An Introduction to Deep Learning: Part II

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... often the model fails to predict the correct answer in their top five guesses (the top-5 error rate), in descending order of confidence. ILSVRC 2012...

Using Machine Learning to Automate FDI Analysis

Reid Thompson, Lance Legel, Thomas Hanlon

Unconventional Resources Technology Conference (URTEC)

... is an automated stage detection model. The core of the stage detection model is a onedimensional deep convolutional U-net neural network with residual layers...

GPU-based 3D anisotropic elastic modeling using mimetic finite differences

Harpreet Singh, Jeffrey Shragge, Ilya Tsvankin, Fatmir Hoxha

International Meeting for Applied Geoscience and Energy (IMAGE)

...., and B. Tapley, 2017, Solving the tensorial 3D acoustic wave equation: A mimetic finite-difference time-domain approach: Geophysics, 82, no. 4, T183...

Boulder prediction for offshore windfarm site evaluation using an interactive 2D CNN and a unique weighting scheme on unmigrated seismic

Samuel Chambers, Jesse Lomask

International Meeting for Applied Geoscience and Energy (IMAGE)

.... This gives the model a basic understanding of what to look for, and how to create the segmented output. The basic convolutional synthetic data was created...

MACHINE LEARNING UTILIZATION FOR ENHANCED SUCKER ROD PUMP DYNACARD RECOGNITION

Fadhila Tanjungsari, Hilman Lazuardi, Bonni Ariwibowo, Indra Sukmana, and Candra Kurniawan

Indonesian Petroleum Association

...% testing datasets, and 10% validation datasets. Different machine learning algorithms were evaluated, and it is found that the top performing model...

Improving Depth Prediction Accuracy of Quantified Drilling Hazards

W. Scott Leaney, William H. Borland

Geological Society of Malaysia (GSM)

... problem without a forward problem, and the forward problem underlying seismic trace inversion is the convolutional model. A processed seismic trace...

A Deep Learning Workflow for Integrated Geological, Petrophysical, and Geomechanical Interpretation

Vanessa Simoes, Atul Katole, Bhuvaneswari Sankaranarayanan, Tao Zhao, Aria Abubakar

Unconventional Resources Technology Conference (URTEC)

... wells, which serve as the basis for training an DL model for marker propagation. This model predicts major markers on the remaining wells, and a marker QC...

Complete detection of small earthquakes uncovers intricate relation between injection and seismicity

Yangkang Chen, Alexandros Savvaidis, Omar M. Saad, Daniel Siervo, Dino Huang, Yunfeng Chen, Iason Grigoratos, Sergey Fomel, Caroline Breton

International Meeting for Applied Geoscience and Energy (IMAGE)

... earthquake compact convolutional transformer (EQCCT) for training a model to pick the P- and S-wave from 3-C earthquake waveforms in Texas (Hassani et...

VSP Guided Reprocessing and Inversion of Surface Seismic Data

R. Gir, Dominique Pajot, Serge Des Ligneris

Southeast Asia Petroleum Exploration Society (SEAPEX)

... in wavelets are the more statistical aspects of wavelets processing. This effect is usually time varying. The common interpretive model for surface...

Abstract: Artificial Intelligence for the Characterization of Paleokarst Features in the Barra Velha Formation, Santos Basin: An Approach Based on Convolutional Neural Networks; #91215 (2026)

I. L. de Jesus, S. Arauco, V. Mattoso, L. Sinimbu, M. Blauth, A. Soares, M. Becker, P. Cruz, A. C. Sanchetta, L. Mendoza, M. A. Pacheco, F. Shecaira, L. R. Tedeschi, C. Roisenberg, L. Rodrigues, L. Gandini, J. Zhao, A. Souza

Search and Discovery.com

... on Convolutional Neural Networks; #91215 (2026) I. L. de Jesus, S. Arauco, V. Mattoso, L. Sinimbu, M. Blauth, A. Soares, M. Becker, P. Cruz, A. C...

Impact of an Integrated Seismic Data Processing Approach: A Case Study in Central Sumatra

Mars E. Semaan, Eddy Murhantoro, Maryanto, Achmad Bermawi, Budi Subianto, Hari Santoso

Indonesian Petroleum Association

... traces are the result of convolving a wavelet with a reflection coefficient series, as defined by the convolutional model. Given a seismic trace...

Volumetric Calculations Using 3D Seismic Calibrated Against Porosity Logs - Pretty Hill Formation Reservoirs, Onshore Otway Basin

P. J. Boult, J. Donley

Petroleum Exploration Society of Australia (PESA)

... be considered an extension of the conventional post-stack inversion process. The latter method uses a convolutional model, which is based on an extracted...

Abstract: A Deep Learning Saturation Imaging Framework to Optimize Reservoir Contact While Drilling; #91204 (2023)

Abdallah AlShehri, Klemens Katterbauer, Ali AlYouesf

Search and Discovery.com

... framework for the optimization of hydrocarbon contact while drilling. The framework utilizes a deep learning convolutional imaging framework in order...

Adaptive Eigenstructure Classification and Stochastic Decorrelation Filters for Coherent Interference Suppression in the Acoustic Zoom Method, #41503 (2014).

J. Guigne, S. Azad, C. Clements, A. Gogacz, W. Hunt, A. Pant, J. Stacey

Search and Discovery.com

.... A time window (Figure 2) of a flattened gather of data is defined as The Acoustic Zoom (AZ) seismic beamforming method is an unconventional oil and gas...

Harnessing AI and Computer Vision for Efficient Geothermal Field Exploration and Prospect Evaluation

Moamen Gasser, Danny Rehg, Marcus Oesterberg, Nathan Meehan

Unconventional Resources Technology Conference (URTEC)

.... The model showed a great alliance with the experts’ prediction of the geothermal potential presence. This AI tool is an immensely powerful tool especially...

Abstract: Kirchhoff Imaging with Adaptive Greens Functions for Compensation for Dispersion, Attenuation, and Velocity Imprecision; #90187 (2014)

Andrew V. Barrett

Search and Discovery.com

... frequencies appear to propagate at the velocity of the asymptotic high frequency. If we know the attenuation constant ‘Q’, and if the model for attenuation...

Automated active learning for seismic facies classification

Haibin Di, Leigh Truelove, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

..., 521–535, doi: https://doi.org/10.1111/1365-2478.12865. Di, H., Z. Li, and A. Abubakar, 2022, Using relative geologic time to constrain convolutional...

Multiple, Diffractions and Diffracted Multiples in the South China Sea: How Dense Does Our Acquisition Geometry Need to be? (Geophysics Paper 16)

Rosemary K Quinn, Lynn B Comeaux

Geological Society of Malaysia (GSM)

... be horizontal over the scale of the SRME aperture in order for the model to be predicted accurately. Clearly, this is rarely the case, but as long...

Improved Resolution of Thin Turbiditic Sands in Offshore Sabah with Bandwidth Extension … A Pilot Study (Paper C11)

G. Yu, N. Shah, M. Robinson, N. H. Nghi, A. A. Nurhono, G. S. Thu

Geological Society of Malaysia (GSM)

... by a convolutional-like process in the CWT domain as illustrated in Figure 2. This effectively reshapes the wavelet and broadens the spectrum. Any...

Deep learning-based raster digitization engine

Atul Laxman Katole, Purnaprajna Mangsuli, Omkar Gune, Mohd Saood Shakeel, Abhiman Neelakanteswara, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... methods used to digitize the raster images are extremely time-consuming and prone to human error. The authors propose a fully automated robust...

Assessment of Micro-Fracture Density using Combined Interpretation of NMR Relaxometry and Electromagnetic Logs

Lu Chi, Marcus, Elliot, Zoya Heidari, Mark Everett

Unconventional Resources Technology Conference (URTEC)

... from the time-convolutional form of Ohm’s law using a Reimann-Liouville derivative. It is displayed below in Equation (6): σ ∂ ∂ σβ *E = β ∂t Γ ( β...

Deep Learning Applied to Fault Interpretation and Attribute Computation

Search and Discovery.com

ABSTRACT: Seismic Heterogeneity Cubes and Corresponding Equiprobable Simulations; #90013 (2003)

Matthias Imhof, William Kempner

Search and Discovery.com

... attributes. Instead, model statistics with only six parameters are fitted to the raw statistics. These six parameters include three orthogonal...

Abstract: A Transfer Learning Approach to Rock Property Estimation Workflows;

Ahmad Mustafa, Motaz Alfarraj, Ghassan Alregib

Search and Discovery.com

.... This results in vertical discontinuities in the computed property volumes using such a model, since it becomes sensitive to lateral changes...

Multi-Modal Neural Network for Porosity and Permeability Estimation in Tight Gas Reservoirs: A Case Study in the Ordos Basin, China

Shengjuan Cai, Yitian Xiao, Han Wang, Feifei Gou, Hanqing Wang, Yujie Zhou, Tianrui Ye

Unconventional Resources Technology Conference (URTEC)

... capture vertical and lateral variations across the reservoir. The network is designed to handle these multimodal inputs, with convolutional layers...