Insights using machine learning in predicting faults and horizons: A case study onshore Texas

Dan Ferdinand Fernandez, Mustafa Karer, Richard Hearn, Ryan King, Sunil Manikani, Gavin Menzel-Jones

International Meeting for Applied Geoscience and Energy (IMAGE)

... Texas dataset. By employing ML technology through convolutional neural networks (CNNs) trained on real data we predict multiple layers of faults from...

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...

Machine learning and explainable AI for predicting missing well log data with uncertainty analysis: A case study in the Norwegian North Sea

Sushil Acharya, Karl Fabian

International Meeting for Applied Geoscience and Energy (IMAGE)

... the results and understand the importance of each input log. The methods are applied to the specific case of compressional sonic travel time log (DTC...

Incorporating Artificial Intelligence into Traditional Exploration Workflows in the Cooper-Eromanga Basin, South Australia

H. M. Garcia, W. G. "Woody" Leel Jr., M. Riehle, P. Szafian

International Meeting for Applied Geoscience and Energy (IMAGE)

... a detail geological model at the seismic resolution. This was complemented with the structural information from the AI. Combining all the information...

Estimating subsurface geostatistical parameters from surface-based GPR reflection data using a deep-learning approach

Yu Liu, James Irving, Klaus Holliger

International Meeting for Applied Geoscience and Energy (IMAGE)

... generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations. Finally, we apply this technique to a field GPR dataset...

Generalization Capability of Data-driven Deep Learning Models for Seismic Full-waveform Inversion: An Example Using the OpenFWI Dataset

Ayrat Abdullin, Umair Bin Waheed

International Meeting for Applied Geoscience and Energy (IMAGE)

... model, and ill-posedness of the inverse problem. There is a lack of Data-driven approaches have witnessed development for FWI, including multilayer...

HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES

Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov

Journal of Sedimentary Research (SEPM)

.... (2020) for semantic segmentation of the porosity of petrographic thin sections. The U-Net model is a fully connected convolutional neural network...

Automated machine learning first-break picking in the Sichuan Basin „ A case study

Jianfa Wu, Xuewen Shi, Qiyong Gou, Ersi Xu, Dongjun Zhang, Dingxue Wang, Phil Bilsby, Qing Zhou, Rong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... of deeper targets i.e. the reservoir. To arrive at a robust and reliable velocity model from refraction tomography, the consumer of the first-break (FB) time...

Deep Dix: Enhancing interval velocity model estimation through adversarial regularization

Joseph Stitt, Robert Clapp, Biondo Biondi

International Meeting for Applied Geoscience and Energy (IMAGE)

... that Convolutional Neural Networks (CNNs) have successfully generated mappings from low-frequency shot gathers to low-wavenumber Earth model...

Orogenic gold prospectivity mapping using machine learning

Mike McMillan, Jen Fohring, Eldad Haber, Justin Granek

Petroleum Exploration Society of Australia (PESA)

...., 1995, Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361. LeCun, Y., Bengio, Y...

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...

Seismic image resolution enhancement with limited-well datasets using deep learning

Son Phan, Haibin Di, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... post-stack seismic volume is in the time domain with the sampling rate of 4 milliseconds (ms). The recorded logs along the wellbores, sampled every half...

Looking for a simplified and generalized training set in ML applications for gravity modelling

Luigi Bianco, Ciro Messina, Maurizio Fedi

International Meeting for Applied Geoscience and Energy (IMAGE)

... be seen as the building blocks of each gravimetric anomaly. Here, we discuss preliminary results obtained with a Convolutional Neural Network (CNN...

Abstract: Cost Efficient Acquisition to Reduce Coarse Land 3D Line Spacings Through Beyond Nyquist Interpolation and Wavefield Reconstruction for Signal and Noise; #90187 (2014)

Bill Goodway

Search and Discovery.com

... interpolation to actually recover signal and even the more challenging noise wavefield. At the time of its introduction in the 90’s the MegaBin design...

Transfer learning seismic and GPR diffraction separation with a convolutional neural network

Alexander Bauer, Jan Walda, Dirk Gajewski

International Meeting for Applied Geoscience and Energy (IMAGE)

...Transfer learning seismic and GPR diffraction separation with a convolutional neural network Alexander Bauer, Jan Walda, Dirk Gajewski Transfer...

Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach

Baosen Zhang, Shiwang Chen, Yitian Xiao, Laiming Zhang, Chengshan Wang

Unconventional Resources Technology Conference (URTEC)

... based on Transfer Learning (TL) and Convolutional Neural Network (CNN) has the potential to provide accurate real-time sedimentary structure...

A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage

Fangning Zheng, Birendra Jha, Behnam Jafarpour

Carbon Capture, Utilization and Storage (CCUS)

... storage. Using simulated data, we train a U-Net convolutional neural network to learn a mapping between well locations s and spatially distributed model...

Date-driven seismic velocity inversion via deep residual U-net

Yiran Huang, Chuang Pan, Qingzhen Wang, Jun Li, Jianhua Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

... and their seismic record from each type are shown. The velocity model is 2KMx3KM, which is gridded in Nv=201 and Nh=301. The time length of acoustic...

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...

AI: a Game Changer in Seismic Acquisition and Processing

Matt Deighton, Sverre Olsen

GEO ExPro Magazine

... the trained Convolutional Neural Network (CNN) to remove of fraud detection is that the coherent image domain noise. Noise indicated by yellow arrows...

Improving subsalt imaging using mode-converted waves and volumetric coherent noise attenuation in southern Gulf of Mexico

Riaz Alai, Nik Nur Halim C. Soh, M Shah B. Sulaiman, M. Iqbal Supardy, Kien Kok Lee, Sandeep Kumar, Syazwani Bt Suhairi, Christian Brinzer, Frederico Xavier de Melo, Hongyan Li, Iestyn Williams, Hugo Enrique Munoz Cuenca, Nolan Brand, Kate Glaccum, George Zhao, Saeeda Hydal, Khaled Abdelaziz, Emmi Sanchez Vargas

International Meeting for Applied Geoscience and Energy (IMAGE)

... originating from the base of salt bodies. All models are derived from convolutional-based (surface multiples), dip guided selective stacking...

Augmented Intelligence for Geoscience Data in Mature Basins

Jess B. Kozman and Lorena Pelgrin

GCAGS Transactions

... with public domain and proprietary datasets have shown that the application of this Augmented Intelligence embedded data workflow can reduce time...

Uncertainty quantification of single and multi-parameter full-waveform inversion through a variational autoencoder

Abdelrahman Elmeliegy, Mrinal Sen, Jennifer Harding, Hongkyu Yoon

International Meeting for Applied Geoscience and Energy (IMAGE)

...zed data. We use the finite difference time domain software Deepwave (Richardson, 2023) as our PDE solver to perform the forward and adjoin...

Precursory Detection of Casing Deformation and Induced Seismicity in Unconventional Reservoirs, via Real-Time Surface Pressure Data Analytics

Thomas de Boer, Matthew Adams, Andrew McMurray, Giovanni Grasselli

Unconventional Resources Technology Conference (URTEC)

... calibration of the machine learning model to basin-specific geological and stress conditions. The goal of this technology is enabling real-time automation...