Wax Disappearance Temperature Research Articles

Modeling wax disappearance temperature using robust white-box machine learning

Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.

Prediction of wax disappearance temperature: a review

Wax deposition in pipelines is a recurring problem in the oil industry. Therefore, several studies have analyzed the phenomenon and predicted the wax disappearance temperature (WDT). This variable represents the exact solid-liquid equilibrium point. Such information is an effective aid in decision-making regarding pipelines and production facilities. However, the study of paraffin deposition is highly dependent on conducting experiments, which are typically costly and can make this type of analysis impractical. The increasing progress of models based on machine learning techniques has been an alternative to experimental and thermodynamic methods to predict this phenomenon. In the present study, a bibliographic review of the main authors on models for predicting kerosene deposition has been compiled.

Experimental and Numerical Investigation of the Degelation Behavior and Non-Isothermal Flow Restart of a Waxy Oil Pipeline.

Flow assurance challenges associated with waxy crude oil precipitation at low ambient conditions are significant concerns for oil industries during production, transportation, and storage. Numerous methods have been employed to mitigate wax deposition and gelation issues. Since wax precipitation is temperature-sensitive, heating has emerged as a promising method to enhance oil flowability. The present work intends to examine the degelation behavior of waxy oil using rheometry, differential scanning calorimetry, and microscopy techniques. In addition, a non-isothermal flow restart simulation is performed using an in-house numerical simulator consisting of a rheological model of sol-gel transition developed in the current work. A numerical simulation of a preheated gelled pipeline demonstrates the significance of the degelation temperature. The effects of the wax concentration, initial gel temperature, and aging period on the degelation temperature are examined. The observed degelation temperature is higher than the gelation temperature, leading to thermal hysteresis. The extent of thermal hysteresis reduces with a decrease in the heating rate. The numerical simulation uses the finite volume method with variables placed on a staggered grid. The gel heated above and below the degelation temperature shows a significant variation in axial velocity profiles. However, further heating does not affect the velocity profiles. A shear banding type of effect is observed in the axial velocity profile above the degelation temperature. Heating the gelled oil to the degelation temperature instead of the wax disappearance temperature saves excessive heating energy during storage and transport operations.

Experimental Measurement and Thermodynamic Modeling of the Wax Disappearance Temperature (WDT) for a Quaternary System of Normal Paraffins

Normal paraffin (N-alkane)-based wax is well known as a severe problem in petroleum production, transportation, and processing. Implementing suitable solutions for wax-related problems requires vast technical knowledge and investigation of the wax disappearance temperature (WDT) of multicomponent systems in petroleum-dominated systems. In this study, the WDTs of a quaternary system comprising different mixtures of n-undecane + n-tetradecane + n-hexadecane + n-octadecane were measured using a visual-based diagnosis apparatus under atmospheric pressure. On the other hand, the WDTs of the studied systems are predicted by applying a solid solution model without any adjustable parameter. Two approaches namely γ–φ and γ–γ are assessed. In the (γ–φ) approach, perturbed-chain statistical associating fluid theory (PC-SAFT) is applied for liquid phase modeling, while the solid phase is described using different activity coefficient models. In the (γ–γ) approach, nonidealities of both the liquid and solid phases are investigated using different combinations of activity coefficient models such as ideal solution, regular solution theory, predictive Wilson, predictive UNIQUAC, and UNIFAC. Comparison of experimental data and thermodynamic modeling results indicates that applying the predictive UNIQUAC model for describing the nonideality of the solid phase and the regular solution model for the liquid phase is the best combination for the aforementioned system with the average absolute deviation (AAD) of 0.8 K.

Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models

Solid scales can cause significant problems in oil production and transmission systems such as oil flow rate reduction. Wax is one of the most critical substances that are highly prone to precipitate and deposit in oil pipelines. This occurrence typically happens in low temperatures, and can extremely affect the flow rate. In the wax deposition process, wax disappearance temperature (WDT) is a crucial factor since it denotes the lowest temperature, at which wax deposits melt and the consequences of wax deposition vanish. In this research, smart models were utilized to predict WDT as a function of molar mass and pressure according to a comprehensive database. The proposed intelligent models are radial basis function (RBF) and multilayer perceptron (MLP) neural networks, adaptive neuro-fuzzy inference system (ANFIS), random forest (RF), extra tree (ET), and decision tree (DT). The MLP networks were trained with Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms, and the ANFIS models were coupled with three optimization algorithms, namely Cultural Algorithm (CA), Biogeography-based Optimization (BBO), and Teaching-Learning-Based Optimization (TLBO). Results demonstrate that the developed RF model could present an outstanding performance and provide predictions for WDT with the lowest average absolute percent relative error (AAPRE = 0.246%), the maximum coefficient of determination (for predicting = 0.993), and minimum root mean squared error (RMSE = 1.01). Trend analysis showed that increasing pressure and molar mass leads to wax disappearance temperature increase. Lastly, outlier discovery was performed using the Leverage approach to recognize the suspected data points. The outlier detection found that only 6 points (out of 346) are located in the upper and lower suspected data zones. Wax disappearance temperature is modeled as a function of pressure and crude oil molar mass. RBF, MLP, ANFIS, DT, RF, and ET are used for modeling. LM, BR, CA, BBO, and TLBO are used to optimize the developed models. RF as the best model could predict WDT with the lowest average absolute percent relative error (AAPRE = 0.246%) The Leverage approach demonstrate the reliability of the developed RF model.

The Chemical Performance Evaluation for Pour Point Depressant (PPD) Testing, X Oil Field, Vietnam

Pour Point Depressant (PPD) Testing are used extensively to modify the crystallinity of hard waxes and to reduce the natural pour point of crude. The best PPD chemical and best dosage for crude oil treatment will be chosen based on the laboratory test results to: Reduce pour point of crude oil; Reduce viscosity of crude oil; Improve rheological properties of crude oil; Reduce wax deposition rate and pipeline restart pressure. The chemical performance evaluation for PPD, proposed to use at X Oil Field has been conducted in Crude oil and Petroleum products Lab. The following properties have been determined in laboratory for blank sample (which is a mixture of H1, H4 and H5 crude oil samples with ratio of 2.1:1.4:1.0) and sample treated by PPD (rundown crude oil sample): Pour Point (for blank sample and treated sample); Wax Appearance Temperature (WAT) and Wax Disappearance Temperature (WDT) (for blank sample); Dynamic Viscosity (for blank and treated samples); Yield Stress by Rheometer (for blank and treated samples) for 100 mins, 1000 mins and 02 days shut-in times; Wax Deposition Rate by Cold Finger for blank and treated samples; Restart Pressure by Restart Loop (for blank and treated samples) for 100 mins, 1000 mins and 02 days shut-in times.

Modeling Wax Disappearance Temperature Using Advanced Intelligent Frameworks

The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) th...

Effect of electrical treatment on structural behaviors of gelled waxy crude oil

Waxy crude oil presents poor flowability and complicated rheological behaviors near and below its gelation temperature. In recent years, high voltage direct current electric field has emerged as a novel approach to significantly reduce the viscosity of waxy crude oil below its wax appearance temperature, but the effect of electrical treatment on the structural behaviors of gelled waxy crude oil remains unknown. In the present study, the viscoelasticity, yielding behavior, and thixotropy of gelled waxy crude oil experienced electrically-treatment were investigated over a range of electrical field strength of 0–0.8 kV/mm and treatment temperature between the wax appearance temperature and the pour point. We demonstrate for the first time that the structural strength of gelled waxy crude oil can be significantly weakened after electrical treatment below its wax appearance temperature. The magnitude of the reduction increases with the electric field strength increasing and treatment temperature decreasing. However, no significant influence was found on the wax appearance temperature, wax disappearance temperature, and the amount of precipitated and dissolved wax, revealing the phase equilibrium of wax in crude oil not affected. The investigation of the wax crystal microscopy shows that the wax crystals in the electrically-treated waxy gels are generally larger, and have larger aspect ratio and boundary box fractal dimension, which indicates that the size and morphology of wax crystals are varied by the influence of the electrical treatment.

Prediction of Wax Disappearance Temperature by Intelligent Models

It is well-known that reservoir hydrocarbon fluids contain heavy paraffins that may form solid phases of wax at low temperatures. Problems associated with wax formation and deposition are a major c...

Experimental measurement and modeling study for estimation of wax disappearance temperature

ABSTRACTWax deposition is a frequent problem in oil pipelines and down-stream industries. Correct prediction of wax formation conditions is required to prevent this phenomenon. In this study, wax appearance temperature (WAT) of 12 Iranian oil and condensate samples were measured using viscometry data and differential scanning Calorimetry (DSC) analysis. Also, a new empirical correlation and intelligent artificial neural network (ANN) model were developed to estimate wax disappearance temperature (WDT) of crude oils. Specific gravity, pressure, and molecular weight of oil sample were used as input variables for these models. The ANN model was trained using different hidden neurons and training algorithms. Experimental measurements studies were used for validation of the new correlation. Comparing the results indicated that the ANN model has 0.27% error while most thermodynamic models have an average error of 0.35% to 2.19%. Also, the proposed correlation can predict WDT with good accuracy and minimum input data. Results show that this correlation has a maximum error of 1.16% for 310 published experimental data and 1.19% for 9 Iranian samples.

Study of wax disappearance temperature using multi-solid thermodynamic model

Wax deposition inside pipeline and process equipment is a major problem in oil industry. In this study, a multi-solid thermodynamic model was developed to predict wax disappearance temperature (WDT). Paraffinic–naphthenic–aromatic (PNA) analysis was performed and two correlations were introduced for fusion properties of these species. In addition, WDT of 12 Iranian oil and condensate samples were measured using viscometry and differential scanning calorimetry (DSC) techniques. Experimental data of multi-component and ternary systems were utilized for validation of the model. It was observed that measured WDT by viscometry method is higher compared to DSC. Statistics analysis shows that DSC technique has lower average absolute relative error (AARE) and standard uncertainty compared to viscometry. Results show that the AARE of the model for ternary systems is 0.52% which is much lower among the previous developed thermodynamic models. In addition, AARE of the new model for 68 data was calculated about 0.23%, and R-square of model prediction was calculated about 0.97. The cumulative distribution function also indicates that P50 values are almost the same for model and experimental data. These results show that the model has a good accuracy. In addition, the accuracy of model increases as the average carbon number of oil mixtures increases. Finally, it was found that PNA analysis and distribution of each component in its sub-fractions have a considerable effect on the model accuracy.

A comparative analysis of thermophysical properties correlations for n-paraffins to be used in wax precipitation modeling

The performance of a thermodynamic wax precipitation model strongly depends upon the n-paraffin thermophysical properties used. In order to estimate them, several correlations have been proposed, and their values have a great impact on both calculated wax disappearance temperature (WDT) and amount of wax precipitated at each temperature (WPC). The main goal of this work is to evaluate the correlations available for the relevant thermophysical properties aiming at achieving a reliable wax precipitation modeling. The methodology used involves the direct comparison of the correlations with the values of pure n-paraffin properties, and indirect evaluation by their use in the estimation of wax disappearance temperatures, the amount of wax precipitated at each temperature, and DSC experimental curves. This study contemplates two thermodynamic approaches for paraffin precipitation: the solid solution (SS), which considers the formation of one solid solution; and the multisolid phase model (MS), that assumes that each solid phase consists of a pure component.

Direct adjustment of wax thermodynamic model parameter to micro Differential Scanning Calorimetry thermograms

Among the flow assurance subjects, wax deposition plays a major role. As exploration an oil production moves towards deeper water frontiers, longer tiebacks increase the risk of wax deposition, which is assessed through modeling. In modeling schemes, the phase behavior of solid wax is essential and it is available through either a measured solubility curve or through thermodynamic models. These thermodynamic models normally need tuning and, due to the experimental difficulties of measuring solubility curves for real fluids, are tuned to the wax appearance temperature (WAT). To reduce the uncertainties of adjusting a model to just one point, a procedure to directly simulate DSC curves from a liquid-solid thermodynamic model is proposed. This new strategy allows a full thermogram adjustment without going through the empirical integration of the experimental DSC. Experimental DSC thermograms and simulated curves are presented for five standard single wax mixtures. Then, the solubility curves obtained from the model are compared to the experimental literature data and the difference between simulated wax disappearance temperature (WDT), experimental WDT and experimental wax appearance temperature (WAT) are discussed.

Thermodynamic modeling of wax disappearance temperature in n-alkane mixtures by perturbed hard sphere chain equation of state

In this study, the perturbed hard sphere chain equation of state (PHSC EoS) has been used for the prediction of the wax disappearance temperature (WDT) in n-alkane mixtures. The PHSC EoS can thermodynamically predict the properties of high-molecular-weight compounds and chain-like molecules. The WDT of binary and ternary n-alkane mixtures have been predicted thermodynamically without using any fitting parameter. The liquid and solid phases are described by PHSC EoS based on multi-solid phase theory. The results have shown that the PHSC EoS is capable to predict the WDT of a n-alkane mixture by reasonable accuracy in comparison with experimental data.

Prediction of solid-liquid equilibrium in paraffinic systems with new solid solution model

A thermodynamic model for the prediction of wax precipitation with the new solid solution model is established. For liquid phase, regular solution model and Flory free-volume equation are adopted to consider the two contributions of activity coefficient: enthalpy contribution, the energetic interactions between the components, and entropy contribution, the differences in size and shape between the molecules. For solid phase, the derived solid solution model accounting for the two parts of the solid non-ideality is proposed, where an improved regular solution model is developed for the description of residual part (enthalpy contribution), on the basis of the combination of regular solution theory and local composition theory; while Wilson equation with the consideration of the end effects between molecules is used for combinatorial part (entropy contribution). The improved model is tested against the experimental data of binary, ternary, quaternary and multi-paraffins systems. All experimental data are obtained at atmospheric pressure with temperature varying from 260 K to 350 K. Results show that present model could precisely predict the experimental WATs (wax appearance temperature)/WDTs (wax disappearance temperature) and precipitation curves.

Experimental investigation and thermodynamic modeling of wax disappearance temperature for n-undecane + n-hexadecane + n-octadecane and n-tetradecane + n-hexadecane + n-octadecane ternary systems

In this investigation, the wax disappearance temperatures (WDT) of two ternary n-alkane systems including n-undecane+n-hexadecane+n-octadecane and n-tetradecane+n-hexadecane+n-octadecane are measured using a homemade visual-based apparatus working under atmospheric pressure (0.9bar). The two aforementioned ternary systems are modeled thermodynamically without using any adjustable parameter to predict the wax disappearance temperature. Two methods are used for modeling. In one method, perturbed-chain statistical associating fluid theory (PC-SAFT) is used to describe the liquid phase while the solid phase is described by a solid solution model. In the other method, the nonidealities of the liquid and solid phases are both captured using activity coefficient models. Ideal solution, regular solution theory and predictive Wilson are used separately for description of the liquid phase while ideal solution, regular solution theory, predictive Wilson, predictive UNIQUAC and UNIFAC activity coefficient models are used to describe the nonideality of the solid phase. The obtained results show that the use of regular solution theory for the liquid phase and predictive Wilson for the solid phase leads to closer results to the experimental data compared with other combinations of models for both examined systems.

Experimental measurements and thermodynamic modeling ofwax disappearance temperature for the binary systems n-C14H30 + n-C16H34, n-C16H34 + n-C18H38 and n-C11H24 + n-C18H38

The current work investigates the wax disappearance temperature (WDT) of three binary systems including n-tetradecane+n-hexadecane, n-hexadecane+n-octadecane and n-undecane+n-octadecane as a function of temperature at local atmospheric pressure. Verifying the uncertainties of the measurements, the WDT of pure n-hexadecane and the system n-tetradecane+n-hexadecane are compared with available experimental data reported in literature. Good agreement between the measured experimental data in the present study and those reported previously in literature is observed. Finally, by modeling the paraffin systems with different combinations of ideal solution behavior, regular solution theory, predictive Wilson, predictive UNIQUAC and UNIFAC, the solid–liquid equilibria of the binary systems were described to find the most accurate model without using any adjustable parameter.

Rheological Investigation on the Effect of Shear and Time Dependent Behavior of Waxy Crude Oil

Rheological measurements are essential in transporting crude oil, especially for waxy crude oil. Several rheological measurements have been conducted to determine various rheological properties of waxy crude oil including the viscosity, yield strength, wax appearance temperature (WAT), wax disappearance temperature (WDT), storage modulus and loss modulus, amongst others, by using controlled stress rheometers. However, a procedure to determine the correct parameters for rheological measurements is still unavailable in the literature. The paper aims to investigate the effect of shear and time dependent behaviours of waxy crude oil during rheological measurements. It is expected that the preliminary work could lead toward a proper rheological measurement guideline for reliable rheological measurement of waxy crude oil.

Prediction of wax disappearance temperature using artificial neural networks

In this study, the artificial neural network (ANN) was used for the prediction of WDT. The inputs to network are molar mass and pressure, and the output is WDT at each input. A two-layer network with different hidden neurons and different learning algorithms such as LM, SCG, GDA and BR were examined. The network with 16 hidden neurons and Levenberg–Marquardt (LM) train function showed the best results in comparison with the other networks. Also, the predicted results of this network were compared with the thermodynamic models and better accordance with experimental data for ANN was concluded.

A New Correlation for Prediction of Wax Disappearance Temperature of Hydrocarbon Mixtures at Various Pressures

Wax precipitate is one of the most serious issues the oil industry is currently facing, since it can cause some troubles such as increasing of the pressure losses in pipe which subsequently increases the required power for pumpage. To remove this problem, prediction of wax disappearance temperature (WDT) seems necessary. In this study, the pressure influence on the wax disappearance temperature in multi-component mixtures has been surveyed and some correlations have been proposed to predict the wax disappearance temperature instead of using thermodynamic models. The accuracies of the correlations as AARD are 0.30 %, 0.27 % and 0.68 % for binary, ternary and multi-component mixtures, respectively.