Viscosity prediction in selected Iranian light oil reservoirs:Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature （Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992） for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network （ANN） code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations （Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992） were compared with the experimental data. The prediction procedure was carried out at three different regimes： at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of lran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.

An adaptive artificial viscosity for the displacement shallow water wave equation

The numerical oscillation problem is a difficulty for the simulation of rapidly varying shallow water surfaces which are often caused by the unsmooth uneven bottom,the moving wet-dry interface,and so on.In this paper,an adaptive artificial viscosity(AAV)is proposed and combined with the displacement shallow water wave equation(DSWWE)to establish an effective model which can accurately predict the evolution of multiple shocks effected by the uneven bottom and the wet-dry interface.The effectiveness of the proposed AAV is first illustrated by using the steady-state solution and the small perturbation analysis.Then,the action mechanism of the AAV on the shallow water waves with the uneven bottom is explained by using the Fourier theory.It is shown that the AVV can suppress the wave with the large wave number,and can also suppress the numerical oscillations for the rapidly varying bottom.Finally,four numerical examples are given,and the numerical results show that the DSWWE combined with the AAV can effectively simulate the shock waves,accurately capture the movements of wet-dry interfaces,and precisely preserve the mass.

An artificial viscosity augmented physics-informed neural network for incompressible flow

Physics-informed neural networks(PINNs)are proved methods that are effective in solving some strongly nonlinear partial differential equations(PDEs),e.g.,Navier-Stokes equations,with a small amount of boundary or interior data.However,the feasibility of applying PINNs to the flow at moderate or high Reynolds numbers has rarely been reported.The present paper proposes an artificial viscosity(AV)-based PINN for solving the forward and inverse flow problems.Specifically,the AV used in PINNs is inspired by the entropy viscosity method developed in conventional computational fluid dynamics(CFD)to stabilize the simulation of flow at high Reynolds numbers.The newly developed PINN is used to solve the forward problem of the two-dimensional steady cavity flow at Re=1000 and the inverse problem derived from two-dimensional film boiling.The results show that the AV augmented PINN can solve both problems with good accuracy and substantially reduce the inference errors in the forward problem.

Optimization of Artificial Viscosity in Production Codes Based on Gaussian Regression Surrogate Models

To accurately model flows with shock waves using staggered-grid Lagrangian hydrodynamics, the artificial viscosity has to be introduced to convert kinetic energy into internal energy, thereby increasing the entropy across shocks. Determining the appropriate strength of the artificial viscosity is an art and strongly depends on the particular problem and experience of the researcher. The objective of this study is to pose the problem of finding the appropriate strength of the artificial viscosity as an optimization problem and solve this problem using machine learning (ML) tools, specifically using surrogate models based on Gaussian Process regression (GPR) and Bayesian analysis. We describe the optimization method and discuss various practical details of its implementation. The shock-containing problems for which we apply this method all have been implemented in the LANL code FLAG (Burton in Connectivity structures and differencing techniques for staggered-grid free-Lagrange hydrodynamics, Tech. Rep. UCRL-JC-110555, Lawrence Livermore National Laboratory, Livermore, CA, 1992, 1992, in Consistent finite-volume discretization of hydrodynamic conservation laws for unstructured grids, Tech. Rep. CRL-JC-118788, Lawrence Livermore National Laboratory, Livermore, CA, 1992, 1994, Multidimensional discretization of conservation laws for unstructured polyhedral grids, Tech. Rep. UCRL-JC-118306, Lawrence Livermore National Laboratory, Livermore, CA, 1992, 1994, in FLAG, a multi-dimensional, multiple mesh, adaptive free-Lagrange, hydrodynamics code. In: NECDC, 1992). First, we apply ML to find optimal values to isolated shock problems of different strengths. Second, we apply ML to optimize the viscosity for a one-dimensional (1D) propagating detonation problem based on Zel’dovich-von Neumann-Doring (ZND) (Fickett and Davis in Detonation: theory and experiment. Dover books on physics. Dover Publications, Mineola, 2000) detonation theory using a reactive burn model. We compare results for default (currently used values in FLAG) and optimized values of the artificial viscosity for these problems demonstrating the potential for significant improvement in the accuracy of computations.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

Shock calculation based on second viscosity using local differentialquadrature method

Based on the second viscosity, the local differential quadrature （LDQ） method is applied to solve shock tube problems. It is shown that it is necessary to consider the second viscosity to calculate shocks and to simulate shock tubes based on the viscosity model. The roles of the shear viscous stress and the second viscous stress are checked. The results show that the viscosity model combined with the LDQ method can capture the main characteristics of shocks, and this technique is objective and simple.

A DDG Method with a Residual-Based Artificial Viscosity for the Transonic/Supersonic Compressible Flow

In this work,a direct discontinuous Galerkin(DDG)method with artificial viscosity is developed to solve the compressible Navier-Stokes equations for simulating the transonic or supersonic flow,where the DDG approach is used to discretize viscous and heat fluxes.A strong residual-based artificial viscosity(AV)technique is proposed to be applied in the DDG framework to handle shock waves and layer structures appearing in transonic or supersonic flow,which promotes convergence and robustness.Moreover,the AV term is added to classical BR2 methods for comparison.A number of 2-D and 3-D benchmarks such as airfoils,wings,and a full aircraft are presented to assess the performance of the DDG framework with the strong residualbased AV term for solving the two dimensional and three dimensional Navier-Stokes equations.The proposed framework provides an alternative robust and efficient approach for numerically simulating the multi-dimensional compressible Navier-Stokes equations for transonic or supersonic flow.

Artificial viscosity in the transonic stream function formulation

The artificial density method which has been applied widely in the transonic potential calculation and the current transonic stream function calculation is investigated theoretically. The analysis shows that in the stream function formulation the artificial density is not equivalent to the artificial viscosity and cannot be used, and a correct expression of the artificial viscosity in the stream function method is then derived. The principal equation of the stream function, the density equation converted from one of the momentum equations and the present artificial viscosity scheme constitute the complete transonic stream function formulation. The numerical practice demonstrates that the range of Mach number computed by this approach is extended and the shock location is close to the experimental result.

A Node-Centered Artificial Viscosity Method for Two-Dimensional Lagrangian Hydrodynamics Calculations on a Staggered Grid

This work deals with the simulation of two-dimensional Lagrangian hydrodynamics problems.Our objective is the development of an artificial viscosity that is to be used in conjunction with a staggered placement of variables:thermodynamics variables are centered within cells and position and fluid velocity at vertices.In[J.Comput.Phys.,228(2009),2391-2425],Maire develops a high-order cell-centered scheme for solving the gas dynamics equations.The numerical results show the accuracy and the robustness of the method,and the fact that very few Hourglass-type deformations are present.Our objective is to establish the link between the scheme of Maire and the introduction of artificial viscosity in a Lagrangian code based on a staggered grid.Our idea is to add an extra degree of freedom to the numerical scheme,which is an approximation of the fluid velocity within cells.Doing that,we can locally come down to a cell-centered approximation and define the Riemann problem associated to discrete variable discontinuities in a very natural way.This results in a node-centered artificial viscosity formulation.Numerical experiments show the robustness and the accuracy of the method,which is very easy to implement.

Suitability of artificial viscosity discontinuous Galerkin method for compressible turbulence

A discontinuous Galerkin method based on an artificial viscosity model is investigated in the context of the simulation of compressible turbulence. The effects of artificial viscosity on shock capturing ability, broadband accuracy and under-resolved instability are examined combined with various orders and mesh resolutions. For shock-dominated flows, the superior accuracy of high order methods in terms of discontinuity resolution are well retained compared with lower ones. For under-resolved simulations, the artificial viscosity model is able to enhance stability of the eighth order discontinuous Galerkin method despite of detrimental influence for accuracy. For multi-scale flows, the artificial viscosity model demonstrates biased numerical dissipation towards higher wavenumbers. Capability in terms of boundary layer flows and hybrid meshes is also demonstrated.It is concluded that the fourth order artificial viscosity discontinuous Galerkin method is comparable to typical high order finite difference methods in the literature in terms of accuracy for identical number of degrees of freedom, while the eighth order is significantly better unless the under-resolved instability issue is raised. Furthermore, the artificial viscosity discontinuous Galerkin method is shown to provide appropriate numerical dissipation as compensation for turbulent kinetic energy decaying on moderately coarse meshes, indicating good potentiality for implicit large eddy simulation.

Applying artificial neural network to predict the viscosity of microalgae slurry in hydrothermal hydrolysis process

Estimation of the viscosity of microalgae slurry is the premise for the design of industrial reactors in microalgal biofuel production.To accurately predict the viscosity of microalgae slurry(Chlorella pyrenoidosa),an artificial neural network(ANN)model is designed in this study.In the ANN model,the mass fraction of microalgal cell,shear rate,temperature,and retention time during the hydrothermal hydrolysis process are used as the input variables,and the viscosity of microalgae slurry is obtained as the output variable.Comparisons show that the ANN model is in excellent agreement with the experimental data.The mean square error(MSE),Mean Absolute Error(MAE),and goodness of fit(R 2)are 0.725,0.484 and 0.991,respectively.The results provide a proof-of-concept for using ANN models to estimate the viscosity of microalgae slurry.In particular,the developed ANN model can accurately predict the viscosity of microalgae slurry in a hydrothermal hydrolysis process,which cannot be accurately predicted by a standard curve fitting method.

水下爆炸冲击波数值仿真精度研究

在水下爆炸数值仿真研究中,网格尺寸和一次项人工粘性系数对冲击波峰值压力计算结果有较大影响。在预定计算精度条件下,快速确定网格尺寸及人工粘性对数值计算意义重大。为此,文中基于LSDYNA有限元软件,建立78 g三硝基甲苯(TNT)二维水下爆炸数值计算模型,重点分析网格尺寸和一次项粘性系数对水下爆炸冲击波峰值压力和整体计算误差的影响规律。结果表明,随着网格密度因子的增大,计算峰值压力对网格的敏感性降低,且网格密度较大时,过小的一次项系数会导致计算峰值压力与经验公式值的相对误差增大。在此基础上获得20%范围内误差与网格尺寸、粘性系数之间的关系,并构建出可用于快速确定网格尺寸和一次项人工粘性系数的误差预估模型,通过0.2~5000 kg范围内的TNT柱形装药(长径比为1)和球形装药的水下爆炸计算,验证了预估模型的普适性,可为二维中近场范围内的水下爆炸冲击波数值仿真计算研究提供参考。

两种AI测试模型在棉粘胶定量分析中的应用

针对传统纤维含量显微镜测定法存在的弊端,鉴于人工智能在检测领域的应用和发展,建立了两种人工智能(AI)测试模型对棉粘胶在纵向、横截面进行准确识别、分类和定量分析,探讨了制样的因素对两种人工智能测试模型识别的影响,并将人工物理法和AI模型测试结果进行分析比对。结果表明:两种AI模型结果与人工显微镜法的结果基本一致,绝对误差均在2.5%以内;且测试效率高,由人工测试耗时80 min左右降低到AI模型检测的13 min。认为:两种AI测试模型的综合运用可实现棉粘胶混纺纤维的快速、准确定量检测。

人工神经网络在含蜡原油黏度预测中的应用

与传统模型相比,人工神经网络在含蜡原油黏度预测中具有更高的准确性及泛化能力。从原油组分、黏度等多个角度进行分析,结合温度、黏度等因素的影响,建立基于人工神经网络的含蜡原油黏度预测模型,对神经网络结构和参数进行优化,进一步提高模型性能,从人工神经网络预测纳米润滑油黏度与原油黏度预测两方面讨论了其在含蜡原油黏度预测中的应用,以期提供理论参考。

水下爆炸冲击波数值模拟中的参数影响

为提高数值模拟水下爆炸冲击波的精度,研究了状态方程、网格密度、人工粘性对数值模拟的影响.以球形药包为例,采用有限元程序MSC.Dytran进行数值模拟,将结果与经验公式对比,分析了各参数的影响作用.结果表明,状态方程的形式对结果影响不大,而网格密度有很大影响.为确定最佳的网格密度,定义了一个描述网格密度的无因次量,并给出了不同药量时最佳无因次网格密度的范围.人工粘性能够较好地抑制冲击波压力计算中出现的数值振荡,但同时也引起了冲击波峰值的衰减.调整水的状态方程的系数,能够在保证数值模拟效果的同时,减少水域中的单元数量.

水中金属丝电爆炸冲击波一维数值模拟

为了明确水中金属丝电爆炸冲击波的形成机理,基于经典活塞模型,采用相似参数法,引入双人工黏性系数,建立了水中金属丝电爆炸冲击波的一维计算模型。以等离子体放电通道边界膨胀轨迹作为模型唯一的输入参数,得到水中冲击波传播的时间与空间的分布规律,通过与其他文献实验结果的对比,验证了该计算方法的有效性。模拟结果表明,双人工黏性系数法使计算得到的冲击波更符合实际情况。爆炸初始时刻,等离子体放电通道边界膨胀压缩周围水介质产生的冲击波高达GPa级,冲击波压强峰值与径向传播距离的-0.70次幂成正比关系。文中采用的计算方法既不涉及脉冲放电过程以及复杂的放电等离子体物理变化过程,也不涉及放电通道与水的物理化学作用过程,只需根据诊断实验得到放电通道边界膨胀轨迹,即可模拟水中金属丝电爆炸冲击波的产生与传播过程,对工程实践具有重要的指导意义。

超短脉冲激光辐照下金属薄膜的热行为

对双温模型的重要热学参量电子热容、电子弛豫时间、电子热导率进行量子化处理,使双温模型能适用于自由电子温度比较高的情况.利用前向差分算法,数值求解了电子-晶格双温双曲两步热传导模型,所得的结果更接近实验值.经过分析得出:1)薄膜前表面自由电子温度达到最大值的时间约为0.27 ps,得到的损伤阈值与实验值符合较好.2)电子热容对电子温升规律影响非常大.电子热导率对自由电子温升规律也有较大的影响.3)在趋肤层内自由电子温升非常快,不同厚度自由电子温度达到最大值所需的时间延迟不明显.趋肤层以下自由电子温度升高较慢,不同厚度自由电子达到最大值所需的时间延迟明显.

计算网格与人工粘性系数对炸药水中爆炸数值模拟计算的影响分析

在炸药水中爆炸的计算中,网格尺寸和人工粘性系数对计算精度有较大影响。确定合理的网格尺寸和人工粘性系数的取值方法,对提高炸药水中爆炸数值模拟计算精度和减小计算量很重要。本文进行了TNT药柱水中爆炸实验,采用基于爆炸相似率的水中爆炸经验公式法,计算了水中冲击波压力峰值和气泡脉动周期,根据实验测量结果验证了经验公式法的计算精度。在此基础上建立了柱形和球形炸药水中爆炸数值计算模型,采用不同网格尺寸和人工粘性系数,对炸药水中爆炸过程进行了数值模拟计算。通过将数值模拟计算与经验公式法计算结果对比,分析了网格尺寸和人工粘性系数,对水中冲击波压力峰值、气泡最大半径和脉动周期的影响。结果显示:冲击波压力峰值与一次项人工粘性系数和网格尺寸存在指数关系,网格密度与一次项人工粘性系数比值大于400时,炸药水中爆炸数值模拟计算可得到较高精度。

III型定常扩展裂纹尖端的黏弹性——理想塑性场

假设扩展裂纹尖端的人工黏性系数与塑性应变率的幂次成反比,推导出理想弹塑性材料的一种率敏感型本构关系.假定应力和应变都具有相同的幂奇异性,对定常扩展裂纹尖端的应力和应变场进行了渐近分析.通过量级分析,讨论了弹性、塑性及黏性3者的匹配条件.对Ⅲ型动态扩展裂纹进行了具体的分析和计算,讨论了解的性质随各参数的变化规律.对相应的准静态问题进行了渐近分析,通过与裂纹扩展速度趋于零时的动态解相比较,表明准静态解是动态解的特例,从而解决了无黏性分析中动态解不能退化为准静态解的矛盾.分析与计算结果表明,黏性效应是扩展裂纹尖端场的一个重要因素.

基于特征粘度的鳕鱼骨胶原蛋白酶解预测模型

以鳕鱼骨为原料制备胶原蛋白并对其酶解，利用人工神经网络建立鳕鱼骨胶原蛋白酶解反应的预测模型。结果显示，胶原蛋白及水解物的粘度随温度的升高而下降，随着胶原蛋白的水解程度加深，肽链长度与分子量减小，特征粘度呈现下降趋势，水解度与特征粘度呈一一映射关系。以水解产物的特征粘度、温度为输入参数，水解度作为输出参数，建立神经网络预测模型。样本值与仿真值的月。值为0．9916，平均相对误差为2．5％，所建网络预测模型性能好、预测精度高。验证试验的相对误差为1．06％～4．32％，实现了鳕鱼骨胶原蛋白酶解反应的仿真预测与监控。