Qinfeng Di

An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine

The flowing bottomhole pressure (FBHP) of gas wells was affected by many factors. Although a lot of research works have been done to predict the FBHP and at least more than ten models were proposed, but no one can effectively provide an accurate results for all ranges of production data and conditions due to the existence of many uncertain relations between the changeable influence factors. In this paper, an artificial intelligence prediction method for FBHP based on the support vector machine (SVM), named the FBHP-SVM method, was studied, and a support vector regression (SVR) model with ε-insensitive loss function (ε-SVR) based on radial basis function (RBF) was used to predict the FBHP of gas wells. Compared with the true values, the average absolute and relative errors of the new method were 0.27MPa and 2.29%, respectively. The FBHP-SVM method was also compared to the vertical pipe flowing method. The results showed this new method was a new practical tool to predict FBHP in gas wells and it had a satisfying prediction accuracy. Introduction Accurately predicting the flowing bottomhole pressure (FBHP) of gas wells is the basis of dynamic analysis and production strategies optimization. The FBHP prediction is very complicated because many parameters have influence on the FBHP in the wellbore and they are continuous changing in the process of production. Although the first empirical formula has been put forward for many decades and many new methods have been proposed continually, there is no an applicable available model due to the existence of many uncertain relations between the changeable influence factors [1]. These prediction methods can be roughly classified into three kinds, i.e. empirical correlations, mechanistic models and artificial intelligence methods. Many empirical correlations for FBHP prediction have been developed since the early 1940s. Most of these correlations were proposed by the investigators from laboratory studies, including those of Duns and Ros [2], Hagedorn and Brown [3], Beggs and Brill [4] and Orkiszewski [5]. Generally, these empirical correlations had a perfect performance under the condition that the model was proposed. But when the prediction conditions differ from the specific boundaries that the model was proposed, the FBHP prediction accuracy tends to decrease. Under this circumstances, the mechanistic models were developed to predict FBHP for gas wells. Most of them were semiempirical models and had sound theoretical foundation and wider application than empirical correlations [6]. The widely used mechanistic models are those of Rzasa and Katz [7], Cullender and Smiths [8], Ansari et al. [9], Chokshi et al. [10], Hasan and Kabir [11]. However, some empiricism, more or less, are still involved to overcome the complexity of the problem and the mechanistic models are very difficult to meet the requirement of a complicated well in which the continuous changes of the temperature and pressure will directly affect the gas production rate. And the gradual change in gas volumes will lead to the change of liquid slip velocity and the appearance of new flow patterns. The variation of flow patterns and their transition boundaries inside the well bore 206 Advances in Engineering Research (AER), volume 105 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) Copyright

Experimental Study on Fluid Flow in Porous Media by MRI Technique

Core flood experiment is commonly used to study the fluid seepage law in porous media for enhanced oil recovery, but the characteristics of fluid flow are hard to be visually described. In this paper, an advanced visual displacement system was developed and optimized to observe the shapes of flow front in the porous media. Interestingly, it is found that the flow front shows the triangular shape which can deteriorate the oil recovery. In response to this phenomenon, a series of visually core flood experiments were conducted to study the formation reasons and influencing factors of the triangular shape. Experimental results show that the density difference between the displacing and the displaced fluid is the direct factor which causes the triangular shape of the displacement front. This result suggests that the density difference between the displacing and the displaced fluid should be taken into account during oil production as well as experimental study. Introduction Core flood experiment was widely applied in the reservoir sensitivity evaluation[1,2], fracturing fluid evaluation[3-5],enhance oil recovery technology research[6-11], etc. However, the fluid distribution in the core can’t be directly observed by means of the core flood experiment due to the core is non-transparent porous medium material. Conventional analysis method treats the core as a “black box” and predicts the fluid flow characteristics in the core by testing the pressure, flow rate, etc. In order to visually reflect the characteristics of the fluid flow in the core, many scholars had struggled to find visualization methods. Darwishet al.[12]developed a visual core-flood experiment device, in which the entry point of core holder was transparent and the camera was placed in front of it. But this visualization device could only observe the phenomenon occurring at the entry face, not within the core. Sun et al.[13]made a two-dimensional physical model of micro-layered sandstone and observed the characteristics of fluid flow through physic model by micro photography. However, this physical model could not simulate the state in three-dimensional environment, so these results had a relatively low credibility. In a word, these methods could not completely reflect the fluid distribution in the core, although it made a great contribution to the oil industry. Fortunately, Lauterbur[14]presented a magnetic resonance imaging (MRI)principle based on nuclear magnetic resonance (NMR).The emergence of MRI technique makes it possible to observe the image of the core although initially the MRI technique was mainly used in medical field. Until recent years, magnetic resonance imaging (MRI) technique has made great progress in porous medium testing. Paulsen et al.[15] presented a MRI method utilizing paramagnetic tagging in combination with a carefully controlled and ideal flow system, it can quantitatively characterize the effects of geometry and intrinsic flow properties for a point injection into a core. Liuet al.[16]obtained a series of images of glass beads pack by MRI technique and the porosity of the sample was calculated by analyzing the image intensity. The results were in good accordance with the data obtained by the traditional method. MRI technique was proved to be a novel and effective method for measuring the porosity of porous media. Langet al.[17]established the methods to analysis the formation structure, porosity distribution, water and oil distribution by MRI technique. 22 Advances in Engineering Research (AER), volume 105 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) Copyright