrate the efficacy of the introduced compression model on both pre-migration and post-migration...

OpenFWI 2.0: Benchmark Datasets for Elastic Full-waveform Inversion

Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... into the inversion significantly expands the model space and introduces increased degrees of freedom, resulting in even more pronounced nonlinearity (Operto et...

Seismic facies segmentation via mask-assisted transformer

Jinlong Huo, Naihao Liu, Zhiguo Wang, Yang Yang, Yijie Zhang, Jinghuai Gao

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the characteristics of seismic reflectors. The application of using convolutional neural networks (CNNs) in seismic facies segmentation is growing rapidly. However, CNN...

GeoStreamer X Delivers Near-Field Multi-Azimuth Dataset for Accurate Lead Characterisation, South Viking Graben, Norway

Cyrille Reiser, Eric Mueller, PGS

GEO ExPro Magazine

... summarised below: • Comprehensive demultiple sequence addressing the short and long period multiples integrating 3D convolutional and wave equation...

Hydraulic Fracture Monitoring Using Distributed Acoustic Sensing: A Case Study Using Dip-in Fiber

Chaoyi Wang, Yuanyuan Ma, David Eaton, Kelly MacDougall, Carolyn Furlong

Unconventional Resources Technology Conference (URTEC)

... for frac monitoring, based on the analysis of both microseismic and low-frequency DAS signals. CandDiD-1 is one of the first projects in Canada...

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)

... to successfully model complex geologic spatial patterns (Zhang et al. 2019; Song et al. 2021; 2022). GANs is an unsupervised learning algorithm...

Deep learning to maximize the value of fast-track 4D seismic processing

Arnab Dhara, Haron Abdel-Raziq, Denis Kiyashchenko, Asiya Kudarova, Janaki Vamaraju, Albena Mateeva, Pandu Devarakota, Kanglin Wang, Jorge Lopez

International Meeting for Applied Geoscience and Energy (IMAGE)

... signals requires beating the noise, which may increase the timeline further. This paper leverages deep convolutional neural networks (CNN) to reduce...

Automated active learning for seismic facies classification

Haibin Di, Leigh Truelove, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks have been popularly implemented for seismic image interpretation including facies classification, the performance...

AAPG Annual Convention and Exhibition 2020; - Abstracts, #91200 (2022).

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