Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data

Ruizhi Zhong, Raymond L. Johnson Jr., Zhongwei Chen

Unconventional Resources Technology Conference (URTEC)

... (Schmidhuber 2015). Supervised learning is learning a predictive model that maps certain inputs to a desired output. To build the supervised learning...

Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring

Guoxiang Liu, Abhash Kumar, Song Zhao, Chung Yan Shih, Veronika Vasylkivska, Paul Holcomb, Richard Hammack, Jeffery Ilconich, Grant Bromhal

Unconventional Resources Technology Conference (URTEC)

... making in reservoir management (Left, C). This machine learning model optimizes relatively rapidly (Right, A), matches well with observed data (Right...

Fault surface extraction based on computational topology

Cheng Zhou, Cun Yang, Ruoshui Zhou, Xingmiao Yao, Guangmin Hu

International Meeting for Applied Geoscience and Energy (IMAGE)

..., time range: 1.536s to 1.844s) to demonstrate the effectiveness of our method. We adopt a convolutional neural network method described in Zhou et al...

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...

Improved Nanoscale Image-based Reservoir Characterization using Supervised Machine Learning

Shannon L. Eichmann, Poorna Srinivasan, Kevin Kenga, Mohammed Khan, Fabian Duque, Felix Oyarzabal, James Howard, Shawn Zhang

Unconventional Resources Technology Conference (URTEC)

..., the model is applied to all of the tiled images in the LgFOV image. In the IB method (Fig.1B), gray-scale values from all pixels in the image are used...

Application of Artificial Intelligence Tools for Fault Imaging in an Unconventional Reservoir: A Case Study from the Permian Basin

H. Garcia, L. Plant

Unconventional Resources Technology Conference (URTEC)

... areas. In the current study, we used a pre-trained convolutional neural network. This foundation network can be applied to the data without providing any...

Innovative disorder seismic attribute for reservoir characterization

Qiang Fu, Saleh Al-Dossary

International Meeting for Applied Geoscience and Energy (IMAGE)

... seismic attribute is a convolutional filtering based algorithm designed using an optimization approach. By design, the attribute is insensitive to faults...

Supervised vs unsupervised deep learning for time-lapse seismic repeatability enforcement

Son Phan, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... involving machine learning algorithms for repeatability enforcement. These are based on the convolutional neural network, which have been proven powerful...

Seismic-based paleoenvironmental analysis of the Paleocene carbonate shelf in Ajdabiya Trough, north-central of Libya

Abdeladim M. Asheibi

Bulletin of Canadian Energy Geoscience (CEGA)

..., constrained sparse spike inversion (Debeye and Riel, 1990). All these methods resolve the components of the convolutional model in each seismic trace...

Sub-seafloor reflectivity estimation by upgoing wavefield deconvolution

Hassan Masoomzadeh, Tim Seher, M. A. H. Zuberi

International Meeting for Applied Geoscience and Energy (IMAGE)

... wavefield. Figure 2 aims to explain that the convolutional relationship between successive data terms is represented by two-way propagation...

Optimization of Relative Geological Time Derived From Flow Field A Label Free Approach

Zhun Li

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Using relative geologic time to constrain convolutional neural network-based seismic interpretation and property estimation: Geophysics, 87, IM25–IM35...

Remote Well Site Biostratigraphy and Advances in Automated Fossil Analysis; #41930 (2016)

Gunilla Gard, Iain Prince, Jason A. Crux, J. M. Shin, Bernard Lee

Search and Discovery.com

... Convolutional Neural Network (CNN) is applied to training fossil recognition. Accuracy 4. In each iteration, the training software checks the training quality...

Combined P and S Waves Survey for Hydrocarbon Exploration

Basuki Puspoputro

Indonesian Petroleum Association

... velocity analysis and the convolutional model: IHRDC, Boston. Sheriff, R.E., 1984, Encyclopedic dictionary of exploration geophysics, 2nd edition...

Estimation of TOC and Brittleness Volumes and Correlation with Reservoir Production

Sumit Verma, Tao Zhao, Kurt J. Marfurt, Deepak Devegowda, Dario Grana

Unconventional Resources Technology Conference (URTEC)

... criteria for step-wise multilinear regression for including well logs in the regression model (Hampson et al., 2001). Validation correlation...

2019 AAPG Annual Convention and Exhibition

Search and Discovery.com

Kerogen-Bitumen-Porosity Nexus: Insights from Multi-Basinal Collocated SEM-Optical Light Petrography

Shaina Kelly, Michelle Johnston, Bernard Lee, Richard San Martin

Unconventional Resources Technology Conference (URTEC)

... SEM stub which is then ready for Broad ION Beam milling (Fischione Model 1060 dual beam system) for a minimum of 3 hours to get past most plucks...

Oriented ellipsoidal DBSCAN for clustering faults from deep learning attributes

Samuel Chambers, Jesse Lomask

International Meeting for Applied Geoscience and Energy (IMAGE)

..., and A. Z. Yusifov, 2019, FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network: IEEE Transactions...

Abstract: Harmonic Decomposition of a Vibroseis Sweep Using Gabor Analysis; #90174 (2014)

Christopher B. Harrison, Gary Margrave, Michael Lamoureux, Art Siewert, and Andrew Barrett

Search and Discovery.com

... (t )  ...   n * n (t )  ... n 1 (1) where all  n are small time-independent convolutional (*) filters. Equation (1) provides a base for four unique methods...

Adapting Music Recognition Technology for Tops Picking and Quality Control

Alan Lindsey, Morgan Cox, Aaron Hugen

Unconventional Resources Technology Conference (URTEC)

... stratigraphy due to either faulting or unconformities. This can lead to false positive marker identifications. Convolutional Neural Networks (CNNs) CNNs...

AAPG International Conference and Exhibition; - Abstracts, #91209 (2025).

Search and Discovery.com

Late Devonian Glacially-Influenced Marine Sedimentation in Western Gondwana: The Cumana Formation, Altiplano, Bolivia

Enrique Diaz Martinez, Peter E. Isaacson

CSPG Special Publications

... 1985. Pebble fabric in an ice-rafted diamicton. Journal of Geology, v. 93, p. 577-592. Dowdeswell, J.A. 1988. A model for iceberg sedimentation...

Pushing the envelope of seismic stratigraphic interpretation: a case-study from the Mannville Group using small 3-D surveys

M. Smaili, B.S. Hart

CSPG Bulletin

..., we used model-based inversion (e.g. Veeken and Da Silva, 2004) to convert the 3-D seismic volumes to acoustic impedance2. This approach integrates...

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...

Viable Solutions to Overcome Weaknesses of Deep Learning Applications in Production Forecasting: A Comprehensive Review

Y. Kocoglu, S. Gorell

Unconventional Resources Technology Conference (URTEC)

... process the same time series data in multiple different ways and can be combined with other workflows or networks such as Convolutional Neural Network...

Halokinetic rotating faults, salt intrusions, and seismic pitfalls in the petroleum exploration of divergent margins

Carlos L. Varela, Webster U. Mohriak

AAPG Bulletin

... and development of hydrocarbon reservoirs: A model from the Adelaide geosyncline, South Australia, in P. J. Post, D. L. Olson, K. T. Lyons, S. L. Palmes, P. F...