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.

Parison Dimension Prediction in Extrusion Blow Molding Using Neural Network Approach: A New Strategy

2007 ◽  
Author(s):  
Han-Xiong Huang ◽  
Dong Li
Keyword(s):  
Neural Network ◽  
Thickness Distribution ◽  
Ann Model ◽  
Neural Network Approach ◽  
Die Geometry ◽  
Highly Nonlinear ◽  
Molded Parts ◽  
Designed Experiment ◽  
Artificial Neural Network Ann ◽  
Extrusion Blow Molding

As the plastics extrusion blow molded parts are getting more and more complex, it is necessary to optimize the parison dimension distribution. Predicting the parison dimension distribution is useful to optimize the thickness distribution and property of the final part. The dependency between parison dimensions and materials characteristics, processing conditions, and die geometry is a highly nonlinear and fully coupled one. In this work, diameter and thickness swells of the high-density polyethylene parison extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the artificial neural network (ANN) model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given range.

Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

1998 ◽  
Vol 1633 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Hossam Eldin Ali ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Network ◽  
Numerical Approach ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Ann Model ◽  
Neural Network Approach ◽  
Liquefaction Potential ◽  
Network Training ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

Initial assessment of bridge backwater using an artificial neural network approach

2008 ◽  
Vol 35 (5) ◽  
pp. 500-510 ◽  
Author(s):  
Murat Cobaner ◽  
Galip Seckin ◽  
Ozgur Kisi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Data ◽  
Laboratory Data ◽  
Initial Assessment ◽  
United States Geological Survey ◽  
Ann Model ◽  
Neural Network Approach ◽  
Ann Models ◽  
Artificial Neural

The assessment of backwater resulting from extra energy losses on flood flows caused by bridge constrictions is of vital interest in hydraulic engineering due to its importance in the design of waterways and management of flooding. Although many detailed methods for estimating bridge backwater have been developed, an initial estimate of the magnitude of bridge backwater using a practical model, such as the multiple linear regression (MLR) technique, has a crucial importance for rapid evaluation of flood damages upstream of the bridge structure. In the current study, first, two artificial neural network (ANN) models using the same amount of input data as that of an MLR approach were developed, and then the ability of these ANN models versus the MLR models was investigated for the initial assessment of bridge backwater, both models having been based on the comprehensive laboratory data of the Hydraulic Research Wallingford in UK. The comparison of the results by the MLR and the ANN approaches revealed that the ANN model gave better predictions than those of the MLR model when applied to these laboratory data. United States Geological Survey (USGS) field data were also used for the validation and comparison of these methods. The results showed that ANN approaches yielded more accurate results than those of the MLR models when applied to these field data including actual flood profiles through many bridges.

scholarly journals A data-driven predictive model of the grinding wheel wear using the neural network approach

2017 ◽  
Vol 4 (17) ◽  
pp. 69-82 ◽  
Author(s):  
Pawel LEZANSKI
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Model Building ◽  
Remaining Useful Life ◽  
Grinding Wheel ◽  
Ann Model ◽  
Wheel Wear ◽  
Neural Network Approach ◽  
Remaining Service Life ◽  
Artificial Neural Network Ann

Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. Thus, to model the state of tool wear and next to predict its remaining useful life (RUL) significantly increases the sustainability of manufacturing processes. there are many approaches, methods and theories applied to predictive model building. the proposed paper investigates an artificial neural network (ANN) model to predict the wear propagation process of grinding wheel and to estimate the RUL of the wheel when the extrapolated data reaches a predefined final failure value. The model building framework is based on data collected during external cylindrical plunge grinding. Firstly, usefulness of selected features of the measured process variables to be symptoms of grinding wheel state is experimentally verified. Next, issues related to development of an effective MLP model and its use in prediction of the grinding wheel RUL is discussed.

Real-time drilling optimisation – driving drilling excellence

2019 ◽  
Vol 59 (1) ◽  
pp. 196
Author(s):  
Abhijit Barhate ◽  
Piyush Patel ◽  
Egil Abrahamsen
Keyword(s):  
Real Time ◽  
Traditional Approach ◽  
Time Data ◽  
Wellbore Instability ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Automatic Sequences ◽  
Look Ahead ◽  
Real Time Data ◽  
Tightly Coupled

Here we explore a real-time solution that anticipates drilling events and avoid delays caused by such issues as poor hole cleaning, higher torque and drag, swab and surge, stuck pipe, lost circulation, formation damage and wellbore instability. This is important if drilling optimisation is a major technical and corporate goal or if you wish to go beyond the traditional approach of collecting real-time data only to monitor operations. The presented state-of-the-art ‘real-time drilling optimisation’ solution creates a dynamic, real-time picture of the entire wellbore and key drilling variables and parameters using advanced, tightly coupled thermodynamic, hydraulic and mechanical drilling models and trend analysis applications to anticipate potential drilling issues in real-time while drilling. The solution can also be used to perform scenario based ‘what-if’ analysis and ‘look-ahead’ simulations of drilling operation with the purpose to analyse outcome of alternative operating scenarios. This is an effective solution that simplifies real-time data analysis by using trends and deviations between modelled and actual data to predict changing wellbore conditions and developing a digital twin of a wellbore. This paper includes robotic drilling automation, aimed at reducing invisible loss time, enhancing drilling efficiency and safety by applying operational safeguards to the drilling control system, providing automatic safety mechanisms and enabling automatic sequences. This paper highlights technical cases demonstrating how this analytic solution not only auto-detects symptoms (which can lead to drilling events or hazards, invisible loss time and non-productive time) but also optimises drilling performance through simulation well ahead of time, thus driving drilling efficiency.

Assessment of Self-Heating Susceptibility of Indian Coal Seams – A Neural Network Approach

2014 ◽  
Vol 59 (4) ◽  
pp. 1061-1076 ◽  
Author(s):  
D.C. Panigrahi ◽  
S.K. Ray
Keyword(s):  
Neural Network ◽  
Spontaneous Combustion ◽  
Potential Method ◽  
Oxidation Potential ◽  
Wet Oxidation ◽  
Experimental Conditions ◽  
Neural Network Approach ◽  
Indian Coal ◽  
Petrographic Analyses ◽  
Artificial Neural Network Ann

Abstract The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method. All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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