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The AAPG/Datapages Combined Publications Database

Showing 2,441 Results. Searched 200,616 documents.

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A Novel System for Determining Flaring Efficiency Utilizing Optical Methods and Artificial Intelligence

Ángel E. Esparza, Joe Etheridge

Unconventional Resources Technology Conference (URTEC)

... to implement multiple layers within neural networks and conduct operations in isolation of user’s active participation. Convolutional Neural Networks (CNN...

2023

Accurate seismic data interpolation based on multiband intelligent training

Xueyi Sun, Benfeng Wang, Tongtong Mo

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Nivlet, 2020, Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks: Geophysics...

2023

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)

... of DL models ranging from classical ML-based approaches to autoencoder based neural networks to the more powerful Transformer-based approaches...

2024

Distribution of Well Performances in Shale Reservoirs and Their Predictions Using the Concept of Shale Capacity; #41139 (2013)

Ahmed Ouenes

Search and Discovery.com

... intelligence route where neural networks are used to correlate the various seismic attributes with rock properties, such as the ones needed to compute the shale...

2013

Assessment of a Marcellus Shale Prospect Using Seismic, Microseismic, and Completions Data

Scott Singleton, Mark Suda

GCAGS Transactions

...) at a time. Neural networks are built from simple units, sometimes called ‘neurons’ by analogy. These units are interlinked by a set of weighted connections...

2012

Tectonic and Petrophysical Investigation of Williston Basin Strata around the Weyburn CO2 Sequestration Reservoir

Sandor Sule, Zoltan Hajnal, and Bhaskar Pandit

Saskatchewan Geological Society

... probabilistic neural networks. The impedance values derived from the seismic amplitudes, and those computed from the product of the acoustic and density logs...

2006

Using Analytics to Quantify the Value of Seismic Data for Mapping Eagle Ford Sweetspots

Murray Roth, Ted Royer, Ross Peebles, Michael Roth

Unconventional Resources Technology Conference (URTEC)

... of completions – with bigger not always being better. Neural networks can provide a non-linear option for modeling these reservoirs, but do not provide...

2013

Machine learning-based workflow for identifying fractures and baffles from Formation Micro Imager (FMI) log: A practical application in Illinois Basin Decatur Project (IBDP)

Mohammad Faiq Adenan, Ebrahim Fathi, Tim Carr, Brian Panetta

International Meeting for Applied Geoscience and Energy (IMAGE)

... formations are known to contain extensive natural fracture networks, which are challenging and timeconsuming to quantify manually. The accuracy...

2023

Approaches to Defining Reservoir Physical Properties from 3-D Seismic Attributes with Limited Well Control: an Example from the Jurassic Smackover Formation, Alabama

Bruce S. Hart, Robert S. Balch

West Texas Geological Society

..., 1996; Russell et al., 1997). Multiple regression, geostatistics, neural networks and other approaches are being explored to help correlate log...

1998

Establishing Novel Views on Reservoir Compartmentalization Utilizing Systematic Data-driven Seismic Processing Frameworks

Riaz Alai, M Afiq B Mokhtar, Jun Wang, Yonghe Guo, Ecep Suryana

Indonesian Petroleum Association

... of extracted fault images to full migrated cubes, steers and converges towards identification of detailed fault networks confirmed by attribute analyses...

2019

Characterization and Modeling of Tight Fractured Carbonate Reservoir of Najmah-Sargelu Formation, Kuwait, #41059 (2012)

Prabir Kumar Nath, Sunil Kumar Singh, Reyad Abu-Taleb, Raghav Prasad, Badruzzaman Khan, Sara Bader

Search and Discovery.com

... and Neural Network and co-krigged with ant-track and coherency volume. The major fracture sets were modeled as a combined DFN (Discrete Fracture Network...

2012

An Analytic Approach to Sweetspot Mapping in the Eagle Ford Unconventional Play; #80406 (2014)

Murray Roth, Michael Roth, Ted Royer

Search and Discovery.com

... relationships) of reservoir thickness and size of completions – with bigger not always being better. Neural networks can provide a non-linear option...

2014

From Nanopore to Seismic Scale: the Role of Organic Matter in Vaca Muerta Shale Oil Productivity and Sweet Spot Prediction in Rincón de Aranda Block, #42011 (2017).

Denis Marchal, A. Perez Mazas, C. Naides, F. Sattler, J. Erlicher, G. Kohler, E. Nigro, S. Sommacal, A. Fogden

Search and Discovery.com

... pore networks at microto nanoscale and quantify relative petrophysical properties using an integrated workflow of CT/FIBSEM 3D imaging of core...

2017

A New Method of Making the Thickness Map of the Shallow Sand Body Constrained by Seismic Attribute, #42092 (2017).

Ming Jun, Zhou Xuefeng, Liu Xuetong, Pan Yong, Li Wenbin

Search and Discovery.com

... the relationship between thickness and seismic attributes by linear fitting or artificial neural networks, and calculate the thickness with seismic attributes...

2017

Digital Innovation in Subsea Integrity Management

Ricky Thethi, Dharmik Vadel, Mark Haning, Elizabeth Tellier

Australian Petroleum Production & Exploration Association (APPEA) Journal

... to carefully selected feature (input) variables, which would include vessel motions and, if available, riser motions and curvatures. Recursive neural networks...

2020

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)

...), avoiding the gradient explosion or vanishing in traditional recurrent neural networks (RNN), which is suitable for long sequence learning...

2020

Characterization of a deeply buried paleokarst terrain in the Loppa High using core data and multiattribute seismic facies classification

Jhosnella Sayago, Matteo Di Lucia, Maria Mutti, Axum Cotti, Andrea Sitta, Kjetil Broberg, Artur Przybylo, Raffaele Buonaguro, Olesya Zimina

AAPG Bulletin

.... A. Naini, 2007, Using multiattribute neural networks classification for seismic carbonate facies mapping: A workflow example from mid-Cretaceous Persian...

2012

High-Resolution Three-Dimensional Water Saturation Prediction - A Case Study from Offshore Nile Delta; #42206 (2018)

Islam A. Mohamed, Ahmed Hosny, Abdulrahman Ali Mohamed

Search and Discovery.com

... was computed as well as the Lamé parameter volumes of lambda-rho (λρ) and mu-rho (μρ). Implementing probabilistic neural network, the inversion results were...

2018

Analytical and Machine Learning Based Modifications to Unconventional Reservoir Simulation Models to Capture Near-Fracture Transient Effects

Hector E. Barrios-Molano, Alvaro Rey, Shihao Wang

Unconventional Resources Technology Conference (URTEC)

... tested these methods using models with different complexity and compared them with refined models. The third method uses two artificial neural networks...

2024

The Integrated Use of Spectral Decomposition, AVO Analysis, Seismic Attributes, Principal Component, Supervised Neural Facies Classification, and Waveform Calibration for Reservoir Delineation

Search and Discovery.com

...The Integrated Use of Spectral Decomposition, AVO Analysis, Seismic Attributes, Principal Component, Supervised Neural Facies Classification...

2013

Abstract: Automatic Event Picking Using a Probabilistic Neural Network

Troy Thompson

Petroleum Exploration Society of Australia (PESA)

...Abstract: Automatic Event Picking Using a Probabilistic Neural Network Troy Thompson I ABSTRACTS OF TALKS Automatic Event Picking Using...

2003

Abstract: Prediction of Porosity and Permeability of Heterogeneous Shaly Gas Sand Reservoirs Using Neural Network Algorithm

G. M. Hamada, M. A. Elshafei

Geological Society of Malaysia (GSM)

...Abstract: Prediction of Porosity and Permeability of Heterogeneous Shaly Gas Sand Reservoirs Using Neural Network Algorithm G. M. Hamada, M...

2017

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