UCS Neural Network Model for Real Time Sand Prediction

2010 ◽  
Vol 2 ◽  
pp. 1-13 ◽  
Author(s):  
Gbenga Folorunso Oluyemi ◽  
Babs Mufutau Oyeneyin ◽  
Chris Macleod
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Gathering ◽  
Time Data ◽  
Sand Control ◽  
Exploration And Production ◽  
Measurement While Drilling ◽  
Textural Data ◽  
Potential Evaluation ◽  
Thick Overburden

Exploration and production activities have moved into more challenging deep-water and subsea environments. Many of the clastic reservoirs in these environments are characterized by thick overburden, HP-HT and largely unconsolidated formations with challenging sand management issues. For effective overall field/reservoir management, it is crucial to know if and when sand would fail and be ultimately produced. Field-life sanding potential evaluation and analysis, which seeks to evaluate the sanding potential of reservoir formations during the appraisal stage and all through the development to the abandonment stage, is therefore necessary so that important reservoir/field management decisions regarding sand control deployment can be made. Recent work has identified Unconfined Compressive Strength (UCS) as a key parameter required for the evaluation and analysis of sanding potential of any reservoir formation. There is therefore the need to be able to predict this important sanding potential parameter accurately and in real time to reduce the level of uncertainties usually associated with sanding potential evaluation and analysis. In this work, neural network coded in C++ was trained with log-derived petrophysical, geomechanical and textural data to develop a stand-alone model for predicting UCS. Real-time functionality of this model is guaranteed by real time data gathering via logging while drilling (LWD) and other measurement while drilling (MWD) tools. The choice of neural network over and above other methods and techniques which have been widely used in the industry was informed by its ability to better resolve the widely known complex relationship between petrophysical, textural and geomechanical strength parameters.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

Leveraging on the Digital Twin for improving retail store daily operations management

2019 ◽  
Author(s):  
Yasmina Maizi ◽  
Ygal Bendavid
Keyword(s):  
Real Time ◽  
Operations Management ◽  
Impact Analysis ◽  
Manufacturing Sector ◽  
Research Work ◽  
Data Gathering ◽  
Time Data ◽  
Fast Development ◽  
The Face ◽  
The Impact

With the fast development of IoT technologies and the potential of real-time data gathering, allowing decision makers to take advantage of real-time visibility on their processes, the rise of Digital Twins (DT) has attracted several research interests. DT are among the highest technological trends for the near future and their evolution is expected to transform the face of several industries and applications and opens the door to a huge number of possibilities. However, DT concept application remains at a cradle stage and it is mainly restricted to the manufacturing sector. In fact, its true potential will be revealed in many other sectors. In this research paper, we aim to propose a DT prototype for instore daily operations management and test its impact on daily operations management performances. More specifically, for this specific research work, we focus the impact analysis of DT in the fitting rooms’ area.

Test and Analysis of Exhaust Pollutants from Automobiles Based on GPS Technology

2010 ◽  
Vol 40-41 ◽  
pp. 675-681
Author(s):  
Ming Li Xian ◽  
Qing Huang Yong
Keyword(s):  
Real Time ◽  
Data Gathering ◽  
Operating Conditions ◽  
Vehicle Emission ◽  
Testing System ◽  
Time Data ◽  
Gps Technology ◽  
Urban Road ◽  
Real Time Data ◽  
Object Of Study

Taking the actual running vehicles on the urban roads of Ningpo City as the object of study, by using the brand-new on-vehicle automobile exhaust real-time testing system, and through actual testing by tracking the running vehicles and real-time data gathering, The paper analyzed urban road operating conditions, the vehicle emission situation on the actual roads, obtained the relations between the operating conditions, the speed and emissions and the law by which the automobile operating conditions affect the automobile exhausts.

A hybrid optimization-based recurrent neural network for real-time data prediction

2013 ◽  
Vol 120 ◽  
pp. 547-559 ◽  
Author(s):  
Xiaoxia Wang ◽  
Liangyu Ma ◽  
Bingshu Wang ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Hybrid Optimization ◽  
Time Data ◽  
Data Prediction ◽  
Real Time Data

scholarly journals Real-time data gathering in sensor networks

2010 ◽  
Vol 158 (5) ◽  
pp. 543-550 ◽  
Author(s):  
Yoram Revah ◽  
Michael Segal ◽  
Liron Yedidsion
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Gathering ◽  
Time Data ◽  
Real Time Data

scholarly journals Traffic Sign Classification Using Convolutional Neural Network

2021 ◽  
pp. 01-10
Author(s):  
Pranav Kale ◽  
Mayuresh Panchpor ◽  
Saloni Dingore ◽  
Saloni Gaikwad ◽  
Prof. Dr. Laxmi Bewoor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Driving ◽  
Time Data ◽  
Traffic Sign ◽  
Real World Data ◽  
Data Set ◽  
Proposed Model ◽  
Validation Set

In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]

Multi-Parameter Prediction for Steam Turbine Based on Real-Time Data Using Deep Learning Approaches

2021 ◽  
Author(s):  
Lei Sun ◽  
Tianyuan Liu ◽  
Yonghui Xie ◽  
Xinlei Xia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Plant ◽  
Steam Turbine ◽  
Real Time ◽  
Learning Approaches ◽  
Time Data ◽  
Time Parameters ◽  
Process Prediction ◽  
Parameter Prediction

Abstract Accurate and real-time parameters forecasting is of great importance to the turbine control and predictive maintenance which can help the improvement of power system. In this study, deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter prediction are proposed, and are applied to predict real-time parameters of steam turbine based on data from a power plant. Firstly, the prediction results of RNN and CNN models are compared by the overall performance. The two models show good performance on forecasting of six state parameters while RNN performs better. Moreover, the detailed performance on a certain day show that the relative error of two models are both less than 2%. Finally, the influence of model designs including loss function, training size and input time-steps on the performance of RNN model are also explored. The effects of the above parameters on the prediction performance, training and prediction time of the models are studied. The results can provide a reference for model deployment in the power plant. It is convinced that the proposed method has a high potential for dynamic process prediction in actual industrial scenarios through the above research.

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