scholarly journals Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1880 ◽  
Author(s):  
Ahmed Gowida ◽  
Salaheldin Elkatatny ◽  
Emad Ramadan ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rheological Properties ◽  
Calcium Chloride ◽  
Drilling Fluid ◽  
Flow Behavior ◽  
Percentage Error ◽  
Flow Behavior Index ◽  
Hole Cleaning ◽  
Artificial Neural

Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

scholarly journals Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

scholarly journals A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Ahmed Al-AbdulJabbar ◽  
Khaled Abdelgawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mutual Effect ◽  
Percentage Error ◽  
Drilling Process ◽  
Ann Model ◽  
Data Set ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

scholarly journals Prediction of Density and Speed of Sound of Binary Ionic Liquid and Ketone Mixtures Using Artificial Neural Network

2019 ◽  
Vol 120 ◽  
pp. 01003
Author(s):  
Redden Rose Rivera ◽  
Allan Soriano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
Speed Of Sound ◽  
Negative Impact ◽  
Green Energy ◽  
Percentage Error ◽  
Average Percentage ◽  
Artificial Neural

The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.

Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Norhisham Bakhary ◽  
Khairulzan Yahya ◽  
Chin Nam Ng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Demand ◽  
Percentage Error ◽  
Potential Applications ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

scholarly journals Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks

2019 ◽  
Vol 11 (18) ◽  
pp. 5008 ◽  
Author(s):  
Elkatatny
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rheological Properties ◽  
Network Models ◽  
Empirical Correlations ◽  
Neural Network Models ◽  
Water Based ◽  
Artificial Neural ◽  
Marsh Funnel ◽  
Artificial Neural Network Models

The rheological properties of drilling fluids are the key parameter for optimizing drilling operation and reducing total drilling cost by avoiding common problems such as hole cleaning, pipe sticking, loss of circulation, and well control. The conventional method of measuring the rheological properties are time-consuming and require a high effort for equipment cleaning, so they are only measured twice a day. There is a need to develop an automated system to measure the rheological properties in real-time based on the frequent measurements of mud density, Marsh funnel time, and solid percent. The main objective of this paper is to apply a modified self-adaptive differential evolution technique to determine the optimum combination of an artificial neural network’s variables to precisely predict the rheological properties of water-based drill-in fluid using the frequent measuring of mud density, Marsh funnel time, and solid percent. The second objective is whitening the black box of an artificial neural network by developing five new empirical correlations to determine the rheological properties without the need for the artificial neural network models. Actual field measurements (900 data points) were used to train, test, and validate the artificial neural network models and the developed empirical correlations. The optimization process illustrated that the best training function was Bayesian regularization backpropagation (trainbr), and the best transferring function was Elliot symmetric sigmoid (elliotsig). The optimum number of neurons was 30 for the plastic viscosity and the flow consistency index, while it was 29 for apparent viscosity, yield point, and the flow behavior index. The developed artificial neural network models and empirical correlations predicted the rheological properties with high accuracy. The correlation coefficient (R) was more than 90%, and the average absolute percentage error was less than 8.6%. The new technique for rheological properties estimation is an example of the new development which will help the new generation to discover and extract oil and gas with less cost and with safer operations.

Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions

2020 ◽  
Vol 10 (2) ◽  
pp. 154-162
Author(s):  
Engin Özdemir ◽  
Didem Eren Sarici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Calorific Value ◽  
Volatile Matter ◽  
Ash Content ◽  
Percentage Error ◽  
Multiple Regression Models ◽  
Artificial Neural

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values. Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor. Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined. Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90). Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

2014 ◽  
Vol 21 (3) ◽  
pp. 411-420 ◽  
Author(s):  
Temel Varol ◽  
Aykut Canakci ◽  
Sukru Ozsahin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Milling Time ◽  
Planetary Mill ◽  
Reinforcement Ratio ◽  
Percentage Error ◽  
Composite Powders ◽  
Neural Network Approach ◽  
Artificial Neural

AbstractIn this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.

Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network

2014 ◽  
Vol 35 (12) ◽  
pp. 1793-1800 ◽  
Author(s):  
Meisam Mirarab Razi ◽  
Vassilios C. Kelessidis ◽  
Roberto Maglione ◽  
Majid Ghiass ◽  
Mohammad Ali Ghayyem
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Artificial Neural Network ◽  
Numerical Modeling ◽  
Xanthan Gum ◽  
Flow Behavior ◽  
Artificial Neural
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