基于曲波变换和贝叶斯理论的储层预测方法研究
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摘要
随着油气需求的不断扩大和地震勘探技术的不断提高,油气勘探与开发领域越来越复杂(如复杂裂缝、裂隙型、隐蔽型和深层油气藏等),对储层预测的要求就越来越高,因此,需要更有效的预测储层的方法和技术。本论文首先应用基于曲波变换的一套方法预测裂缝发育带的强度和裂缝发育带展布方向,寻找有利的油气聚集区。然后应用基于贝叶斯理论的方法,从概率分析的角度对得到的大量属性进行优化,主要以叠后属性分析的手法预测油气储层及其范围。最后,研究了基于叠前资料进行流体识别的新方法和新技术,以区分储层内流体的性质。概括来说,本文的思路就是:有利油气聚集区的预测——油气储层及其范围的预测——储层含流体性质的识别。从储层预测的源头开始,应用层层递进的方法实现储层范围及其流体性质的预测。
裂缝在地层中是普遍存在的,对油气运移和储集具有非常重要的作用。本文通过对储层裂缝进行深入的研究,提出了预测裂缝发育强度及其走向的边缘保存锐化算法、基于曲波变换的多尺度、多方向和多谱体曲率分析技术。首先,针对近断层/断裂处的地震反射数据比较复杂,并且含有比较强的噪音的特征,以保边去噪技术(EPS)和锐化滤波器(LUM)为基础,构建了边缘保存锐化滤波器,通过参数的调整实现噪音的衰减和微小线性特征的保存。随后,将曲波(Curvelet)变换分别和相干技术、曲率分析相结合,提出了多尺度、多方向的相干体技术和多谱体曲率分析技术,利用曲波变换的多尺度和多方向特性,实现不同尺度裂缝带及其走向的预测。多尺度、多方向的相干体方法在曲波域中给出不同的重构系数,得到突出不同频带和不同方向的地震数据体,然后再利用相干算法分别得到多尺度、多方向的相干体。多谱体曲率分析方法利用曲波变换分离不同波长的波动信息,并应用多谱导数算子实现尺度系数的微调,然后将与所考虑问题的尺度相匹配的波动信息进行重构,得到突出不同波数特征的曲率数据体。上述三种方法相结合,分别从波形和构造两个方面对裂缝发育强度和裂缝发育带的走向进行预测,提高了预测结果的可靠性。将这套方法应用到实际地震数据中,提高了数据的信噪比,保护了微小断裂信号,突出了特定频带通道的地质异常体,实现了地质目标的精细解释,为储层预测提供了一种新方法。
地震属性优化是提高地震储层预测精度的必要步骤。本文以贝叶斯理论和主成分分析为基础,研究了利用概率模型进行核主成分分析的方法——概率核主成分分析(PKPCA)。此方法能有效克服主成分分析缺少概率模型和缺失高阶统计量信息的不足。随后研究了PKPCA的混合分析模型——混合概率核主成分分析(MPKPCA),以多种概率模型的混合对数据分布进行表征。应用期望最大(EM)估计得到最佳概率模型。实际数据的应用显示基于贝叶斯的属性概率优化方法提高了属性优化的精度,同时提高了储层预测的准确性。
本文在储层预测的基础上开展流体识别方法的研究。推导了基于入射角的AVO近似方程和叠前AVO属性(G,Rs等)的修正公式。以Aki和Shuey方程为基础,通过Snell定律,研究了更加清晰地表示反射系数与上层入射角、岩石物性参数间内在关系的近似方程。新方程将反射系数表示为上层入射角的函数,而实际地震CRP道集在从偏移距转化为角度时恰好也是入射角的函数,这样理论公式与实际地震数据客观状况更加符合。以入射角AVO近似方程为基础研究了更准确提取AVO属性的方法,推导了叠前AVO属性(G,Rs等)的修正公式。利用角度部分叠加数据的优点,研究了预测储层的快速估计法、角度流体道集法和曲波域波场分离法,并将其与叠前AVO属性修正公式相结合,实现了提高储层预测准确性和可靠性的目的。对常用的流体因子间关系进行理论研究,发现了它们之间的内在联系,并根据它们的实质所在分别建立反射系数域和阻抗域的新流体因子公式。实际数据的应用结果显示本论文的方法不但可以准确地判定储层的位置及范围,还能更好地区分气、水储层。
With the demand growing of oil and gas and the improving of seismic exploration technology, the exploration and development field of oil and gas become complex (such as complex fractures type, concealment type and deep hydrocarbon reservoirs, etc.), so requirements for reservoir prediction is getting higher. Therefore, more effective methods and techniques are needed for reservoir prediction. First, this thesis researched set of Curvelet-based methods to predict the fracture strength and fracture zone strike to ook favorable oil and gas accumulation area. And then, methods based on Bayesian theory were used to optimize a large number of attributes from the perspective of probability analysis, and main aim is to predict hydrocarbon reservoir and its scope by the post-stack attribute analysis approach. At last, the new methods and technology based on prestack data were researched and were used to distinct the nature of the fluid in reservoir. In general, the idea of the thesis is prediction of favorable oil and gas accumulation zone-prediction of the range of oil and gas reservoirs-the identification of reservoir fluid properties. Starting from the source of reservoir prediction, apply the progressive methods to predict the reservoir range and the fluid properties.
Fractures and cracks are common in the strata. This paper deep studies reservoir fractures and proposes edge preserving and sharpen filter, multi-scale, multi- direction coherence technology and multi-spectral body curvature analysis based on the Curvelet transform. First, since the seismic reflection data near-fault /fractures is complex, and contains relatively strong noise, an edge preserving and sharpen filter is proposed based on edge preserving smooth (EPS) techniques and the lower-upper-middle (LUM) filters. This filter preserves small linear features, while removing noise by adjusting the parameters. After that,combining Curvelet transform with coherence and curvature technology, we develop a new and effective multiscale, multidirectional coherence cube and multi-spectral volumetric curvature method of predicting different scale fracture zones and their strike. Multiscale and multidirectional coherence cube methods gives different reconstruction coefficient in Curvelet domain, gains seismic data that bursts characteristic of the different frequency bands and different directions, and then obtains multi-scale and multi-directional coherence at last. By applying Curvelet transform to separate different wavelengths wave motion information and using multi-spectral derivative operator to finely tune scale coefficients, multi-spectral volumetric curvature method reconstructs wave motion information matching with the scale of target reservoir and gains seismic data that bursts characteristic of the different wave-number. The three methods predict the fracture strength and fracture zone strike from the waveform and structure, respective and improve the reliability of prediction results. Real data were used to test the sets of methods. The results showed they can improve the signal to noise ratio (SNR) of data, protect small fracture signals and burst seismic data of the different frequency bands and different directions and better represent complex and variable geologic body details. These methods provide a new way to predict lithology.
In reservoir prediction, seismic attribute analysis has been an important way to obtain reservoir parameters. According to analysis results, accurate reservoir information can be got. Based on Bayesian theory and principal component analysis, PKPCA method was proposed. This method use probability model constraints kernel principal component analysis and overcome the two shortcomings of principal component analysis that lack probabilistic model and higher order statistics information. Then, a mix model of PKPCA was developed-mix probabilistic principal component analysis (MPKPCA). It mixed a variety of probability models to characterize the data. At last, EM algorithm is used to get the best probability model. The application of the actual data showed the attribute probability optimization method based on Bayesian theory improves the accuracy of attribute optimization, while increasing the accuracy of reservoir prediction.
Reservoir fluid recognition is one of the final targets of the seismic exploration, so we carried out the research in the method of fluid recognition based on the reservoir prediction. First, the focus of our research is the incidence angle AVO approximation equation, which is constructed based on aki, Shuey approximate equation and snell law, and expresses the inner relationship between the reflection coefficients and the upper incident and petrophysic parameters more clearly. The practical seismic CRP gathers transferring offset into angles is also the function of upper incident angles, so the theory formulas are more consistent with the objective conditions of actual seismic data. After that, based on the incident angle AVO approximation equations, we studied the more accurate AVO attribute extraction method, proposed the correction formulas of the prestack AVO attributes (G, Rs, etc.). Then, using the advantages of stack data in the angle part , this article proposes three effective methods and techniques of fluid recognition and reservoir prediction, and combining it with the prestack AVO attributes correction formula, realizes the purpose of improving reservoir prediction accuracy and reliability. Finally, after some theoretical researches on the common fluid factor relations were carried out, we discovered the inner relation between them, and respectively established the new fluid factor formula in the reflection coefficients domain and impedance domain according to their essence. The application results of actual data show that the methods in this the paper can not only be used to accurately determine the position and scope of reservoirs, but also better distinguish gas, water reservoirs.
裂缝在地层中是普遍存在的,对油气运移和储集具有非常重要的作用。本文通过对储层裂缝进行深入的研究,提出了预测裂缝发育强度及其走向的边缘保存锐化算法、基于曲波变换的多尺度、多方向和多谱体曲率分析技术。首先,针对近断层/断裂处的地震反射数据比较复杂,并且含有比较强的噪音的特征,以保边去噪技术(EPS)和锐化滤波器(LUM)为基础,构建了边缘保存锐化滤波器,通过参数的调整实现噪音的衰减和微小线性特征的保存。随后,将曲波(Curvelet)变换分别和相干技术、曲率分析相结合,提出了多尺度、多方向的相干体技术和多谱体曲率分析技术,利用曲波变换的多尺度和多方向特性,实现不同尺度裂缝带及其走向的预测。多尺度、多方向的相干体方法在曲波域中给出不同的重构系数,得到突出不同频带和不同方向的地震数据体,然后再利用相干算法分别得到多尺度、多方向的相干体。多谱体曲率分析方法利用曲波变换分离不同波长的波动信息,并应用多谱导数算子实现尺度系数的微调,然后将与所考虑问题的尺度相匹配的波动信息进行重构,得到突出不同波数特征的曲率数据体。上述三种方法相结合,分别从波形和构造两个方面对裂缝发育强度和裂缝发育带的走向进行预测,提高了预测结果的可靠性。将这套方法应用到实际地震数据中,提高了数据的信噪比,保护了微小断裂信号,突出了特定频带通道的地质异常体,实现了地质目标的精细解释,为储层预测提供了一种新方法。
地震属性优化是提高地震储层预测精度的必要步骤。本文以贝叶斯理论和主成分分析为基础,研究了利用概率模型进行核主成分分析的方法——概率核主成分分析(PKPCA)。此方法能有效克服主成分分析缺少概率模型和缺失高阶统计量信息的不足。随后研究了PKPCA的混合分析模型——混合概率核主成分分析(MPKPCA),以多种概率模型的混合对数据分布进行表征。应用期望最大(EM)估计得到最佳概率模型。实际数据的应用显示基于贝叶斯的属性概率优化方法提高了属性优化的精度,同时提高了储层预测的准确性。
本文在储层预测的基础上开展流体识别方法的研究。推导了基于入射角的AVO近似方程和叠前AVO属性(G,Rs等)的修正公式。以Aki和Shuey方程为基础,通过Snell定律,研究了更加清晰地表示反射系数与上层入射角、岩石物性参数间内在关系的近似方程。新方程将反射系数表示为上层入射角的函数,而实际地震CRP道集在从偏移距转化为角度时恰好也是入射角的函数,这样理论公式与实际地震数据客观状况更加符合。以入射角AVO近似方程为基础研究了更准确提取AVO属性的方法,推导了叠前AVO属性(G,Rs等)的修正公式。利用角度部分叠加数据的优点,研究了预测储层的快速估计法、角度流体道集法和曲波域波场分离法,并将其与叠前AVO属性修正公式相结合,实现了提高储层预测准确性和可靠性的目的。对常用的流体因子间关系进行理论研究,发现了它们之间的内在联系,并根据它们的实质所在分别建立反射系数域和阻抗域的新流体因子公式。实际数据的应用结果显示本论文的方法不但可以准确地判定储层的位置及范围,还能更好地区分气、水储层。
With the demand growing of oil and gas and the improving of seismic exploration technology, the exploration and development field of oil and gas become complex (such as complex fractures type, concealment type and deep hydrocarbon reservoirs, etc.), so requirements for reservoir prediction is getting higher. Therefore, more effective methods and techniques are needed for reservoir prediction. First, this thesis researched set of Curvelet-based methods to predict the fracture strength and fracture zone strike to ook favorable oil and gas accumulation area. And then, methods based on Bayesian theory were used to optimize a large number of attributes from the perspective of probability analysis, and main aim is to predict hydrocarbon reservoir and its scope by the post-stack attribute analysis approach. At last, the new methods and technology based on prestack data were researched and were used to distinct the nature of the fluid in reservoir. In general, the idea of the thesis is prediction of favorable oil and gas accumulation zone-prediction of the range of oil and gas reservoirs-the identification of reservoir fluid properties. Starting from the source of reservoir prediction, apply the progressive methods to predict the reservoir range and the fluid properties.
Fractures and cracks are common in the strata. This paper deep studies reservoir fractures and proposes edge preserving and sharpen filter, multi-scale, multi- direction coherence technology and multi-spectral body curvature analysis based on the Curvelet transform. First, since the seismic reflection data near-fault /fractures is complex, and contains relatively strong noise, an edge preserving and sharpen filter is proposed based on edge preserving smooth (EPS) techniques and the lower-upper-middle (LUM) filters. This filter preserves small linear features, while removing noise by adjusting the parameters. After that,combining Curvelet transform with coherence and curvature technology, we develop a new and effective multiscale, multidirectional coherence cube and multi-spectral volumetric curvature method of predicting different scale fracture zones and their strike. Multiscale and multidirectional coherence cube methods gives different reconstruction coefficient in Curvelet domain, gains seismic data that bursts characteristic of the different frequency bands and different directions, and then obtains multi-scale and multi-directional coherence at last. By applying Curvelet transform to separate different wavelengths wave motion information and using multi-spectral derivative operator to finely tune scale coefficients, multi-spectral volumetric curvature method reconstructs wave motion information matching with the scale of target reservoir and gains seismic data that bursts characteristic of the different wave-number. The three methods predict the fracture strength and fracture zone strike from the waveform and structure, respective and improve the reliability of prediction results. Real data were used to test the sets of methods. The results showed they can improve the signal to noise ratio (SNR) of data, protect small fracture signals and burst seismic data of the different frequency bands and different directions and better represent complex and variable geologic body details. These methods provide a new way to predict lithology.
In reservoir prediction, seismic attribute analysis has been an important way to obtain reservoir parameters. According to analysis results, accurate reservoir information can be got. Based on Bayesian theory and principal component analysis, PKPCA method was proposed. This method use probability model constraints kernel principal component analysis and overcome the two shortcomings of principal component analysis that lack probabilistic model and higher order statistics information. Then, a mix model of PKPCA was developed-mix probabilistic principal component analysis (MPKPCA). It mixed a variety of probability models to characterize the data. At last, EM algorithm is used to get the best probability model. The application of the actual data showed the attribute probability optimization method based on Bayesian theory improves the accuracy of attribute optimization, while increasing the accuracy of reservoir prediction.
Reservoir fluid recognition is one of the final targets of the seismic exploration, so we carried out the research in the method of fluid recognition based on the reservoir prediction. First, the focus of our research is the incidence angle AVO approximation equation, which is constructed based on aki, Shuey approximate equation and snell law, and expresses the inner relationship between the reflection coefficients and the upper incident and petrophysic parameters more clearly. The practical seismic CRP gathers transferring offset into angles is also the function of upper incident angles, so the theory formulas are more consistent with the objective conditions of actual seismic data. After that, based on the incident angle AVO approximation equations, we studied the more accurate AVO attribute extraction method, proposed the correction formulas of the prestack AVO attributes (G, Rs, etc.). Then, using the advantages of stack data in the angle part , this article proposes three effective methods and techniques of fluid recognition and reservoir prediction, and combining it with the prestack AVO attributes correction formula, realizes the purpose of improving reservoir prediction accuracy and reliability. Finally, after some theoretical researches on the common fluid factor relations were carried out, we discovered the inner relation between them, and respectively established the new fluid factor formula in the reflection coefficients domain and impedance domain according to their essence. The application results of actual data show that the methods in this the paper can not only be used to accurately determine the position and scope of reservoirs, but also better distinguish gas, water reservoirs.
引文
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