Determination the Levels of Thief Zones Based on Machine Learning

2021 ◽  
Author(s):  
Chunhua Lu ◽  
Hanqiao Jiang ◽  
Chengcheng You ◽  
Fulong Wang ◽  
Fei Xu ◽  
...  
Keyword(s):  
Transport Model ◽  
Dropout Rate ◽  
Activation Function ◽  
Breakthrough Curves ◽  
Classification Model ◽  
Automatic Identification ◽  
Tracer Transport ◽  
Practical Applications ◽  
Tracer Testing ◽  
Model Training

Abstract The technique of interwell tracer testing is considered as one of the most effective method to identify the thief zone (TZ) in reservoirs. However, in heavy oil reservoirs, tracer breakthrough curves are mostly parabolic and unimodal, thus resulting in slight differences between curves. It is inefficient and inaccurate to identify different types of curves with traditional methods applied to characterize the levels of TZs. In this paper, convolutional neural network (CNN) is applied to construct a classification model for the automatic identification of the levers of TZs. According to the TZs criteria specified on the field, the analytical tracer transport model was applied to generate 3000 curves as the sample, which can meet the requirements of model training accuracy. In the meantime, One-hot encoding, Xavier initialization, Adam optimizer, and mini-batch normalization were used to construct the model, and the key parameters are optimized to improve the performance of the model. The results show that the appropriate activation function is ReLU and the optimal dropout rate is 0.5. Moreover, the construction of CNN with discrete data points (DDP-CNN) as input contributed to a further improvement of classification accuracy of tracer curves. The accuracy of DDP-CNN in training set is 0.96, which is 14% and 23% higher than random forest (RF) and k-means, respectively. In practical applications, DDP-CNN proves capable to correctly classify 88 of the 100 curves.

scholarly journals On the reliability of analytical models to predict solute transport in a fracture network

2014 ◽  
Vol 18 (6) ◽  
pp. 2359-2374 ◽  
Author(s):  
C. Cherubini ◽  
C. I. Giasi ◽  
N. Pastore
Keyword(s):  
Solute Transport ◽  
Transport Model ◽  
Fractured Rock ◽  
Early Time ◽  
Taylor Dispersion ◽  
Breakthrough Curves ◽  
Nonlinear Flow ◽  
Analytical Models ◽  
Late Time ◽  
Tracer Transport

Abstract. In hydrogeology, the application of reliable tracer transport model approaches is a key issue to derive the hydrodynamic properties of aquifers. Laboratory- and field-scale tracer dispersion breakthrough curves (BTC) in fractured media are notorious for exhibiting early time arrivals and late time tailing that are not captured by the classical advection–dispersion equation (ADE). These "non-Fickian" features are proven to be better explained by a mobile–immobile (MIM) approach. In this conceptualization the fractured rock system is schematized as a continuous medium in which the liquid phase is separated into flowing and stagnant regions. The present study compares the performances and reliabilities of the classical MIM and the explicit network model (ENM), taking expressly into account the network geometry for describing tracer transport behavior in a fractured sample at bench scale. Though ENM shows better fitting results than MIM, the latter remains still valid as it proves to describe the observed curves quite well. The results show that the presence of nonlinear flow plays an important role in the behavior of solute transport. First, the distribution of solute according to different pathways is not constant, but it is related to the flow rate. Second, nonlinear flow influences advection in that it leads to a delay in solute transport respect to the linear flow assumption. However, nonlinear flow is not shown to be related with dispersion. The experimental results show that in the study case the geometrical dispersion dominates the Taylor dispersion. However, the interpretation with the ENM shows a weak transitional regime from geometrical dispersion to Taylor dispersion for high flow rates. Incorporating the description of the flow paths in the analytical modeling has proven to better fit the curves and to give a more robust interpretation of the solute transport.

scholarly journals On the reliability of analytical models to predict solute transport in a fracture network

2013 ◽  
Vol 10 (12) ◽  
pp. 14905-14948
Author(s):  
C. Cherubini ◽  
C. I. Giasi ◽  
N. Pastore
Keyword(s):  
Solute Transport ◽  
Transport Model ◽  
Fractured Rock ◽  
Early Time ◽  
Taylor Dispersion ◽  
Breakthrough Curves ◽  
Nonlinear Flow ◽  
Analytical Models ◽  
Late Time ◽  
Tracer Transport

Abstract. In hydrogeology, the application of reliable tracer transport model approaches is a key issue to derive the hydrodynamic properties of aquifers. Laboratory and field-scale tracer dispersion breakthrough curves (BTC) in fractured media are notorious for exhibiting early time arrivals and late-time tailing that are not captured by the classical advection–dispersion equation (ADE). These "non-Fickian" features are proved to be better explained by a mobile–immobile (MIM) approach. In this conceptualization the fractured rock system is schematized as a continuous medium in which the liquid phase is separated into flowing and stagnant regions. The present study compares the performances and reliabilities of classical Mobile–Immobile Model (MIM) and the Explicit Network Model (ENM) that takes expressly into account the network geometry for describing tracer transport behavior in a fractured sample at bench scale. Though ENM shows better fitting results than MIM, the latter remains still valid as it proves to describe the observed curves quite well. The results show that the presence of nonlinear flow plays an important role in the behaviour of solute transport. Firstly the distribution of solute according to different pathways is not constant but it is related to the flow rate. Secondly nonlinear flow influences advection, in that it leads to a delay in solute transport respect to the linear flow assumption. Whereas nonlinear flow does not show to be related with dispersion. However the interpretation with the ENM model shows a weak transitional regime from geometrical dispersion to Taylor dispersion for high flow rates. The experimental results show that in the study case the geometrical dispersion dominates the Taylor dispersion. Incorporating the description of the flowpaths in the analytical modeling has proved to better fit the curves and to give a more robust interpretation of the solute transport.

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

2019 ◽  
Vol 12 (3) ◽  
pp. 156-161 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Primary Objective ◽  
Experimental Comparison ◽  
Activation Functions ◽  
Practical Applications ◽  
Network Activation ◽  
Non Linear ◽  
Hidden Layer

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.

Fully automated contrast and non-contrast cardiac view detection in echocardiography a multi-centre, multi-vendor study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Gao ◽  
D Stojanovski ◽  
A Parker ◽  
P Marques ◽  
S Heitner ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Classification Model ◽  
Validation Dataset ◽  
Funding Source ◽  
Private Company ◽  
Validation Data ◽  
Independent Test ◽  
Model Training ◽  
Confusion Matrices

Abstract Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of >99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

scholarly journals Automatic Detection Technology of Sonar Image Target Based on the Three-Dimensional Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wanzeng Kong ◽  
Jinshuai Yu ◽  
Ying Cheng ◽  
Weihua Cong ◽  
Huanhuan Xue
Keyword(s):  
Data Storage ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Automatic Detection ◽  
Classification Model ◽  
Image Detection ◽  
Practical Applications ◽  
Sonar Image ◽  
Detection Technology

With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

2021 ◽  
pp. 2150030
Author(s):  
Jing Jin ◽  
Hua Fang ◽  
Ian Daly ◽  
Ruocheng Xiao ◽  
Yangyang Miao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Minimum Covariance Determinant ◽  
Fisher Score ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Highly Sensitive ◽  
Feature Optimization ◽  
The Common ◽  
Model Training

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

scholarly journals ORFhunteR: an accurate approach for the automatic identification and annotation of open reading frames in human mRNA molecules

2021 ◽  
Author(s):  
Vasily V. Grinev ◽  
Mikalai M. Yatskou ◽  
Victor V. Skakun ◽  
Maryna K. Chepeleva ◽  
Petr V. Nazarov
Keyword(s):  
Single Molecule ◽  
Web Application ◽  
R Package ◽  
Nucleotide Sequences ◽  
Open Reading Frames ◽  
Classification Model ◽  
Automatic Identification ◽  
Large Set ◽  
Link Type ◽  
Reading Frames

AbstractMotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates.ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules.Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

The Fingerprint of Indian Ocean Rain and River Water in Salinity and Circulation

2021 ◽  
Author(s):  
Vinu Valsala
Keyword(s):  
Indian Ocean ◽  
General Circulation ◽  
Climate Models ◽  
Transport Model ◽  
Tropical Indian Ocean ◽  
Quasi Equilibrium ◽  
Tracer Transport ◽  
Tropical Ocean ◽  
Cumulative Impact ◽  
Circulation Anomalies

Abstract Per unit area of the tropical Indian Ocean receives the world’s largest tropical ocean rain and river runoff (RRW). The 3-dimensional spreading of RRW entering the tropical Indian Ocean and associated salinity and circulation anomalies are explored for 60 years using ocean reanalysis data tailored to a tracer transport model. Over 60 years, the cumulative impact of RRW entering the tropical Indian Ocean is to freshen the Indian Ocean basin as large as 2-0.1 p.s.u from the surface to 500m. The RRW has propagated to a vast extent of the Atlantic and Pacific Oceans via general circulation pathways. A quasi-equilibrium model of accumulation of RRW over the tropical Indian Ocean suggests that it induces clockwise geostrophic currents from the Bay of Bengal to the Arabian Sea over 0-500m depths, a net inter-basin transport tendency of 0.8±0.14 Sv year-1. The study implies that coupled climate models with apparent precipitation biases may miscalculate such salinity and circulation anomalies due to RRW and aggravating biases in simulated climate dynamics.

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