Shear Stress and Filtration Loss Properties Assessment of Nano-Silica Water-Based Drilling Fluid using Machine Learning Approaches

2021 ◽  
pp. 1-33
Author(s):  
Yee Cai Ning ◽  
Syahrir Ridha ◽  
Suhaib Umer Ilyas ◽  
Shwetank Krishna ◽  
Muslim Abdurrahman
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Shear Stress ◽  
Drilling Fluid ◽  
Least Square ◽  
Support Vector ◽  
Learning Approaches ◽  
Filtration Loss ◽  
Water Based ◽  
Filtration Properties

Abstract A complete overview of the rheology and filtration properties of drilling fluids are essential to ensure an efficient transport process with minimized fluid loss. Silica nanoparticle is an excellent additive for rheology and filtration properties enhancement. Existing correlations are not available for nano-SiO2-water-based drilling fluid that can extensively quantify the rheology or filtration loss of nanofluids. Thus, two data-driven machine learning approaches are proposed for prediction, i.e., artificial-neural-network (ANN) and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimized using the Levenberg-Marquardt learning algorithm. The parameters for the LSSVM are optimized using Couple Simulated Annealing (CSA). The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2(coefficient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.

Prediction of the Hardness of Cu-Ti-Co Alloy Using Machine Learning Techniques

2018 ◽  
Vol 777 ◽  
pp. 372-376 ◽  
Author(s):  
Shan Feng Fang
Keyword(s):  
Machine Learning ◽  
Copper Alloys ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Forecasting Accuracy ◽  
Learning Techniques ◽  
Vector Machines ◽  
Forecasting Performance

Diverse machine learning approaches were employed to build regression models for predicting mechanical property of Cu-Ti-Co alloy. The forecasting performance of the least-square support vector machines (LSSVM) model has been compared with other artificial intelligence methods such as GRNN, RBF-PLS and RBFNN. The models were developed and validated utilizing a cross-validation (CV) procedure to improve the forecasting accuracy and generalization ability. The result demonstrates that the generalization performance of the new LSSVM is slightly better or superior to those acquired using GRNN, RBF-PLS and RBFNN. In future, it would be expected that the relatively new model based on machine learning is used as an especially helpful implement to accelerate materials design of copper alloys.

scholarly journals A Zwitterionic Copolymer as Rheology Modifier and Fluid Loss Agents for Water-Based Drilling Fluids

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3120
Author(s):  
Xianfeng Tan ◽  
Longchen Duan ◽  
Weichao Han ◽  
Ying Li ◽  
Mingyi Guo
Keyword(s):  
Zeta Potential ◽  
Negative Impact ◽  
Drilling Fluid ◽  
Fluid Loss ◽  
Drilling Fluids ◽  
High Concentration ◽  
Free Radical Copolymerization ◽  
Filtration Loss ◽  
Water Based ◽  
Filtration Properties

To overcome the negative impact on the rheological and filtration loss properties of drilling fluids caused by elevated temperature and salts contamination, which are common in ultradeep or geothermal drilling operations, it is imperative to develop highly efficient additives used in the water-based drilling fluid. In this study, a zwitterionic copolymer P (AM/DMC/AMPS/DMAM, ADAD) was synthesized by using acrylamide (AM), cationic monomer methacrylatoethyl trimethyl ammonium chloride (DMC), anionic monomer 2-acrylamide-2-methyl propane sulfonic acid (AMPS), and N,N-dimethylacrylamide (DMAM) through free radical copolymerization. The copolymer was characterized by 1H Nuclear Magnetic Resonance (NMR), Fourier transform infrared spectroscopy (FTIR), elemental analysis, thermogravimetric analysis (TGA), and zeta potential. The rheological behavior, filtration properties, and the performance exposure to salt or calcium contamination in water-based drilling fluid were investigated. The bentonite/polymer suspension showed improved rheological and filtration properties even after aging at 160 °C or a high concentration of salt and calcium. The filtration loss can be greatly reduced by more than 50% (from 18 mL to 7 mL) by the inclusion of 2.0 wt% copolymer, while a slight increase in the filtrate loss was observed even when exposed to electrolyte contamination. Particle size distribution and zeta potential further validate the idea that zwitterionic copolymer can greatly improve the stability of base fluid suspension through positive group enhanced anchoring on the clay surface and repulsion force between negative particles. Moreover, this study can be directed towards the design and application of zwitterionic copolymer in a water-based drilling fluid.

scholarly journals Effect of silica nano particles on the rheological and HTHP filtration properties of environment friendly additive in water-based drilling fluid

2021 ◽  
Author(s):  
Ved Prakash ◽  
Neetu Sharma ◽  
Munmun Bhattacharya
Keyword(s):  
Drilling Fluid ◽  
Cost Effective ◽  
Vital Role ◽  
Nano Particles ◽  
Rheological Parameters ◽  
Drilling Fluids ◽  
Filtration Loss ◽  
Water Based ◽  
Drilling Operations ◽  
Filtration Properties

AbstractRheological and filtration properties of drilling fluid contribute a vital role in successful drilling operations. Rheological parameters such as apparent viscosity (AV), plastic viscosity (PV), yield point (YP) and gel strength of drilling fluids are very essential for hydraulic calculations and lifting of drill cuttings during the drilling operation. Control of filtration loss volume is also very important for cost effective and successful drilling operations. Therefore, the main goal of this research is to improve the rheological and filtration properties of Grewia Optiva fibre powder (GOFP) by using 30–50 nm size of silica nano particles (SNP) in water-based drilling fluid. The experimental outcomes revealed that after hot rolling of mud samples at 100 °C for 16 h, the low pressure-low temperature (LPLT) and high pressure-high temperature (HPHT) filtration loss of GOFP additives was improved, after the addition of SNP on it. The mixture of 5% GOFP + 4% SNP has reduced the LPLT and HPHT filtration loss of drilling fluid by 74.03 and 78.12%, respectively, as compared to base mud. Thus, it was concluded that after the addition of 0.4% SNP, the LPLT and HPHT filtration control ability of GOFP additive in WBM were increased by 17.6 and 15%, respectively. The rheological parameters such as AV, PV, YP and gelation of drilling fluids were also improved by the addition of GOFP + SNP mixture in the base mud. Therefore, the implementation of GOFP + SNP mixture in water-based mud showed auspicious results which reaffirm the feasibility of using them in the successful drilling operations.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

scholarly journals Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1694
Author(s):  
Mathew Ashik ◽  
A. Jyothish ◽  
S. Anandaram ◽  
P. Vinod ◽  
Francesco Mercaldo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Dynamic Features ◽  
System Calls ◽  
Prevention Methods ◽  
Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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