基于高分辨率反演谱分解的储层流体流度计算方法研究
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摘要
反射地震数据中的低频信息包含了与储层及流体有关的丰富信息,从地震数据中提取储层流体流度属性可以为利用地震低频信息进行储层预测和流体识别提供一种新的途径。为此,研究并提出了基于高分辨率稀疏反演谱分解的储层流体流度计算方法。首先基于Biot孔隙介质依赖频率的反射系数低频渐近分析理论,推导出了储层流体流度属性的计算表达式;然后利用地震数据低频段优势频率的瞬时谱振幅代替相应频率处的反射系数,给出了储层流体流度属性的直接近似计算方法,其中关于瞬时谱的计算采用了基于交替方向算法的高分辨率稀疏反演谱分解方法,该方法相对于常规谱分解方法具有更高的时间分辨率和频率分辨率。陆上和海上二维叠后地震资料的试处理结果表明,基于高分辨率稀疏反演谱分解的储层流体流度计算方法得到的储层流体流度属性剖面分辨率非常高,对于含油气储层显示了良好的成像能力,降低了储层流体识别的多解性和不确定性。
The low-frequency information of seismic reflection data contains abundant information related to reservoir and fluid.Extracting reservoir fluid mobility property from seismic data provides a new means for reservoir prediction and fluid recognition.Therefore,we study and propose a calculation method of reservoir fluid mobility based on high resolution inversion spectral decomposition.Firstly,the computation formula for reservoir fluid mobility is deduced based on asymptotic analysis theory of frequency-dependent reflection coefficient in saturate porous medium of Biot model.Then,the instantaneous spectral amplitude of the dominant frequency at low frequency replaces its reflection coefficient,and the direct approximate calculation method for reservoir fluid mobility is derived.It is emphasized that the related instantaneous spectrum is calculated by using the high resolution sparse inversion spectral decomposition based on alternating direction algorithm,which has higher timeresolution and frequency-resolution than the conventional spectral decomposition method.Finally,the reservoir fluid mobility calculation method is applied to process the land and marine post-stack seismic data.The actual data processing results show that the reservoir fluid mobility section based on high-resolution sparse inversion spectral decomposition has high-resolution and well imaging capability to oil and gas reservoir,which reduces the multi-solutions and uncertainty of reservoir fluid identification.
The low-frequency information of seismic reflection data contains abundant information related to reservoir and fluid.Extracting reservoir fluid mobility property from seismic data provides a new means for reservoir prediction and fluid recognition.Therefore,we study and propose a calculation method of reservoir fluid mobility based on high resolution inversion spectral decomposition.Firstly,the computation formula for reservoir fluid mobility is deduced based on asymptotic analysis theory of frequency-dependent reflection coefficient in saturate porous medium of Biot model.Then,the instantaneous spectral amplitude of the dominant frequency at low frequency replaces its reflection coefficient,and the direct approximate calculation method for reservoir fluid mobility is derived.It is emphasized that the related instantaneous spectrum is calculated by using the high resolution sparse inversion spectral decomposition based on alternating direction algorithm,which has higher timeresolution and frequency-resolution than the conventional spectral decomposition method.Finally,the reservoir fluid mobility calculation method is applied to process the land and marine post-stack seismic data.The actual data processing results show that the reservoir fluid mobility section based on high-resolution sparse inversion spectral decomposition has high-resolution and well imaging capability to oil and gas reservoir,which reduces the multi-solutions and uncertainty of reservoir fluid identification.
引文
[1]Silin D B,Korneev V A,Goloshubin G M,et al.A hydrologic view on Biot's theory of poroelasticity[R].Berkeley:Lawrence Berkeley National Laboratory,2004:1-23
[2]Silin D B,Korneev V A,Goloshubin G M,et al.Low-frequency asymptotic analysis of seismic reflection from a fluid-saturated medium[J].Transport in Porous Media,2006,62(3):283-305
[3]Silin D,Goloshubin G.Seismic wave reflection from apermeable layer:low-frequency asymptotic analysis[C]∥ASME 2008International Mechanical Engineering Congress and Exposition.Boston:American Society of Mechanical Engineers,2008:47-56
[4]Silin D.A low-frequency asymptotic model of seismic reflection from a high-permeability layer[R].Berkeley:Lawrence Berkeley National Laboratory,2009:1-28
[5]Silin D,Goloshubin G.An asymptotic model of seismic reflection from a permeable layer[J].Transport in Porous Media,2010,83(1):233-256
[6]Batzle M L,Han D H,Hofmann R.Fluid mobility and frequency-dependent seismic velocity-direct measurements[J].Geophysics,2006,71(1):N1-N9
[7]Korneev V A,Silin D,Goloshubin G M,et al.Seismic imaging of oil production rate[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004,1476-1479
[8]Goloshubin G,van Schuyver C,Korneev V,et al.Reservoir imaging using low frequencies of seismic reflections[J].The Leading Edge,2006,25(5):527-531
[9]Goloshubin G,Silin D,Vingalov V,et al.Reservoir permeability from seismic attribute analysis[J].The Leading Edge,2008,27(3):376-381
[10]Goloshubin G,Chabyshova E.A possible explanation of low frequency shadows beneath gas reservoirs[J].Expanded Abstracts of 82nd Annual Internat SEG Mtg,2012,1-5
[11]蔡涵鹏.基于地震资料低频信息的储层流体识别[D].成都:成都理工大学,2012Cai H P.Reservoir fluid identification from low-frequency seismic wave[D].Chengdu:Chengdu University of Technology,2012
[12]Chen X H,He Z H,Zhu S X,et al.Seismic low-frequency-based calculation of reservoir fluid mobility and its applications[J].Applied Geophysics,2012,9(3):326-332
[13]Chen X H,He Z H,Pei X G,et al.Numerical simulation of frequency-dependent seismic response and gas reservoir delineation in turbidites:a case study from China[J].Journal of Applied Geophysics,2013,94(4):22-30
[14]韩利.高分辨率全谱分解方法研究[D].长春:吉林大学,2013Han L.Research on the methods of high-resolution full spectrum decomposition[D].Changchun:Jilin University,2013
[15]Castagna J P,Sun S,Siegfried R W.Instantaneous spectral analysis:detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127
[16]Liu J,Marfurt K J.Instantaneous spectral attributes to detect channels[J].Geophysics,2007,72(2):P23-P31
[17]Sinha S,Routh P S,Anno P D,et al.Spectral decomposition of seismic data with continuous-wavelet transform[J].Geophysics,2005,70(6):P19-P25
[18]Wang Y.Seismic time-frequency spectral decomposition by matching pursuit[J].Geophysics,2007,72(1):V13-V20
[19]Portniaguine O,Castagna J.Inverse spectral decomposition[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004,1786-1789
[20]Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61
[21]Van Den Berg E,Friedlander M P.Probing the Pareto frontier for basis pursuit solutions[J].SIAM Journal on Scientific Computing,2008,30(2):890-912
[22]Beck A,Teboulle M.A fast iterative shrinkagethresholding algorithm for linear inverse problems[J].SIAM Journal on Imaging Sciences,2009,2(1):183-202
[23]Yun S,Toh K C.A coordinate gradient descent method for L1-regularized convex minimization[J].Computational Optimization and Applications,2011,48(2):273-307
[24]Yang J F,Zhang Y.Alternating direction algorithms for L1-problems in compressive sensing[J].SIAM Journal on Scientific Computing,2011,33(1):250-278
[2]Silin D B,Korneev V A,Goloshubin G M,et al.Low-frequency asymptotic analysis of seismic reflection from a fluid-saturated medium[J].Transport in Porous Media,2006,62(3):283-305
[3]Silin D,Goloshubin G.Seismic wave reflection from apermeable layer:low-frequency asymptotic analysis[C]∥ASME 2008International Mechanical Engineering Congress and Exposition.Boston:American Society of Mechanical Engineers,2008:47-56
[4]Silin D.A low-frequency asymptotic model of seismic reflection from a high-permeability layer[R].Berkeley:Lawrence Berkeley National Laboratory,2009:1-28
[5]Silin D,Goloshubin G.An asymptotic model of seismic reflection from a permeable layer[J].Transport in Porous Media,2010,83(1):233-256
[6]Batzle M L,Han D H,Hofmann R.Fluid mobility and frequency-dependent seismic velocity-direct measurements[J].Geophysics,2006,71(1):N1-N9
[7]Korneev V A,Silin D,Goloshubin G M,et al.Seismic imaging of oil production rate[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004,1476-1479
[8]Goloshubin G,van Schuyver C,Korneev V,et al.Reservoir imaging using low frequencies of seismic reflections[J].The Leading Edge,2006,25(5):527-531
[9]Goloshubin G,Silin D,Vingalov V,et al.Reservoir permeability from seismic attribute analysis[J].The Leading Edge,2008,27(3):376-381
[10]Goloshubin G,Chabyshova E.A possible explanation of low frequency shadows beneath gas reservoirs[J].Expanded Abstracts of 82nd Annual Internat SEG Mtg,2012,1-5
[11]蔡涵鹏.基于地震资料低频信息的储层流体识别[D].成都:成都理工大学,2012Cai H P.Reservoir fluid identification from low-frequency seismic wave[D].Chengdu:Chengdu University of Technology,2012
[12]Chen X H,He Z H,Zhu S X,et al.Seismic low-frequency-based calculation of reservoir fluid mobility and its applications[J].Applied Geophysics,2012,9(3):326-332
[13]Chen X H,He Z H,Pei X G,et al.Numerical simulation of frequency-dependent seismic response and gas reservoir delineation in turbidites:a case study from China[J].Journal of Applied Geophysics,2013,94(4):22-30
[14]韩利.高分辨率全谱分解方法研究[D].长春:吉林大学,2013Han L.Research on the methods of high-resolution full spectrum decomposition[D].Changchun:Jilin University,2013
[15]Castagna J P,Sun S,Siegfried R W.Instantaneous spectral analysis:detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127
[16]Liu J,Marfurt K J.Instantaneous spectral attributes to detect channels[J].Geophysics,2007,72(2):P23-P31
[17]Sinha S,Routh P S,Anno P D,et al.Spectral decomposition of seismic data with continuous-wavelet transform[J].Geophysics,2005,70(6):P19-P25
[18]Wang Y.Seismic time-frequency spectral decomposition by matching pursuit[J].Geophysics,2007,72(1):V13-V20
[19]Portniaguine O,Castagna J.Inverse spectral decomposition[J].Expanded Abstracts of 74th Annual Internat SEG Mtg,2004,1786-1789
[20]Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61
[21]Van Den Berg E,Friedlander M P.Probing the Pareto frontier for basis pursuit solutions[J].SIAM Journal on Scientific Computing,2008,30(2):890-912
[22]Beck A,Teboulle M.A fast iterative shrinkagethresholding algorithm for linear inverse problems[J].SIAM Journal on Imaging Sciences,2009,2(1):183-202
[23]Yun S,Toh K C.A coordinate gradient descent method for L1-regularized convex minimization[J].Computational Optimization and Applications,2011,48(2):273-307
[24]Yang J F,Zhang Y.Alternating direction algorithms for L1-problems in compressive sensing[J].SIAM Journal on Scientific Computing,2011,33(1):250-278
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