重震联合界面反演方法研究
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摘要
深层勘探目标中,潜山和砂砾岩体油气藏是今后勘探的主要方向之一。但由于深层地震地质条件较差,构造复杂,导致了深层地震资料品质总体上比较差,难以搞清深层的构造和圈闭特征,从而影响了潜山面貌识别和构造落实。为了能够准确落实地震难以落实的局部构造,需要充分发挥其它物探资料的重要辅助作用,开展多学科、全信息综合研究工作。实践证明,重震联合识别古潜山等深层目标是一种较为有效的技术手段,因此本文将重震联合界面反演作为研究的主要内容。
针对以往重震联合反演研究没有反演界面的问题,对密度、速度和界面深度建立概率模型,提出了一种基于贝叶斯理论的重震联合界面反演的目标函数,从所提目标函数出发详细推导了重震联合反演界面深度、重震联合反演速度和密度、重震联合反演速度密度和界面深度的反演公式,形成了一套完整的重震联合随机反演理论框架。为了实现所提出的随机反演理论,引入了随机近似最大期望(SAEM)算法,在反演的每次迭代后,自动修改各统计参数,加快了计算速度,提高了反演精度和稳定性。
重震联合反演中的一个核心问题是如何确定速度和密度之间的关系,本文假设速度和密度之间存在未知线性关系,这个线性关系在模型的不同区域可以是不同的,通过SAEM算法根据每次迭代的密度更新量和速度更新量对速度密度关系系数B和它的方差进行修正,即可以利用先验的速度-密度关系信息,又克服了传统联合反演方法中固定的速度-密度关系不能真实合理的反映不同区域速度-密度关系的缺陷,即能实现传统顺序、剥层联合反演方法的功能,又比它们更具优越性,即能反演界面,同时又能反演属性,从而实现真正意义上的重震联合同步反演。
最后通过模型试算验证了方法的正确性和有效性,并给出了参数的选取原则,通过对济阳坳陷南部的实际数据进行重震联合反演验证了方法的实用性。
Deep exploration target, the hill and glutenite oil and gas reservoirs are one of the main directions of exploration. However, due to poor geological conditions of deep seismic, tectonic complex, led to deep seismic data on the quality of the overall relatively poor, it is difficult to understand the underlying characteristics of the structure and traps, thus affecting the hill looks to identify and implement the structure. In order to implement accurately the implementation of local seismic construction, necessary to give full play of other important geophysical data supporting the role of multidisciplinary, comprehensive study of the whole information. Practice has proved that the seismic and gravity joint identify deep-buried hill is a more effective technical mean. This article will be gravity-seismic joint inversion interface of the main contents of the study.
Aiming at the absent of interface inversion, the probability model of density, velocity and interface depth is set up, an objective function of seismic-gravity jointing interface inversion based on the Bayesian theory is proposed, from the proposed objective function seismic-gravity jointing inversion interface depth, seismic-gravity jointing inversion velocity and density, seismic-gravity jointing inversion of velocity, density and interface depth formula are derived to form a complete set of stochastic seismic-gravity joint inversion scheme. In order to achieve the stochastic inversion scheme, the stochastic approximation expectation maximum (SAEM) algorithm is introduced, the inversion at each iteration, the automatic modification of the statistical parameters to speed up the computing speed, improves accuracy and stability of the inversion.
The core problem of seismic-gravity joint inversion is how to determine the speed and density of the relationship between velocity and density which is the contact link gravity and seismic data, determining a reasonable velocity-density relationship; we assume there is a unknown linear relationship between velocity and density which may vary with depth, e.g. due to temperature variations.
In this paper, SAEM algorithm modifies the B coefficient of velocity and density relationship and its covariance, according to the amendments of velocity and density at each iteration. that is, the relationship a priori between velocity and density can be used, but also to overcome the defect of the traditional joint inversion in fixed velocity - density relationship that is not a reasonable reflection of the truth of the different regions, which can achieve the traditional sequential inversion and striped layers inversion, more than their superiority, that is able to interface inversion while inversion property, in order to achieve the true sense of the simultaneous joint inversion of seismic data and gravity data.
Finally, through model tests to verify the correctness and effectiveness, and give the the principle to choose parameters. The actual data of southern Jiyang depression are used to verify the practicality of the method.
针对以往重震联合反演研究没有反演界面的问题,对密度、速度和界面深度建立概率模型,提出了一种基于贝叶斯理论的重震联合界面反演的目标函数,从所提目标函数出发详细推导了重震联合反演界面深度、重震联合反演速度和密度、重震联合反演速度密度和界面深度的反演公式,形成了一套完整的重震联合随机反演理论框架。为了实现所提出的随机反演理论,引入了随机近似最大期望(SAEM)算法,在反演的每次迭代后,自动修改各统计参数,加快了计算速度,提高了反演精度和稳定性。
重震联合反演中的一个核心问题是如何确定速度和密度之间的关系,本文假设速度和密度之间存在未知线性关系,这个线性关系在模型的不同区域可以是不同的,通过SAEM算法根据每次迭代的密度更新量和速度更新量对速度密度关系系数B和它的方差进行修正,即可以利用先验的速度-密度关系信息,又克服了传统联合反演方法中固定的速度-密度关系不能真实合理的反映不同区域速度-密度关系的缺陷,即能实现传统顺序、剥层联合反演方法的功能,又比它们更具优越性,即能反演界面,同时又能反演属性,从而实现真正意义上的重震联合同步反演。
最后通过模型试算验证了方法的正确性和有效性,并给出了参数的选取原则,通过对济阳坳陷南部的实际数据进行重震联合反演验证了方法的实用性。
Deep exploration target, the hill and glutenite oil and gas reservoirs are one of the main directions of exploration. However, due to poor geological conditions of deep seismic, tectonic complex, led to deep seismic data on the quality of the overall relatively poor, it is difficult to understand the underlying characteristics of the structure and traps, thus affecting the hill looks to identify and implement the structure. In order to implement accurately the implementation of local seismic construction, necessary to give full play of other important geophysical data supporting the role of multidisciplinary, comprehensive study of the whole information. Practice has proved that the seismic and gravity joint identify deep-buried hill is a more effective technical mean. This article will be gravity-seismic joint inversion interface of the main contents of the study.
Aiming at the absent of interface inversion, the probability model of density, velocity and interface depth is set up, an objective function of seismic-gravity jointing interface inversion based on the Bayesian theory is proposed, from the proposed objective function seismic-gravity jointing inversion interface depth, seismic-gravity jointing inversion velocity and density, seismic-gravity jointing inversion of velocity, density and interface depth formula are derived to form a complete set of stochastic seismic-gravity joint inversion scheme. In order to achieve the stochastic inversion scheme, the stochastic approximation expectation maximum (SAEM) algorithm is introduced, the inversion at each iteration, the automatic modification of the statistical parameters to speed up the computing speed, improves accuracy and stability of the inversion.
The core problem of seismic-gravity joint inversion is how to determine the speed and density of the relationship between velocity and density which is the contact link gravity and seismic data, determining a reasonable velocity-density relationship; we assume there is a unknown linear relationship between velocity and density which may vary with depth, e.g. due to temperature variations.
In this paper, SAEM algorithm modifies the B coefficient of velocity and density relationship and its covariance, according to the amendments of velocity and density at each iteration. that is, the relationship a priori between velocity and density can be used, but also to overcome the defect of the traditional joint inversion in fixed velocity - density relationship that is not a reasonable reflection of the truth of the different regions, which can achieve the traditional sequential inversion and striped layers inversion, more than their superiority, that is able to interface inversion while inversion property, in order to achieve the true sense of the simultaneous joint inversion of seismic data and gravity data.
Finally, through model tests to verify the correctness and effectiveness, and give the the principle to choose parameters. The actual data of southern Jiyang depression are used to verify the practicality of the method.
引文
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