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A new multiphase and dynamic asphaltene deposition tool (MAD-ADEPT) to predict the deposition of asphaltene particles on tubing wall

Naseri, S ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.petrol.2020.107553
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. As expounded, the precipitation and deposition of asphaltene particles in pipelines has been proved to be the most challenging flow assurance problem due to its unknown and complex behaviors. In this work, a new multicomponent, multiphase and dynamic tool was developed to model the aggregation and deposition of asphaltene particles in a bulk medium. The multiphase and dynamic asphaltene deposition tool, shortened as MAD-ADEPT is, in fact, a modified version of the previously developed ADEPT. The new tool was developed to make the asphaltene deposition and aggregation concepts in oil production wells more predictable. To tackle the complexity of the asphaltene problem, a bespoke algorithm was developed to predict asphaltene precipitation, aggregation and deposition. The algorithm was made up of three main modules, i.e., thermodynamic, preprocessor and integrated time step adaptive modules. In the thermodynamic module, the Peng-Robinson equation of state was used and fine-tuned to estimate the fluid properties of multiphase hydrocarbon systems, while the PC-SAFT equation of state was used in order to obtain the asphaltene precipitation phase envelope (APE). In the preprocessor module, the sensitivity of the system to the reduction of wellbore inner diameter (ID) was examined. In the main module which could dynamically model the asphaltene aggregation and deposition, a non-isothermal multiphase mechanistic model, a convective-diffusive-reactive model and a non-convective volume of fluid model were solved simultaneously with an adaptive time step selection method. The new MAD-ADEPT was utilized to simulate a real field case to predict the asphaltene deposition in different time and space intervals along the production tubing. The results obtained from MAD-ADEPT was validated by those scant field data available in the literature. The results confirmed that the new tool can predict the asphaltene deposition reliably at various operating conditions within a production system. Also, the effects of multiphase flow as well as wellbore configurational parameters were studied on the amount of asphaltene deposition thickness on tubing wall. The accuracy of the results for the case studies, fast rate of convergence and less CPU computation time are the clearest advantages of the new asphaltene deposition dynamic tool over the previously developed ones. © 2020 Elsevier B.V
  6. Keywords:
  7. Asphaltene deposition ; Flow assurance problem ; MAD-ADEPT ; PC-SAFT equation of state ; Volume of fluid method ; Boreholes ; Deposition ; Equations of state ; Forecasting ; Offshore oil well production ; Oil field equipment ; Oil wells ; Petroleum industry ; Precipitation (chemical) ; Tubing ; Asphaltene aggregation ; Asphaltene precipitation ; Hydrocarbon systems ; Mechanistic modeling ; Oil production wells ; Peng-Robinson equation of state ; Volume of fluid model ; Asphaltenes ; Accuracy assessment ; Aggregation ; Algorithm ; Asphaltene ; Equation of state ; Model validation ; Pipeline ; Precipitation (chemistry) ; Prediction
  8. Source: Journal of Petroleum Science and Engineering ; Volume 195 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0920410520306240