油田开发过程中套管损坏预报方法及应用研究
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摘要
随着油田的不断开发,世界范围内套损现象日趋严重,给油田带来了巨大经济损失,也严重妨碍了油田正常生产。因此,采用合理的预报方法对套管损坏情况进行早期预报并采取相应的预防措施,对延长套管使用寿命、降低套损率进而实现高效生产具有重要意义。
套损问题是一个非常复杂的大系统,其具有不确定性、模糊性、时变性的特点。因此,本文首先从系统论的角度,介绍了预报基本理论、常用预报方法,通过对常用预报方法的对比分析优选了支持向量机方法,该方法尤其适合解决小样本、非线性、高维数问题。然后结合油田现场实际,从套损的不同视角,系统全面地考察了油田开发过程中导致套管损坏的潜在因素并分析了机理。结果表明,油田开发过程中套管损坏是多因素共同作用的结果,其核心机理在于开发因素如局部注水压力、注采比、注水强度及注采对应情况等的改变产生非对称的孔隙压差使得套管受力状态发生变化,从而导致套管发生不同程度的损坏。在ROSETTA平台上应用粗糙集理论对套损影响因素进行属性约简,并基于属性重要度的概念计算套损相关因素的权重系数,从而找出油田开发过程中油水井套损的敏感性因素;以约简后的属性集作为支持向量机输入,针对传统支持向量机解决时变问题存在的局限性,提出了基于时间序列和支持向量机集成的动态预报方法,并建立了开发过程中套损动态预报模型,考察了核函数、核参数、样本数量、样本维数对预报精度的影响。针对浅层套管腐蚀问题,室内配置了不同组分的模拟地层水,考察了钙离子浓度、镁离子浓度、碳酸氢根离子浓度、氯离子浓度、矿化度、溶解氧、pH值、温度等对套管钢腐蚀的影响;利用灰色关联分析法研究了各种因素对套管钢腐蚀的影响程度;用支持向量机回归算法建立了套管腐蚀预测模型。结果表明,该方法在套管腐蚀预测中获得了很好的效果,而且它的预测精度要优于回归分析方法和神经网络方法。
在理论研究基础上,以SQL Server数据库、Csharp语言为平台,开发了实用可靠的套损动态预报分析软件系统,并在榆树林油田、港西油田进行了现场应用。结果表明,该系统模型简单、使用方便、可信度高,具有较高的推广应用价值。
With the continuous development of oilfield, casing damage is becoming more and more serious gradually around the world, which has caused enormous economic loss and hindered the routine production severely. Therefore, a reasonable method of forecasting casing damage early and the appropriate preventive measures should be adopted to extend casing life, reduce the rate of casing damage and then to achieve efficient production. So what this paper researched is of great significance.
Casing damage problem is a highly complex large-scale system, which is characterized by uncertainty, ambiguity and time-varying. The basic theory of forecasting and the common forecasting method are introduced from the perspective of system theory firstly. The method of support vector machine is optimally selected by comparative analysis of common forecasting methods, which is particularly suitable to solve the small sample, nonlinear and high dimension problem. Then the underlying factors causing casing damage in the process of oilfield development have been studied systematically and comprehensively and casing damage mechanism is analyzed from different perspectives of casing damage based on field practice. The results indicate that casing damage is the combined effect of many factors in the process of oilfield development. The main mechanism is the change of development parameters, such as local injection pressure, injection-production ratio, water injection strength and the injection-production corresponding situation causing asymmetric pore pressure, which makes the force status of casing changing and then causes different degrees of casing damage. The sensitive factors causing casing damage were identified in the process of oilfield development after the attribute reduction to the influencing factors of casing damage were conducted by the rough set theory on the ROSETTA platform and the weight coefficients of the attributes were figured out by the concept of the significance of attributes. Taking the reduced attributes as the input variables of support vector machine, aiming at limitations of traditional support vector machine to solve the time-varying problem, dynamic forecasting methods based on time series integrated with support vector machines was put forward and casing damage dynamic forecasting model was established in the development process. The impact of kernel function, kernel parameters, sample numbers and sample dimension on forecasting accuracy was studied. Aiming at corrosion problem of shallow casing, simulating formation water of different components were prepared and the influence of calcium concentration, magnesium ion concentration, bicarbonate ion concentration, chlorine ion concentration, salinity, dissolved oxygen, pH value and temperature on the corrosion of casing steel were investigated. Gray correlation analysis method was used to study the impact degree of various factors on corrosion of casing steel and the casing corrosion forecasting model was established based on support vector machine regression algorithm. The results indicate that the method of casing corrosion forecasting can obtain good results, and its forecasting accuracy is superior to that of regression analysis and neural network methods.
On the basis of theoretical research, a practical and reliable casing damage dynamic forecasting analysis software system was developed with SQL Server database and Csharp language. The application results in Yushulin oilfield and Gangxi oilfield showed that this system has the advantages of simplicity, convenience and high reliability, which is worth widely popularizing and applying.
套损问题是一个非常复杂的大系统,其具有不确定性、模糊性、时变性的特点。因此,本文首先从系统论的角度,介绍了预报基本理论、常用预报方法,通过对常用预报方法的对比分析优选了支持向量机方法,该方法尤其适合解决小样本、非线性、高维数问题。然后结合油田现场实际,从套损的不同视角,系统全面地考察了油田开发过程中导致套管损坏的潜在因素并分析了机理。结果表明,油田开发过程中套管损坏是多因素共同作用的结果,其核心机理在于开发因素如局部注水压力、注采比、注水强度及注采对应情况等的改变产生非对称的孔隙压差使得套管受力状态发生变化,从而导致套管发生不同程度的损坏。在ROSETTA平台上应用粗糙集理论对套损影响因素进行属性约简,并基于属性重要度的概念计算套损相关因素的权重系数,从而找出油田开发过程中油水井套损的敏感性因素;以约简后的属性集作为支持向量机输入,针对传统支持向量机解决时变问题存在的局限性,提出了基于时间序列和支持向量机集成的动态预报方法,并建立了开发过程中套损动态预报模型,考察了核函数、核参数、样本数量、样本维数对预报精度的影响。针对浅层套管腐蚀问题,室内配置了不同组分的模拟地层水,考察了钙离子浓度、镁离子浓度、碳酸氢根离子浓度、氯离子浓度、矿化度、溶解氧、pH值、温度等对套管钢腐蚀的影响;利用灰色关联分析法研究了各种因素对套管钢腐蚀的影响程度;用支持向量机回归算法建立了套管腐蚀预测模型。结果表明,该方法在套管腐蚀预测中获得了很好的效果,而且它的预测精度要优于回归分析方法和神经网络方法。
在理论研究基础上,以SQL Server数据库、Csharp语言为平台,开发了实用可靠的套损动态预报分析软件系统,并在榆树林油田、港西油田进行了现场应用。结果表明,该系统模型简单、使用方便、可信度高,具有较高的推广应用价值。
With the continuous development of oilfield, casing damage is becoming more and more serious gradually around the world, which has caused enormous economic loss and hindered the routine production severely. Therefore, a reasonable method of forecasting casing damage early and the appropriate preventive measures should be adopted to extend casing life, reduce the rate of casing damage and then to achieve efficient production. So what this paper researched is of great significance.
Casing damage problem is a highly complex large-scale system, which is characterized by uncertainty, ambiguity and time-varying. The basic theory of forecasting and the common forecasting method are introduced from the perspective of system theory firstly. The method of support vector machine is optimally selected by comparative analysis of common forecasting methods, which is particularly suitable to solve the small sample, nonlinear and high dimension problem. Then the underlying factors causing casing damage in the process of oilfield development have been studied systematically and comprehensively and casing damage mechanism is analyzed from different perspectives of casing damage based on field practice. The results indicate that casing damage is the combined effect of many factors in the process of oilfield development. The main mechanism is the change of development parameters, such as local injection pressure, injection-production ratio, water injection strength and the injection-production corresponding situation causing asymmetric pore pressure, which makes the force status of casing changing and then causes different degrees of casing damage. The sensitive factors causing casing damage were identified in the process of oilfield development after the attribute reduction to the influencing factors of casing damage were conducted by the rough set theory on the ROSETTA platform and the weight coefficients of the attributes were figured out by the concept of the significance of attributes. Taking the reduced attributes as the input variables of support vector machine, aiming at limitations of traditional support vector machine to solve the time-varying problem, dynamic forecasting methods based on time series integrated with support vector machines was put forward and casing damage dynamic forecasting model was established in the development process. The impact of kernel function, kernel parameters, sample numbers and sample dimension on forecasting accuracy was studied. Aiming at corrosion problem of shallow casing, simulating formation water of different components were prepared and the influence of calcium concentration, magnesium ion concentration, bicarbonate ion concentration, chlorine ion concentration, salinity, dissolved oxygen, pH value and temperature on the corrosion of casing steel were investigated. Gray correlation analysis method was used to study the impact degree of various factors on corrosion of casing steel and the casing corrosion forecasting model was established based on support vector machine regression algorithm. The results indicate that the method of casing corrosion forecasting can obtain good results, and its forecasting accuracy is superior to that of regression analysis and neural network methods.
On the basis of theoretical research, a practical and reliable casing damage dynamic forecasting analysis software system was developed with SQL Server database and Csharp language. The application results in Yushulin oilfield and Gangxi oilfield showed that this system has the advantages of simplicity, convenience and high reliability, which is worth widely popularizing and applying.
引文
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