Numerical simulation of mineral precipitation in hydrocarbon reservoirs and wellbores

Hajirezaie, Sassan; Wu, Xingru; Soltanian, Mohamad Reza; Sakha, Sarah

doi:10.1016/j.fuel.2018.10.101

Cited by 33 publications

(18 citation statements)

References 33 publications

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“…It has been reported that the cost to remediate scale damage in a subsea well can be as high as tens of millions USD (Zhang et al, 2017a). In addition, the operational risks associated with downhole scale remediation and removal are considerably more than that at processing facilities (Cosultchi et al, 2002;Hajirezaie et al, 2019). Thus, oilfield strategy in scaling threat control, especially in subsurface and subsea production systems, is to inhibit or delay scale formation via chemical inhibition.…”

Section: Chemical Inhibition and Delivery Methodsmentioning

confidence: 99%

“…At extreme cases, the conduit is completely blocked by the deposited scale particles. Scale formation can also form in the formation subsurface (Cosultchi et al, 2002;Hajirezaie et al, 2019). During water injection campaigns, seawater can be injected into the formation to enhance oil recovery and to maintain reservoir pressure.…”

Section: Oilfield Flow Assurance and Mineral Scalementioning

confidence: 99%

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Review of Synthesis and Evaluation of Inhibitor Nanomaterials for Oilfield Mineral Scale Control

Zhang

2020

Front. Chem.

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Oilfield flow assurance is the subject to study the impact on the flow of production fluids due to physicochemical changes in the production system. Mineral scale deposition is among the top 3 water-related flow assurance challenges in petroleum industry, particularly for offshore and shale operations. Scale deposition can lead to serious operational risks and significant financial loss. The most commonly adopted strategy in oilfield scale control is the deployment of chemical inhibitors. Although conventional chemical inhibitors are effective in inhibiting scale threat, they have the drawbacks of short transport distance and limited squeeze lifetime due to their intrinsic chemical properties. In the past decade, as an alternative to conventional chemical inhibition, research efforts have been made to prepare functional nanomaterials with different chemical compositions to overcome the drawbacks of conventional chemical inhibitors. These synthesized nanomaterials can serve as delivery vehicles to deploy inhibitors into the target location in the production system. These nanomaterials are reported to have multiple advantages over the conventional inhibitors in terms of transportability, controlled release, and functionality, evidenced by a series of experimental studies. This review presents an overview of scale inhibitor nanomaterial development and the current methods to synthesize and to evaluate these nanomaterials in a systematic and comprehensive manner. This review focuses on the chemistry principles and methodologies underlying inhibitor nanomaterial synthesis and also the chemical instrument and strategies in evaluating the physiochemical properties of these materials in terms of inhibition effectiveness, transportability, and inhibitor return. The scale inhibitor nanomaterials (SINMs) presented in this review exemplify the continuous development in our capabilities in adopting novel nanotechnology in combating actual engineering challenges in petroleum industry.

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Section: Chemical Inhibition and Delivery Methodsmentioning

confidence: 99%

Section: Oilfield Flow Assurance and Mineral Scalementioning

confidence: 99%

Review of Synthesis and Evaluation of Inhibitor Nanomaterials for Oilfield Mineral Scale Control

Zhang

2020

Front. Chem.

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“…Within these processes, chemical, physical, and biological interactions occur over a wide range of temporal and spatial scales [ 1 , 2 ]. Examples of characteristic reactive transport problems are the storage of carbon dioxide in carbonate reservoirs [ 3 , 4 ], contaminated groundwater remediation [ 5 ], storage of radioactive waste [ 6 ], enhanced oil recovery [ 7 ], fuel cells [ 8 ], and the degradation of building materials [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Coupled Lattice Boltzmann-Volume of Fluid Framework for Modeling Porous Media Evolution

2021

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In this paper, we present a framework for the modeling and simulation of a subset of physical/chemical processes occurring on different spatial and temporal scales in porous materials. In order to improve our understanding of such processes on multiple spatio-temporal scales, small-scale simulations of transport and reaction are of vital importance. Due to the geometric complexity of the pore space and the need to consider a representative elementary volume, such simulations require substantial numerical resolutions, leading to potentially huge computation times. An efficient parallelization of such numerical methods is thus vital to obtain results in acceptable wall-clock time. The goal of this paper was to improve available approaches based on lattice Boltzmann methods (LBMs) to reliably and accurately predict the combined effects of mass transport and reaction in porous media. To this end, we relied on the factorized central moment LBM as a second-order accurate approach for modeling transport. In order to include morphological changes due to the dissolution of the solid phase, the volume of fluid method with the piece-wise linear interface construction algorithm was employed. These developments are being integrated into the LBM research code VirtualFluids. After the validation of the analytic test cases, we present an application of diffusion-controlled dissolution for a pore space obtained from computer tomography (CT) scans.

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“…The application of artificial intelligence and soft computing for building intelligent methods in many industries has recently attracted much attention (Anitescu, Atroshchenko, Alajlan, & Rabczuk, 2019;Chuntian & Chau, 2002;Fotovatikhah et al, 2018;Guo, Zhuang, & Rabczuk, 2019;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Taherei Ghazvinei et al, 2018;Yaseen, Sulaiman, Deo, & Chau, 2019). In petroleum and gas industries, intelligent models have been used to determine, oil and gas thermodynamic properties, reservoir formation properties and miscibility conditions required for gas injection processes (Dargahi-Zarandi, Hemmati-Sarapardeh, Hajirezaie, Dabir, & Atashrouz, 2017;Dashtian, Bakhshian, Hajirezaie, Nicot, & Hosseini, 2019;Hajirezaie, Hemmati, & Ayatollahi, 2014;Hajirezaie, Hemmati-Sarapardeh, Mohammadi, Pournik, & Kamari, 2015;Hajirezaie, Pajouhandeh, Hemmati-Sarapardeh, Pournik, & Dabir, 2017;Hajirezaie, Wu, Soltanian, & Sakha, 2019;Hemmati-Sarapardeh, Tashakkori, Hosseinzadeh, Mozafari, & Hajirezaie, 2016;Kamari, Pournik, Rostami, Amirlatifi, & Mohammadi, 2017;Kamari, Safiri, & Mohammadi, 2015;Rostami, Kamari, Panacharoensawad, & Hashemi, 2018). These models take both input and output values to get trained and later can make predictions.…”

Section: Introductionmentioning

confidence: 99%

Modeling natural gas compressibility factor using a hybrid group method of data handling

Hemmati-Sarapardeh

Hajirezaie

Soltanian

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model's superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity. ARTICLE HISTORY

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Numerical simulation of mineral precipitation in hydrocarbon reservoirs and wellbores

Cited by 33 publications

References 33 publications

Review of Synthesis and Evaluation of Inhibitor Nanomaterials for Oilfield Mineral Scale Control

Review of Synthesis and Evaluation of Inhibitor Nanomaterials for Oilfield Mineral Scale Control

A Parallel Coupled Lattice Boltzmann-Volume of Fluid Framework for Modeling Porous Media Evolution

Modeling natural gas compressibility factor using a hybrid group method of data handling

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