Modeling the effects of additives on rheological properties of fresh self-consolidating cement paste using artificial neural network

2011 ◽  
Vol 8 (3) ◽  
pp. 279-292 ◽  
Author(s):  
Alireze Mohebbi ◽  
Mohammad Shekarchi ◽  
Mehrdad Mahoutian ◽  
Shima Mohebbi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rheological Properties ◽  
Cement Paste ◽  
Artificial Neural

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.

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.

Modelling the Yield Stress of Fly-Ash Added Superplasticized Cement Paste at Different Temperatures Using Artificial Neural Network

2022 ◽  
Vol 1048 ◽  
pp. 366-375
Author(s):  
Pavan Chandrasekar ◽  
Anjala Nourin ◽  
Addepalli Sri Naga Bhushana Aravind Gupta ◽  
Bavineni Venkata Jyoshna ◽  
Dhanya Sathyan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fly Ash ◽  
Yield Stress ◽  
Cement Paste ◽  
Test Temperature ◽  
Fresh Concrete ◽  
Rheological Parameters ◽  
Different Temperatures ◽  
Artificial Neural

Abstract: Rheology is the science that concerns the flow of liquids, and the distortion of solids under an applied force. The study of the rheology of concrete determines the properties of fresh concrete. The rheological parameters are affected by temperature, stress conditions and several other factors. The main intention of this research is to model the rheological parameters of the fly ash incorporated cement with various types of superplasticizers exposed under different temperatures using an Artificial Neural Network. Test data were generated by performing rheological tests on cement paste at three distinct temperatures (15, 27, 35°C). Mixes were prepared using OPC, fly ash (15, 25, 35%) and superplasticizers of four different families. By conducting experiments, 252 data have been generated by modifying the combination of fly-ash, superplasticizer, and test temperature. Among the 252 data, 80% has been utilized for training and 20% is utilized for predicting the model’s accuracy. The input layer of the model consists of test temperature, the amount of fly ash replaced, cement and water content, and four different groups of superplasticizers. The cement paste’s yield stress was the output parameter of the model. The model generated data has been compared with the experimentally generated data to determine the accuracy of the model.Keywords: Rheology, Fly Ash, Superplasticizer, Temperature, ANN

scholarly journals Rheology modelling of cement paste with manufactured sand and silica fume: Comparing suspension models with artificial neural network predictions

2022 ◽  
Vol 317 ◽  
pp. 126114
Author(s):  
Elisabeth Leite Skare ◽  
Shohreh Sheiati ◽  
Rolands Cepuritis ◽  
Ernst Mørtsell ◽  
Sverre Smeplass ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silica Fume ◽  
Cement Paste ◽  
Manufactured Sand ◽  
Artificial Neural ◽  
Suspension Models
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