Study of wax deposition law by cold finger device

Hu, Zhiyong; Meng, Deli; Liu, Yi; Dai, Zhipeng; Jiang, Nan; Zhuang, Zhenggang

doi:10.1080/10916466.2019.1613431

Cited by 14 publications

(14 citation statements)

References 8 publications

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“…This study investigates the intensity of wax deposition using a cold finger device. According to the literature reviews, many researchers have also studied the kinetics of wax deposition employing the cold finger (Eskin et al 2013;Hu et al 2019;Ito et al 2021;Li et al 2019;Lira-Galeana et al 1996;Phillips et al 2011). The authors stated that four zones were formed in their models.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive method for determining the dewaxing interval period in gas lift wells

Van

Pham²,

Рогачев

et al. 2023

J Petrol Explor Prod Technol

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The gas lift method is an artificial lift method of well production using the energy of compressed gas injected into the well to lift the reservoir fluid to the surface. The formation of paraffin deposits has remained one of the critical oil production problems due to the growing percentage of hard-to-recover reserves in the overall structure. This complication is also typical for many oil and gas fields in Vietnam, such as the White Tiger, White Bear, and Dragon fields. Wax deposit formation negatively affects the operation of individual production wells and the development of the field as a whole, which leads to a decrease in productivity and the need to take measures to remove paraffin deposits, subsequently increasing the downtime period of the well. In order to ensure stable production of highly paraffinic oil, it is necessary to take measures to dewax wells systematically. The frequency of wax removal operations depends on the intensity of the wax formation, which is determined by various technological, technical, and geological factors. The interval between dewaxing operations is called the dewaxing interval period (DIP). This value is an important technological parameter and characterizes the efficiency of a well’s operation. In this study, a comprehensive method has been developed to determine the dewaxing interval period (treatment interval) for gas-lift wells when the formation of wax deposits has occurred. The optimal dewaxing interval period is suggested to be determined by the change in the liquid well flow rate at the point when it falls by 20% from the initial value. In addition, a mathematical model of the time-dependent wax thickness, taking into account the heat and mass transfer laws and the laboratory results using the Cold Finger method, has also been developed. The proposed model for determining the dewaxing interval period was applied to an oil well in Vietnam. The DIP prediction model gave a similar value to the actual DIP field data (6.67 and 6 days, respectively). The obtained results showed that the model had proven its accuracy following the results of a comparison with the field's data of dewaxing operations.

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Section: Introductionmentioning

confidence: 99%

“…Hence, the concentration of the dissolved wax in the layer is subject to the temperature profile. In addition, the kinetics of wax precipitation is rapid and much faster than the wax molecule diffusion rate (Hu et al 2019;Podoprigora et al 2022;Tananykhin et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive method for determining the dewaxing interval period in gas lift wells

Van

Pham²,

Рогачев

et al. 2023

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…The presence of wax deposits in the pipe walls and reservoirs can cause the pipe walls to become smaller, reduce oil production capacity, increase handling costs, increase production downtime, reduce production effi ciency and cause pipe damage when the pressure due to wax deposits is greater than the maximum yield strength of the pipe. (Kamari, et al, 2014;Chi, et al, 2017, Chu et al, 2017Alnaimat & Ziaduddin, 2019;Sousa, et al, 2019;Hu, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil

Septiano¹,

Yasutra²,

Rahmawati³

2022

SCOG

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Wax deposit is one of the major fl ow assurance experienced in the process of oil production and transportation from sub- surface to surface. Large amounts of data are required to perform modeling using existing thermodynamic models such as carbon number data from HGTC. In this paper, a machine learning algorithm using unifi ed model approach from Huang (2008). Two types of input are implemented in order to simulate infl uence of feature selection used in training and testing machine learning which are input A consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), and temperature gradient (dT/dR) and input B consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), temperature gradient (dT/dR), shear stripping variable (SV) dan diffusion variable (DV). The random forest with Ntree = 500 known to be the best machine learning method compared to others. Based on accuracy parameter it achieves error parameter R-squared (R2) for training, testing and total data for input A and B are 0.999, 0.992, 0.9975 and 0.999, 0.993, 0.9977, respectively.

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“…To study wax deposition mimicking conditions encountered in the subsea flowlines, a variety of lab-scale devices have been developed, with the most prevalent being the coldfinger and flow loop. ,− Coldfinger is a small cylindrical heat exchanger made of metal, which is submerged into a bulk oil phase . By separately controlling the temperature of the coldfinger and the bulk oil phase, a specified thermal gradient can be achieved for wax deposition.…”

Section: Introductionmentioning

confidence: 99%

“…8,20−22 Coldfinger is a small cylindrical heat exchanger made of metal, which is submerged into a bulk oil phase. 20 By separately controlling the temperature of the coldfinger and the bulk oil phase, a specified thermal gradient can be achieved for wax deposition. The coldfinger experimental system is simple and inexpensive to build and operate, however, it cannot directly represent the real conditions of wax deposition, because of the incapability to resemble the geometries and flow fields in flowlines.…”

Section: Introductionmentioning

confidence: 99%

Wax Deposition Characterization under Flowing Conditions Using an Oscillatory Flow Setup

Zhang

Mahir

et al. 2021

Energy Fuels

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Wax deposition is a significant flow assurance issue in oil/gas production and transportation, often leading to significant flow constrictions with eventual blockages of flowlines. Wax deposition issues are generally studied using flow loops and ex-situ measurement methods to determine the amount of wax deposition. In this work, we introduce a new benchtop system for wax deposition combined with methods for deposit thickness, distribution, and wax content measurements. Results are shown for both long and short-term wax tests to characterize the evolution of deposit thickness and wax content. Results show that extra deposition will occur during the pigging process and lead to an overestimation of the average deposit thickness from traditional measurement methods; however, it can be overcome by using the new test-needle measuring system. Moreover, a time-varying deposition mechanism is suggested, where deposit thickening is dominated in short-term deposition by the fast formation of a gel layer, and wax accumulation is dominated in long-term deposition, resulting in the hardening of the deposit. Overall, the results highlight the features of our new experimental system for wax deposition test and characterization, which are much needed to advance our understanding on this important flow assurance issue.

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Study of wax deposition law by cold finger device

Cited by 14 publications

References 8 publications

A comprehensive method for determining the dewaxing interval period in gas lift wells

A comprehensive method for determining the dewaxing interval period in gas lift wells

Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil

Wax Deposition Characterization under Flowing Conditions Using an Oscillatory Flow Setup

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