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Application of Artificial Neural Network in Prediction of Fluid Properties of CO2-Decane System

机译：人工神经网络在二氧化碳系统流体性能预测中的应用

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CO2-based injection methods have been favourably applying in oil fields due to simultaneous enhanced oil recovery (EOR) and CO2 storage potential Various strategies of CO2 injection including miscible CO2 injection, water alternating gas (WAG) and carbonated water injection have been considered by petroleum engineers During injection of CO2 into the oil reservoirs, CO2 migrates into the the oil phase which this results in higher oil recovery. This is mainly due to improvement of oil mobility by changing of oil properties including density and viscosity. Therefore, accurate calculation of fluid properties of mixture plays an important role in compositional simulation of CO2-EOR techniques. Equations of state (EOS) are normally used for description of fluid properties; however, CO2 fluid at usual reservoir conditions is at supercritical state and most available EOSs show poor capability in predicting the properties of fluids near or above the critical conditions. Artificial neural network (ANN) has shown a good potential for modelling the engineering systems. ANN is an effective algorithm which finds a relationship between input and output data during a learning process. ANN can be used to predict the fluid properties of supercritical CO2-oil mixture. In this study ANN is used to estimate the density and viscosity of CO2-decane mixture from 310 to 403 K and 7 to 30 MPa. The data are collected from the measured data available in the literature. The measured data are the density and viscosity of CO2-decane mixture as a function of temperature, pressure and CO2 mole fraction. Multilayer perceptron (MLP) network is used and trained to find a model which can accurately predict the density and viscosity of CO2-decane mixture at different pressures, temperatures and compositions Mean squared error (MSE) is checked to evaluate the performance of the designed network. Results show that a trained ANN can predict the fluid properties of CO2-decane mixture properly Furthermore. ANN is used and some density and viscosity data for CO2-decane mixture are generated Moreover in this study the potential of ANN for the CO2-decane system is considered, however, it can be extended to study other real oil- CO2 systems if some experimental data be available.
机译：由于同时增强的采油（EOR）和CO2储存潜力，基于二氧化碳的注射方法已经有利地施加在油田中，并且CO2注射的各种策略包括混溶性二氧化碳注射，水交替气体（摇率）和碳酸注射注射的石油在将CO2注入油藏期间的工程师，CO2迁移到油阶段，这导致较高的储油。这主要是由于通过改变包括密度和粘度的油性能改善油动流动性。因此，精确计算混合物的流体性质在CO2-EOR技术的组成模拟中起重要作用。状态（EOS）的方程通常用于流体性质的描述;然而，通常的储层条件下的CO2流体处于超临界状态，并且最多可用的EOSS在预测临界条件附近或高于临界条件的液体性质方面表现出较差的能力。人工神经网络（ANN）已经显示了对工程系统建模的良好潜力。 ANN是一种有效的算法，它在学习过程中找到输入和输出数据之间的关系。 ANN可用于预测超临界CO2-油混合物的流体性质。在该研究中，ANN用于估计CO 2-癸烷混合物的密度和粘度从310-403k和7-30MPa估计。从文献中可用的测量数据收集数据。测量的数据是Co 2癸烷混合物的密度和粘度，作为温度，压力和CO2摩尔级分的函数。使用多层erceptron（MLP）网络和培训以发现可以准确地预测在不同压力下的CO 2癸烷混合物的密度和粘度，温度和组成均值平均误差（MSE）的模型，以评估设计网络的性能。结果表明，此外，培训的ANN可以预测CO 2癸烷混合物的流体性质。使用了Ann，并且在这项研究中，产生了一些密度和粘度数据，在这项研究中，考虑了CO2-癸烷系统的ANN的电位，然而，如果有一些实验，可以扩展到研究其他真正的油二氧化碳系统数据可用。

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