地震叠前数据的弹性阻抗非线性反演方法研究
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摘要
Connolly于1999年正式提出了弹性阻抗的概念,从此掀起了弹性阻抗反演的热潮。弹性阻抗是声阻抗AI与AVO的结合,与AI相比,克服了其垂直入射的假设及由于叠加损失了很多有效信息的缺点,对油气更敏感,能够更加充分的利用多种测井曲线,更全面、更直观反映与油气有关的信息,降低声波阻抗油气检测的多解性,不仅可以进行地层反演,同时也可以进行储层特征反演；与AVO相比,克服了AVO固有的子波不随偏移距变化的缺点,抗噪能力更强,反演更稳健。弹性阻抗反演已经成为波阻抗反演进一步发展的方向之一,地震反演的发展正在走向AI和EI相结合、AI和AVO相结合的道路。
但目前对于弹性阻抗的研究多集中于新的弹性阻抗公式的推导及实际应用中弹性阻抗的定性分析,而对于影响弹性阻抗公式精度的因素以及弹性阻抗反演的定量计算还缺乏系统的研究和讨论。本文介绍了弹性阻抗的基本原理及目前常见的11种PP波弹性阻抗公式,按照是否引入射线参数将其分为两大类,并根据不同模型的正演模拟,详细探讨了影响第一类弹性阻抗公式精度的主要因素：K值、入射角和褶积模型假设。同时,根据不同模型的正演模拟,对两大类弹性阻抗公式的精度进行了详细对比,为选取合适的弹性阻抗公式,有效进行弹性阻抗反演提供理论依据。
关于弹性阻抗的反演方法,由于弹性阻抗概念简单,其反射系数表达形式与声阻抗类似,因此目前弹性阻抗的反演方法也采用与叠后声阻抗反演类似的线性方法或广义线性反演方法,不可避免地造成了其精度低、强烈依赖于初始模型、易陷入局部极优等缺点。为克服这些缺点,本文首次将非线性反演的思想引入弹性阻抗反演过程中,介绍了两种新型的非线性反演方法——蚁群算法和粒子群算法,并成功应用于弹性阻抗反演。
在对蚁群算法的原理、研究现状深入研究的基础上,通过引入反S函数和混沌算子,对基本蚁群算法加以改进,提高了其搜索效率和搜索精度,用四个Benchmark函数测试了改进后的蚁群算法的性能,并将改进后的蚁群算法应用到模型数据的弹性阻抗反演过程中,这是蚁群算法在地震叠前反演的首次应用,而且取得了良好的反演效果。
对粒子群算法的基本原理、参数选择问题及研究现状也进行了探讨,通过引入离散搜索算子和模拟退火算子,大大增强了基本粒子群的全局搜索能力,提高了搜索精度,同样用四个Benchmark函数测试了其性能,并利用改进的混合粒子群算法进行了弹性阻抗反演,效果良好。这同样也是粒子群算法在地震叠前反演领域的首次应用。
基于蚁群算法和粒子群算法以及两项EI-Fatti弹性阻抗公式,本文提出了一种新的弹性阻抗反演新策略,并利用不同程度的随机噪声对弹性阻抗反演进行了抗噪实验,以检验新策略的抗噪能力。模型数据的正反演结果表明,本文提出的弹性阻抗反演新方法和新策略抗噪能力强,对于随机噪声达到10%的角道集数据,仍能反演出合理的弹性参数。而且不依赖于初始模型,不依赖于解释层位,克服了常规弹性阻抗反演强烈依赖初始模型,易陷入局部极优的缺点,是一种切实可行的弹性阻抗反演新方法。
最后利用本文提出的弹性阻抗反演新策略对胜利油田郭局子洼陷沙二段油藏的叠前角道集数据进行了弹性阻抗非线性反演,虽然目的层埋藏较深,AVO特征也不明显,储层预测难度很大,但利用本文提出的弹性阻抗非线性反演新方法得到的属性剖面仍然能够反映出这一AVO异常,与测井资料解释成果相符,验证了本文方法的正确性,说明本文提出的弹性阻抗非线性反演新方法和新策略具有良好的发展潜力和应用前景。
The concept of elastic impedance (El) was formally proposed by Connolly in 1999, and then elastic impedance inversion spread quickly all over the world. Elastic impedance is a combination of acoustic impedance (AI) and AVO. As compared with AI, El overcomes the shortcomings of vertical incidence assumption and losing valid information sensitive to the oil and gas, and can make full use of a variety of logging curves, which makes it more comprehensive and more intuitive to distinguish the oil and gas information and can effectively reduce multi-solution of AI inversion. El can be used for not only stratigraphic inversion, but also for reservoir characteristics inversion. As compared with AVO technique, El overcomes the shotcoming of wavelet not changing with the offset in AVO inversion, and is a more robust inversion method. Elastic impedance inversion has become one of the development direction of seismic prestack inversion. Now the seismic inversion is moving towards the development of AI and El combination, AI and AVO combination.
The studies on elastic impedance are mainly focused on the new elastic impedance formula derivation and the qualitative analysis in practical application, otherwise, there is a lack of systematic study and discussion for the accuracy of El formula and the quantitative calculation of El inversion. This paper discusses the basic principles of El and current eleven PP-wave El formula. According to the introduction of ray-path parameter, these El formula can be divided into two categories. With the forward modeling of different models, the main factors, such as K value, incident angle and the convolution model assumptions impacting on the accuracy of first Class El formula are discussed. Meanwhile, according to different forward modeling result, the accuracy of the two major categories of El formula are compared. These analysis will effectively provide a theoretical basis for the next El inversion.
With regard to El inversion method, because the concept of El is very simple and its reflection coefficient expression is similar to AI's, current El inversion method also uses the linear method or the generalized linear inversion method, just like the post-stack inversion of AI. The linear method has inevitably led to its low accuracy, strong dependence on the initial model, easily falling into a local defects. To overcome these shortcomings, this paper introduces non-linear methods into El inversion process, which are two new non-linear inversion method-ant colony algorithm (ACA) and particle swarm optimization (PSO).
The principle and status quo of ant colony algorithm are presented in this paper. Through the introduction of anti-S function and chaotic operator, a new hydrid ACA is raised, which improves the efficiency and precision of search. Four benchmark functions are used to test the performance of improved ant colony algorithm, and finally the improved ant colony algorithm is applied to the El inversion process of forward modeling data, which has achieved good results.
After introducing the basic principle, parameter selection question and research status of particle swarm optimization algorithm, a new hydrid PSO is raised, which greatly enhances the global search ability and search accuracy of basic PSO by introducing of simulated annealing operator into discrete particle swarm optimization algorithm. The same four benchmark functions test its performance, and then the improved hybrid particle swarm algorithm is used in El inversion, and achieved good results.
After the above discusses, a new El nonlinear inversion method based on ant colony algorithm and particle swarm algorithm with EI-Fatti formula is raised. To test the anti-noise ability of this new method, different degrees of random noise are added to the original data. The inversion results show that the new El inversion method has strong anti-noise ability. Even with the random noise up to 10% of the seismic angle gathers data, this new method can still achieve the reasonable elastic parameters. Besides, unlike the conventional El linear inversion methods, the new nonlinear inversion method does not depend on the initial model, does not depend on the interpretation of horizon.
The final part of this paper is the application of new EI nonlinear inversion of prestack data in Shengli Oilfield. Although the goal layer buries deeper and AVO anomaly is not obvious, which make the reservoir prediction very difficult, the results of new EI inversion method raised in this paper still reflect the AVO anomaly. The well logging data interpretation also verifies the correctness of this method, which means that the new EI nonlinear inversion method has good potential and application prospects for the reservior exploration.
但目前对于弹性阻抗的研究多集中于新的弹性阻抗公式的推导及实际应用中弹性阻抗的定性分析,而对于影响弹性阻抗公式精度的因素以及弹性阻抗反演的定量计算还缺乏系统的研究和讨论。本文介绍了弹性阻抗的基本原理及目前常见的11种PP波弹性阻抗公式,按照是否引入射线参数将其分为两大类,并根据不同模型的正演模拟,详细探讨了影响第一类弹性阻抗公式精度的主要因素：K值、入射角和褶积模型假设。同时,根据不同模型的正演模拟,对两大类弹性阻抗公式的精度进行了详细对比,为选取合适的弹性阻抗公式,有效进行弹性阻抗反演提供理论依据。
关于弹性阻抗的反演方法,由于弹性阻抗概念简单,其反射系数表达形式与声阻抗类似,因此目前弹性阻抗的反演方法也采用与叠后声阻抗反演类似的线性方法或广义线性反演方法,不可避免地造成了其精度低、强烈依赖于初始模型、易陷入局部极优等缺点。为克服这些缺点,本文首次将非线性反演的思想引入弹性阻抗反演过程中,介绍了两种新型的非线性反演方法——蚁群算法和粒子群算法,并成功应用于弹性阻抗反演。
在对蚁群算法的原理、研究现状深入研究的基础上,通过引入反S函数和混沌算子,对基本蚁群算法加以改进,提高了其搜索效率和搜索精度,用四个Benchmark函数测试了改进后的蚁群算法的性能,并将改进后的蚁群算法应用到模型数据的弹性阻抗反演过程中,这是蚁群算法在地震叠前反演的首次应用,而且取得了良好的反演效果。
对粒子群算法的基本原理、参数选择问题及研究现状也进行了探讨,通过引入离散搜索算子和模拟退火算子,大大增强了基本粒子群的全局搜索能力,提高了搜索精度,同样用四个Benchmark函数测试了其性能,并利用改进的混合粒子群算法进行了弹性阻抗反演,效果良好。这同样也是粒子群算法在地震叠前反演领域的首次应用。
基于蚁群算法和粒子群算法以及两项EI-Fatti弹性阻抗公式,本文提出了一种新的弹性阻抗反演新策略,并利用不同程度的随机噪声对弹性阻抗反演进行了抗噪实验,以检验新策略的抗噪能力。模型数据的正反演结果表明,本文提出的弹性阻抗反演新方法和新策略抗噪能力强,对于随机噪声达到10%的角道集数据,仍能反演出合理的弹性参数。而且不依赖于初始模型,不依赖于解释层位,克服了常规弹性阻抗反演强烈依赖初始模型,易陷入局部极优的缺点,是一种切实可行的弹性阻抗反演新方法。
最后利用本文提出的弹性阻抗反演新策略对胜利油田郭局子洼陷沙二段油藏的叠前角道集数据进行了弹性阻抗非线性反演,虽然目的层埋藏较深,AVO特征也不明显,储层预测难度很大,但利用本文提出的弹性阻抗非线性反演新方法得到的属性剖面仍然能够反映出这一AVO异常,与测井资料解释成果相符,验证了本文方法的正确性,说明本文提出的弹性阻抗非线性反演新方法和新策略具有良好的发展潜力和应用前景。
The concept of elastic impedance (El) was formally proposed by Connolly in 1999, and then elastic impedance inversion spread quickly all over the world. Elastic impedance is a combination of acoustic impedance (AI) and AVO. As compared with AI, El overcomes the shortcomings of vertical incidence assumption and losing valid information sensitive to the oil and gas, and can make full use of a variety of logging curves, which makes it more comprehensive and more intuitive to distinguish the oil and gas information and can effectively reduce multi-solution of AI inversion. El can be used for not only stratigraphic inversion, but also for reservoir characteristics inversion. As compared with AVO technique, El overcomes the shotcoming of wavelet not changing with the offset in AVO inversion, and is a more robust inversion method. Elastic impedance inversion has become one of the development direction of seismic prestack inversion. Now the seismic inversion is moving towards the development of AI and El combination, AI and AVO combination.
The studies on elastic impedance are mainly focused on the new elastic impedance formula derivation and the qualitative analysis in practical application, otherwise, there is a lack of systematic study and discussion for the accuracy of El formula and the quantitative calculation of El inversion. This paper discusses the basic principles of El and current eleven PP-wave El formula. According to the introduction of ray-path parameter, these El formula can be divided into two categories. With the forward modeling of different models, the main factors, such as K value, incident angle and the convolution model assumptions impacting on the accuracy of first Class El formula are discussed. Meanwhile, according to different forward modeling result, the accuracy of the two major categories of El formula are compared. These analysis will effectively provide a theoretical basis for the next El inversion.
With regard to El inversion method, because the concept of El is very simple and its reflection coefficient expression is similar to AI's, current El inversion method also uses the linear method or the generalized linear inversion method, just like the post-stack inversion of AI. The linear method has inevitably led to its low accuracy, strong dependence on the initial model, easily falling into a local defects. To overcome these shortcomings, this paper introduces non-linear methods into El inversion process, which are two new non-linear inversion method-ant colony algorithm (ACA) and particle swarm optimization (PSO).
The principle and status quo of ant colony algorithm are presented in this paper. Through the introduction of anti-S function and chaotic operator, a new hydrid ACA is raised, which improves the efficiency and precision of search. Four benchmark functions are used to test the performance of improved ant colony algorithm, and finally the improved ant colony algorithm is applied to the El inversion process of forward modeling data, which has achieved good results.
After introducing the basic principle, parameter selection question and research status of particle swarm optimization algorithm, a new hydrid PSO is raised, which greatly enhances the global search ability and search accuracy of basic PSO by introducing of simulated annealing operator into discrete particle swarm optimization algorithm. The same four benchmark functions test its performance, and then the improved hybrid particle swarm algorithm is used in El inversion, and achieved good results.
After the above discusses, a new El nonlinear inversion method based on ant colony algorithm and particle swarm algorithm with EI-Fatti formula is raised. To test the anti-noise ability of this new method, different degrees of random noise are added to the original data. The inversion results show that the new El inversion method has strong anti-noise ability. Even with the random noise up to 10% of the seismic angle gathers data, this new method can still achieve the reasonable elastic parameters. Besides, unlike the conventional El linear inversion methods, the new nonlinear inversion method does not depend on the initial model, does not depend on the interpretation of horizon.
The final part of this paper is the application of new EI nonlinear inversion of prestack data in Shengli Oilfield. Although the goal layer buries deeper and AVO anomaly is not obvious, which make the reservoir prediction very difficult, the results of new EI inversion method raised in this paper still reflect the AVO anomaly. The well logging data interpretation also verifies the correctness of this method, which means that the new EI nonlinear inversion method has good potential and application prospects for the reservior exploration.
引文
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