Influence of alkali-surfactant-polymer flooding on the coalescence and sedimentation of oil/water emulsion in gravity separation

Khan, Javed Akbar; Al-Kayiem, Hussain H.; Aleem, Waqas; Saad, Ahmed B.

doi:10.1016/j.petrol.2018.10.055

Cited by 54 publications

(21 citation statements)

References 28 publications

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“…ANN models are effective in handling data with noise, mimic neural networks, perform better than regression based models and accurately predict highly non-linear parameters [30]. In the past, statistical/regression-based models have shown accurate prediction for a lower number of parameters having linearity [31,32]. As the present study involved a large number of non-linear parameters, the advanced statistical approach of machine learning was adopted to accurately predict stuck pipes to reduce the non-productive time (NPT).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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“…The emulsion samples were prepared by mixing equal volumes of oil and synthetic produced water with different concentrations of the chemicals, as shown in Table 3. Previously, (Al-Kayiem and Khan 2017; Khan et al 2019) have carried out experimental studies on the impact of ASP on crude emulsion stabilization, but the concentration of chemicals is higher compared to the low concentrations adopted in the current research. The present study was performed at a minimum possible concentration of the chemical that breaks through in the primary separator.…”

Section: Preparation Of Emulsionmentioning

confidence: 96%

Experimental study on the performance of emulsions produced during ASP flooding

Laben¹,

Alameen

Khan

et al. 2021

J Petrol Explor Prod Technol

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ASP (Alkaline/Surfactant/Polymer) flooding is one of the most promising techniques that has proven to have successful application in several laboratory and pilot tests. However, the formation of persistent and stable emulsions is one of the associated problems with ASP flooding. The present work investigated the effect of sodium carbonate alkaline, Alpha Olefin Sulfonate (AOS) surfactant, and GLP100 polymer on produced crude oil emulsion. The study was conducted by measuring the emulsion stability in terms of water separation and rag layer volume using a TurbiScan analyzer, the dispersed droplet size using cross-polarization microscopy, the interfacial tension using spinning drop tensiometer, and rheological properties using rheometer. The experimental results have shown that AOS presence increased the emulsion stability only when its concentration is above 100 ppm. Meanwhile, below 100 ppm, the presence of AOS promoted water separation and reduced the rag layer volume. In a less significant manner, a high concentration of sodium carbonate alkali increased the stability of the emulsion. The use of GLP100 Polymer has shown substantial ability in promoting water separation and reducing the rag layer volume to a minimal level. It is believed that the outcomes of this work will aid in developing a suitable destabilization process to enhance the oil–water separation and produced water treatment from ASP flooding in the oil production fields. Further investigations on AS, AP, SP as well as the ASP's combined effect on emulsion stability, droplet size, interfacial tension and rheological properties are highly recommended to support the decision-makers on the EOR implementations with chemical additives.

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“…The ANN is an emerging machine-learning tool due to its precise estimations of complex nonlinear systems [ 16 ]. Moreover, gravitational techniques have been also used to enhance separation performance [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Irfan

Waqas

Arshad³

et al. 2022

Materials

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Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.

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Influence of alkali-surfactant-polymer flooding on the coalescence and sedimentation of oil/water emulsion in gravity separation

Cited by 54 publications

References 28 publications

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Experimental study on the performance of emulsions produced during ASP flooding

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

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