智能井井下监测数据处理方法与应用研究

发布时间：2018-05-03 01:32

本文选题：智能井 + 井下监测数据　；参考：《西安石油大学》2017年硕士论文

【摘要】：智能井井下监测设备获得的生产数据携带了大量的油藏信息,这些信息对我们认识油藏、解释油藏状态以及生产优化控制具有不可估量的价值。但由于各种因素的影响,数据中会存在一些噪音,甚至是异常的数据。为了提高数据的真实性,必须对监测数据进行处理,从而得到较为准确的数据。准确的监测数据才能真实可靠地反映油藏动态变化,将这些数据应用到油藏实时拟合中,可及时地更新油藏模型,有利于获取油藏的流动状态,更好地实现智能井的优化控制。本文以处理智能井井下监测数据为基础,以智能井油藏实时拟合为手段,为真实而准确地获取油藏参数提供了一种新思路。首先,利用Thompson的异常值检测算法检测数据中的奇异值点;其次,基于小波理论,结合MATLAB软件用wden函数对去除异常值后的数据做降噪处理;接着,使用MATLAB软中的自带函数wdencmp函数对数据做压缩处理;最后,深入剖析集合卡尔曼滤波拟合技术,通过序贯高斯模拟方法生成初始油藏模型集合,并结合Eclipse设计智能井油藏实时拟合软件,利用该软件验证EnKF在智能井油藏实时拟合中的作用,同时研究了影响拟合效果的敏感性因素。结果表明:井下监测数据经处理后,噪音水平明显降低,数据冗余度大幅减小,且数据的真实变化趋势和细节特征都被完整地保留了下来,为油藏实时拟合提供了可靠的数据基础。通过油藏实时拟合可得到更为真实的油藏参数,使我们更正确地了解油藏,为油藏的生产优化控制奠定坚实的基础,同时也对合理油藏开发方案的设计提供了重要依据。这些工作有助于我们进一步认识油藏,改善油藏模型,使其能更正确地反映油藏动态,对合理、准确制定油藏生产优化控制策略起着至关重要的作用。
[Abstract]:The production data obtained by intelligent downhole monitoring equipment carry a large amount of reservoir information, which is of inestimable value for us to understand the reservoir, explain the reservoir state and optimize the control of production. However, due to the influence of various factors, there will be some noise in the data, even abnormal data. In order to improve the authenticity of the data, the monitoring data must be processed to obtain more accurate data. The accurate monitoring data can truly and reliably reflect the reservoir dynamic change. Applying these data to the reservoir real-time fitting can update the reservoir model in time, which is helpful to obtain the flow state of the reservoir and to realize the optimization control of the intelligent well better. Based on the data processing of intelligent well downhole monitoring and real-time fitting of intelligent well reservoir, this paper provides a new way to obtain reservoir parameters truthfully and accurately. Firstly, the outlier value detection algorithm of Thompson is used to detect the singular value points in the data. Secondly, based on the wavelet theory and MATLAB software, wden function is used to reduce the noise of the data after removing the outlier value. The wdencmp function in MATLAB software is used to compress the data. Finally, the set Kalman filter fitting technique is deeply analyzed, and the initial reservoir model set is generated by sequential Gao Si simulation. Combining with Eclipse to design the real-time fitting software of intelligent well reservoir, the function of EnKF in real-time fitting of intelligent well reservoir is verified by the software. The sensitive factors that affect the fitting effect are also studied. The results show that the noise level is obviously reduced, the data redundancy is greatly reduced, and the true trend and detail characteristics of the data are completely preserved after the downhole monitoring data is processed. It provides a reliable data base for reservoir real-time fitting. Through real-time reservoir fitting, we can get more real reservoir parameters, make us understand the reservoir more correctly, lay a solid foundation for reservoir production optimization and control, and provide an important basis for the design of reasonable reservoir development plan. These works are helpful for us to further understand the reservoir, improve the reservoir model, make it more accurate to reflect reservoir performance, and play an important role in the rational and accurate formulation of reservoir production optimization control strategy.
【学位授予单位】：西安石油大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE151

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