scholarly journals A Workflow Incorporating an Artificial Neural Network to Predict Subsurface Porosity for CO2 Storage Geological Site Characterization

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 813
Author(s):  
George Koperna ◽  
Hunter Jonsson ◽  
Richie Ness ◽  
Shawna Cyphers ◽  
JohnRyan MacGregor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Capture ◽  
Large Scale ◽  
Co2 Storage ◽  
Site Characterization ◽  
Critical Porosity ◽  
Storage Reservoir ◽  
Artificial Neural ◽  
Porosity Measurements

The large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The trained neural network was applied to 36 oil wells across the Citronelle Field and used to generate synthetic porosities of the storage reservoir and overlying stratigraphy. Ultimately, permeability of the storage reservoir was estimated using a combination of synthetic porosity and an empirically derived relationship between porosity and permeability determined from core.

A new hybrid hydrologic model of XXT and artificial neural network for large-scale daily runoff modeling

2010 ◽  
Author(s):  
Jingwen Xu ◽  
Weidan Liu ◽  
Ziyuan Zheng ◽  
Wanchang Zhang ◽  
Li Ning
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Hydrologic Model ◽  
Runoff Modeling ◽  
Artificial Neural

scholarly journals A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Min Xu ◽  
Pengjiang Qian ◽  
Jiamin Zheng ◽  
Hongwei Ge ◽  
Raymond F. Muzic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Magnetic Resonance ◽  
Large Scale ◽  
Medical Images ◽  
High Tech ◽  
Mr Images ◽  
Specific Target ◽  
Radial Basis ◽  
Artificial Neural

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

Large Scale Covid-19 Detection with Blood Platelets Using Artificial Neural Network

2021 ◽  
pp. 393-399
Author(s):  
Ahmed Tamer ◽  
Andreas Pester
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Blood Platelets ◽  
Artificial Neural

Ontology alignment using artificial neural network for large-scale ontologies

2013 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Warith Eddine Djeddi ◽  
Mohamed Tarek Khadir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Ontology Alignment ◽  
Artificial Neural

Research of Fault Detection System Combining with Fault Tree Analysis with Artificial Neural Network in Large Scale Die-Forging Press

2011 ◽  
Vol 317-319 ◽  
pp. 661-666 ◽  
Author(s):  
Ji Dong Yang ◽  
Yan Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Large Scale ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Artificial Neural ◽  
Forging Press

This paper research on a new fault detection method used in large scale die-forging press, based on the national major science and technology project (2009ZX04005-011). This method combines fault tree analysis (FTA) with artificial neural network (ANN), it need to pick up training samples of ANN from fault tree constructing by analyzing system first. An effective network model can be achieved through using BP algorithm to train the samples based on the tree-layer feed-forward network structure. This network model can output fault reasons according to fault symptoms.

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