Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

Pre-heating temperature induced flowability and wax deposition characteristics of crude oil adding wax inhibitors

This paper investigated the effects of pre-heating treatment temperatures(T_(pre))on the flowability and wax deposition characteristics of a typical waxy crude oil after adding wax inhibitors.It is found that there is little difference in wax precipitation exothermic characteristics of crude oils at different T_(pre),as well as the wax crystal solubility coefficient in the temperature range of 25-30℃.For the undoped crude oil,the flowability after wax precipitation gets much improved and the wax deposition is alleviated as T_(pre)increasing.At T_(pre)=50℃,the viscosity and wax deposition rate of crude oil adding wax inhibitors are higher than those of the undoped crude oil.When the T_(pre)increases to 60,70,and 80℃,the flowability of the doped crude oil are largely improved and the wax deposition is suppressed with the T_(pre)increase,but the wax content of wax deposit increases gradually.It is speculated that,on the one hand,the T_(pre)increase helps the dispersion of asphaltenes into smaller sizes,which facilitates the co-crystallization with paraffin waxes and generates more aggregated wax crystal flocs.This weakens the low-temperature gel structure and increases the solid concentration required for the crosslink to form the wax deposit.On the other hand,the decrease in viscosity increases the diffusion rate of wax molecules and accelerates the aging of wax deposits.The experimental results have important guiding significance for the pipeline transportation of doped crude oils.

Wax deposition modeling in oil-water stratified pipe flow

Wax deposition in oil-water stratified flow is commonly encountered onshore and offshore oil production pipe systems,and typically reduces transportation capacity of oil.The accurate predicted model of wax deposition has becomes an indispensable approach to design effective remediation strategies.However,a reliable mechanistic model for wax deposition prediction in oil-water two-phase stratified pipe flow is lacking to validate the deposition process.In this work,a three-dimensional(axial,radial,and angular)robust wax deposit model for oil-water stratified circular pipe flow was developed.The model of formation of a gel deposit based on the first principles of rheology was developed,associated with the results obtained from hydrodynamics and heat/mass transfer simulations.The predictions for wax deposition are found to compare satisfactorily with experimental data with two different oils for single phase and four different water cuts for oil-water stratified pipe flow.It can be seen from the wax gelation mechanism that an increase in water cut can help to reduce the wall/oil-deposit interface shear stress,thereby leading to an increase in the degree of gelation as well as the deposit rate.Furthermore,a local deposit analysis in the circumferential direction was conducted,for water cut 75%and total flow rate 5 m3/h,which provided insights to understand that the thickness on pipe wall was roughly uniformly distributed locates near the top of the pipe and the nearer the position gets close to two points,where the oil-water interface contacts the inner wall,the deposition thickness quickly dropped to 0.It was attributed to the fact that a roughly uniformly thickness far away from the oil-water interface contact the inner wall resulted in the slowly changes temperature along the circumferential pipe wall wetted by oil.

Effect of surfactants and their blend with silica nanoparticles on wax deposition in a Malaysian crude oil

The present study investigated the wax deposition tendencies of a light Malaysian crude oil（42.4° API）, and the wax inhibiting potential of some surfactants and their blends with nanoparticles. With the knowledge that the majority of the wax inhibition research revolved around polymeric wax inhibitors, which cause environmental issues, we highlighted the potential of surfactants and their blend with SiO2 nanoparticles as wax deposition inhibitors. Different surfactants including oil-based, silane-based, Gemini and bio-surfactants were considered as primary surfactants. The primary surfactants and their respective blends at a concentration of 400 ppm were screened as wax inhibitor candidates using cold finger apparatus. The screening results showed a significant influence on the paraffin inhibition efficiency on wax deposition by using 400 ppm of silane-based surfactant, which decreased the wax deposition up to 53.9% as compared to that of the untreated crude oil. The inhibition efficiency among the silane-based surfactant（highest） and bio-surfactant（lowest）revealed an appreciable difference up to 36.5%. Furthermore, the wax from the treated sample was found to deposit in a thin gel-like form, which adhered inadequately to the surface of the cold finger. A further investigation by blending the 400 ppm silane-based surfactant with a 400 ppm SiO2 nanoparticle suspension in a load ratio of 3：1 found that the wax inhibition decreased up to 81% as compared to the scenario when they were not added. However, we have shown that the synergy between the silane-based surfactant and the nanoparticles is influenced by the concentration and load ratio of surfactant and nanoparticles, residence time, differential temperature and rotation rate.

Predicting wax deposition using robust machine learning techniques

Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid many issues that interrupt the flow assurance during production of hydrocarbon fluids.The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions.To do so,multilayer perceptron(MLP)optimized with Levenberg-Marquardt algorithm(MLPLMA)and Bayesian Regularization algorithm(MLP-BR)were taught using 88 experimental measurements.These latter were described by some independent variables,namely temperature(in K),specific gravity,and compositions of C1eC3,C4eC7,C8eC15,C16eC22,C23eC29 and C30þ.The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination(R2)of 0.9971.The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature.

Wax deposition law and OLGA-Based prediction method for multiphase flow in submarine pipelines

In this study,the effects of wax deposition on submarine multiphase pipelines are investigated.In order to understand the mechanism of wax deposition in submarine multiphase pipelines,a wax deposition model in a submarine pipeline was established using the OLGA wax deposition module,and key factors influencing this phenomenon were analyzed.Additionally,a multifactor impact analysis of wax deposition was performed via the orthogonal test method.The results indicated the relative influence of five factors on oil-gas-water three-phase wax deposition,namely,oil flow rate,water content,inlet temperature,gas-oil ratio,and outlet pressure.Then,the main influencing factors of wax deposition in a multiphase pipeline flow were extracted,and the wax deposition prediction model was established.The wax deposition rate under different operating conditions was simulated using the OLGA wax deposition simulation module.The SPSS software was used to perform nonlinear regression analysis under different working conditions.In this manner,the constant terms in the wax deposition prediction model for a submarine multiphase pipeline were obtained.The prediction model could be used to programmatically predict wax deposition along a multiphase pipeline by programming the initial waxing conditions.By using this method,the wax deposition prediction model of an MN submarine multiphase pipeline was obtained under different working conditions.After comparing the predicted and the OLGA simulation results,it was concluded that the established wax deposition model can be used to accurately calculate the wax deposition in a submarine multiphase pipeline within an allowable error range.If the physical properties and operating parameters of the conveying medium in a multiphase pipeline are given,the developed oil-gas-water three-phase wax deposition model can be used to predict the distribution of wax deposition in submarine multiphase pipelines without laboratory experiments.The results of this study can help production units understand the waxing situation of pipelines in time in order to scientifically formulate a pigging plan and avoid pipeline blockages and shutdowns.

A prediction method for the wax deposition rate based on a radial basis function neural network

The radial basis function neural network is a popular supervised learning tool based on machinery learning technology.Its high precision having been proven,the radial basis function neural network has been applied in many areas.The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power,a decreased flow rate or even to the total blockage of the line,with losses of production and capital investment,so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline.This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors,the pipe wall temperature gradient,pipe wall wax crystal solubility coefficient,pipe wall shear stress and crude oil viscosity,by the gray correlational analysis method.MATLAB software is employed to establish the RBF neural network.Compared with the previous literature,favorable consistency exists between the predicted outcomes and the experimental results,with a relative error of 1.5%.It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

Physical properties of wax deposits on the walls of crude pipelines

Wax deposits on the wall of a crude oil pipeline are a solid wax network of fine crystals, filled with oil, resin, asphaltene and other impurities. In this paper, a series of experiments on wax deposition in a laboratory flow loop were performed under different conditions （flow rate, temperature differential between crude oil and pipeline wall, and dissolved wax concentration gradient）, and the wax deposits were analyzed, so quantitative relationships among wax content, wax appearance temperature （WAT）, shear stress, and radial concentration gradient of dissolved wax at the solid/liquid interface were obtained. Finally, a model was established to predict WAT and the wax content of the deposit.

Waxes in asphaltenes of crude oils and wax deposits

Composition and molecular mass distribution of n-alkanes in asphaltenes of crude oils of different ages and in wax deposits formed in the borehole equipment were studied. In asphaltenes, n-alkanes from C12 to C60 were detected. The high molecular weight paraffins in asphaltenes would form a crystalline phase with a melting point of 80–90 ℃. The peculiarities of the redistribution of high molecular paraffin hydrocarbons between oil and the corresponding wax deposit were detected. In the oils, the high molecular weight paraffinic hydrocarbons C50–C60were found, which were not practically detected in the corresponding wax deposits.

Application of Epoxy Coatings to Increase the Efficiency of Wax Oils Production

When producing hydrocarbons,an important task relates to the optimization of the stock of the producing well.The main complications for wells in non-working mode are represented by the formation of asphalt-resinparaffin deposits.This issue is one of the most common problems in the production and transportation of oil.A promising method to deal with these deposits is the application of smooth coatings made of epoxy polymers on the inner surface of the production well tubing.In this work,a number of laboratory studies were carried out on the“Cold Finger”installation to assess the effectiveness of this approach.These laboratory studies have shown that the efficiency related to smooth coatings is 27%while the resulting thermal conductivity ranges from 0.259 to 0.279 W/(m°C).These results demonstrate that this technology can reduce the amount of organic deposits and increase the temperature of oil.