A Power Law Committee Scaling Equation for Quantitative Estimation of Asphaltene Precipitation

Precipitation and deposition of asphaltene as a challenging issue have drawn much attention in oil industry owing to severe problems that created during petroleum production and processing. Due to its adverse impact on petroleum production, proposed potent model which capable to predict amount of asphaltene precipitation with high accuracy is necessary. Recently different researcher proposed novel model so-called scaling equation for predicting the amount of asphaltene precipitation. Although derived equation is valuable it possess with flaw as limit accuracy of prediction. The current study proposes a novel technique to predict the accurate value of asphaltene precipitation amount by integration of different scaling equation using the concept of power law committee machine (PLCM). Elements of PLCM model are Rassamdana scaling (RE) model, Hu scaling (HU) model, and Ashoori scaling (AS) model. PLCM model has a parallel architecture that combined the outputs of the aforementioned models in order to reaping the benefits of individual models and increases the accuracy of final asphaltene precipitation amount prediction. Optimal contribution of individual scaling equations in final output is computed by virtue of genetic algorithm (GA) tool. Finally, determined result from PLCM model is compared with individual scaling approaches. It is observed, implementation of PLCM can lead to more accurate prediction compared to different scaling models which conducted alone for predicting amount of asphaltene precipitation

Magnetic field effect on convective heat transfer in corrugated flow channel

In this study heat transfer and fluid flow analysis in a wavy channel is numerically studied, while a magnetic field is applied in transverse direction to the main flow stream. Recently in a numerical study, we have observed that usage of wavy channel instead of straight one enhances heat exchange between the core flow and hot walls. On the other hand, the usage of magnetic field transverse to hot walls can enhance heat transfer in a straight channel. In this paper, we would like to examine if presence of these two methods simultaneously is useful for enhancement of heat exchange. For this purpose, the governing equations are numerically solved in the domain by the control volume approach based on the SIMPLE technique. Numerical studies are performed over a range of Reynolds number, Hartmann number, and the wave amplitude. From this study, it is concluded that heat transfer in channels can be enhanced by the usage of magnetic field or usage of wavy channel instead of a straight one. But simultaneous usage of magnetic field and wavy channel is not recommended

Magnetic field effect on convective heat transfer in corrugated flow channel

In this study heat transfer and fluid flow analysis in a wavy channel is numerically studied, while a magnetic field is applied in transverse direction to the main flow stream. Recently in a numerical study, we have observed that usage of wavy channel instead of straight one enhances heat exchange between the core flow and hot walls. On the other hand, the usage of magnetic field transverse to hot walls can enhance heat transfer in a straight channel. In this paper, we would like to examine if presence of these two methods simultaneously is useful for enhancement of heat exchange. For this purpose, the governing equations are numerically solved in the domain by the control volume approach based on the SIMPLE technique. Numerical studies are performed over a range of Reynolds number, Hartmann number, and the wave amplitude. From this study, it is concluded that heat transfer in channels can be enhanced by the usage of magnetic field or usage of wavy channel instead of a straight one. But simultaneous usage of magnetic field and wavy channel is not recommended

Model-based production optimization under geological and economic uncertainties using multi-objective particle swarm method

Optimization of the water-flooding process in the oilfields is inherently subject to several uncertainties arising from the imperfect reservoir subsurface model and inadequate data. On the other hand, the uncertainty of economic conditions due to oil price fluctuations puts the decision-making process at risk. It is essential to handle optimization problems under both geological and economic uncertainties. In this study, a Pareto-based Multi-Objective Particle Swarm Optimization (MOPSO) method has been utilized to maximize the short-term and long-term production goals, robust to uncertainties. Some modifications, including applying a variable in the procedure of leader determination, namely crowding distance, a corrected archive controller, and a changing boundary exploration, are performed on the MOPSO algorithm. These corrections led to a complete Pareto front with enough diversity on the investigated model, covering the entire solution space. Net Present Value (NPV) is considered the first goal that represents the long-term gains, while a highly discounted NPV (with a discount rate of 25%) has been considered short-term gains since economic uncertainty risk grows with time. The proposed optimization method has been used to optimize water flooding on the Egg benchmark model. Geological uncertainty is represented with ensembles, including 100 model realizations. The k-means clustering method is utilized to reduce the realizations to 10 to reduce the computing cost. The Pareto front is obtained from Robust Optimization (RO) by maximizing average NPV over the ensembles, as the conservative production plan. Results show that optimization over the ensemble of a reduced number of realizations by the k-means technique is consistent with all realizations’ ensembles results, comparing their cumulative density functions. Furthermore, 10 oil price functions have been considered to form the economic uncertainty space. When SNPV and LNPV are optimized, considering uncertainty in oil price scenarios, the Pareto front’s production scenarios are robust to oil price fluctuations. Using the robust Pareto front of LNPV versus SNPV in both cases, one can optimize production strategy conservatively and update it according to the current reservoir and economic conditions. This approach can help a decision-maker to handle unexpected situations in reservoir management

Application of response surface methodology for optimization of the stability of asphaltene particles in crude oil by TiO₂/SiO₂ nanofluids under static and dynamic conditions

<p>In this work, the onset of asphaltene flocculation for an Iranian crude oil by titration of samples with heptane in the presence and absence of the TiO₂/SiO₂ nanofluids was obtained by Near-IR spectroscopy. Nanoparticles and nanocomposites were characterized by BET, FESEM, EDX, XRD, and XRF analysis. Modeling and optimization of inhibition of asphaltene flocculation process by TiO₂/SiO₂ nanofluids were conducted by response surface methodology (RSM). Under optimum conditions (nanocomposite composition = 0.04 wt% (80%TiO₂:20%SiO₂), salinity = 4.01 wt%, and pH = 3.42), the onset point increased. For nanofluids stability analysis, the optimum nanofluid was compared with the two other nanofluids (SiO₂ and TiO₂) by visual observation method. The results indicated that high stability and surface area of the 80%TiO₂ nanocomposites increase asphaltene adsorption on the particles surface that subsequently increases the onset point. In addition, the optimum nanofluid performance on the carbonate rocks was evaluated by contact angle and core flooding experiments. The 80% TiO₂ nanofluid changed the wettability of carbonate rocks from strongly oil-wet to strongly water-wet condition and also decreased the residual oil saturation and enhanced the oil recovery with an increase in the recovery factor of about 15%.</p