Parameter Evaluations for Vertical Wells with Hydraulic Fracture Using Well-Testing and Deep Learning Method

2021 ◽  
Author(s):  
Zhiming Chen ◽  
Peng Dong ◽  
Meiling Meng ◽  
Jianan Wang ◽  
Tianyi Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Well Testing ◽  
Vertical Wells ◽  
Parameter Evaluation ◽  
Automatic Interpretation ◽  
Model Training ◽  
Validation Set ◽  
Fractured Well ◽  
Deep Learning Model ◽  
Interpretation Model

Abstract The reliability of well-testing interpretation largely depends on the experience of reservoir engineers, which make the issue of non-unique solution serious and increase its application threshold. Virtually, deep learning assistive techniques are good strategies in well-testing interpretation. Although some work has been done based on automatic interpretation techniques, there is still a lack of an automatic interpretation model with wide applicability and fast interpretation on parameter evaluation of vertically fractured well. To improve this situation and make the well-testing interpretation easier to apply, this paper uses deep learning methods to build an automatic interpretation model of well-testing data for vertically fractured well. The model can automatically identify the corresponding parameters. The results in the validation set show that the median relative error of the curve parameter inversion is less than 10%. In addition, the accuracy of parameter prediction can be improved by increasing the weight of some important parameters in deep learning model training, such as permeability and fracture half-length. Finally, the automatic interpretation model is tested on a field case. The test results prove that the model has high accuracy and interpretation speed.

scholarly journals Dropout, a basic and effective regularization method for a deep learning model: a case study

2021 ◽  
Vol 24 (2) ◽  
pp. 1009
Author(s):  
Brahim Jabir ◽  
Noureddine Falih
Keyword(s):  
Deep Learning ◽  
Weed Species ◽  
It Impacts ◽  
Artificial Neurons ◽  
Pest Detection ◽  
Learning Set ◽  
Validation Set ◽  
Deep Learning Model ◽  
And Training

Deep learning is based on a network of artificial neurons inspired by the human brain. This network is made up of tens or even hundreds of "layers" of neurons. The fields of application of deep learning are indeed multiple; Agriculture is one of those fields in which deep learning is used in various agricultural problems (disease detection, pest detection, and weed identification). A major problem with deep learning is how to create a model that works well, not only on the learning set but also on the validation set. Many approaches used in neural networks are explicitly designed to reduce overfit, possibly at the expense of increasing validation accuracy and training accuracy. In this paper, a basic technique (dropout) is proposed to minimize overfit, we integrated it into a convolutional neural network model to classify weed species and see how it impacts performance, a complementary solution (exponential linear units) are proposed to optimize the obtained results. The results showed that these proposed solutions are practical and highly accurate, enabling us to adopt them in deep learning models.

scholarly journals The contribution of hippocampal subfields to the progression of neurodegeneration

2020 ◽  
Author(s):  
Kichang Kwak ◽  
Marc Niethammer ◽  
Kelly S. Giovanello ◽  
Martin Styner ◽  
Eran Dayan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Molecular Layer ◽  
Total Sample ◽  
Neural Network Architecture ◽  
Hippocampal Subfields ◽  
Validation Set ◽  
Hippocampal Structure ◽  
Deep Learning Model ◽  
Variable Progression

AbstractMild cognitive impairment (MCI) is often considered the precursor of Alzheimer’s disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus, but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression using a total sample of 1157 subjects (349 in the training set, 427 in a validation set and 381 in the testing set). We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry. The proposed deep learning model predicted MCI progression with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI. Our predictive model uncovers pronounced differences in the contribution of hippocampal subfields to the progression of MCI. The results may reflect the sparing of hippocampal structure in individuals with a slower progression of neurodegeneration.

scholarly journals Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanghao Jin ◽  
Yixin Hu ◽  
Yuming Jiao ◽  
Junfang Wen ◽  
Qingzeng Song
Keyword(s):  
Deep Learning ◽  
Local Environment ◽  
Learning Model ◽  
Human Beings ◽  
Training Samples ◽  
Local Probability ◽  
Validation Set ◽  
Deep Learning Model ◽  
New Framework ◽  
Better Than

Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

scholarly journals Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training

2021 ◽  
Vol 11 (21) ◽  
pp. 10377
Author(s):  
Hyeonseong Choi ◽  
Jaehwan Lee
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Training Model ◽  
High Accuracy ◽  
Access Pattern ◽  
Learning Models ◽  
Training Models ◽  
Model Training ◽  
Access Patterns ◽  
Deep Learning Model

To achieve high accuracy when performing deep learning, it is necessary to use a large-scale training model. However, due to the limitations of GPU memory, it is difficult to train large-scale training models within a single GPU. NVIDIA introduced a technology called CUDA Unified Memory with CUDA 6 to overcome the limitations of GPU memory by virtually combining GPU memory and CPU memory. In addition, in CUDA 8, memory advise options are introduced to efficiently utilize CUDA Unified Memory. In this work, we propose a newly optimized scheme based on CUDA Unified Memory to efficiently use GPU memory by applying different memory advise to each data type according to access patterns in deep learning training. We apply CUDA Unified Memory technology to PyTorch to see the performance of large-scale learning models through the expanded GPU memory. We conduct comprehensive experiments on how to efficiently utilize Unified Memory by applying memory advises when performing deep learning. As a result, when the data used for deep learning are divided into three types and a memory advise is applied to the data according to the access pattern, the deep learning execution time is reduced by 9.4% compared to the default Unified Memory.

scholarly journals Detecting orientation of Brain MR scans using deep learning

2021 ◽  
Author(s):  
Chinmay Singhal ◽  
Nihit Gupta ◽  
Anouk Stein ◽  
Quan Zhou ◽  
Leon Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Free Text ◽  
Learning Models ◽  
Human Errors ◽  
Linguistic Barriers ◽  
Model Training ◽  
The Impact ◽  
Deep Learning Model ◽  
Made In

AbstractThere has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

scholarly journals Paddy Rice Imagery Dataset for Panicle Segmentation

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1542
Author(s):  
Hao Wang ◽  
Suxing Lyu ◽  
Yaxin Ren
Keyword(s):  
Deep Learning ◽  
Rice Field ◽  
High Spatial Resolution ◽  
Learning Model ◽  
Accurate Solution ◽  
Training Process ◽  
Field Phenotyping ◽  
Model Training ◽  
Public Datasets ◽  
Deep Learning Model

Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based on high-spatial-resolution images provide a high-throughput and accurate solution of panicle segmentation. Panicle segmentation tasks require costly annotations to train an accurate and robust deep learning model. However, few public datasets are available for rice-panicle phenotyping. We present a semi-supervised deep learning model training process, which greatly assists the annotation and refinement of training datasets. The model learns the panicle features with limited annotations and localizes more positive samples in the datasets, without further interaction. After the dataset refinement, the number of annotations increased by 40.6%. In addition, we trained and tested modern deep learning models to show how the dataset is beneficial to both detection and segmentation tasks. Results of our comparison experiments can inspire others in dataset preparation and model selection.

scholarly journals A classification method for social information of sellers on social network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haoliang Cui ◽  
Shuai Shao ◽  
Shaozhang Niu ◽  
Chengjie Shi ◽  
Lingyu Zhou
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Mobile Phones ◽  
Classification Scheme ◽  
Social Information ◽  
Operating Environment ◽  
Network Applications ◽  
Text Information ◽  
Model Training ◽  
Deep Learning Model

AbstractSocial e-commerce has been a hot topic in recent years, with the number of users increasing year by year and the transaction money exploding. Unlike traditional e-commerce, the main activities of social e-commerce are on social network apps. To classify sellers by the merchandise, this article designs and implements a social network seller classification scheme. We develop an app, which runs on the mobile phones of the sellers and provides the operating environment and automated assistance capabilities of social network applications. The app can collect social information published by the sellers during the assistance process, uploads to the server to perform model training on the data. We collect 38,970 sellers’ information, extract the text information in the picture with the help of OCR, and establish a deep learning model based on BERT to classify the merchandise of sellers. In the final experiment, we achieve an accuracy of more than 90%, which shows that the model can accurately classify sellers on a social network.

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