Study on seismic stochastic inversion method based on characteristic parameters of inhomogeneous media
WANG Baoli1,2, LIN Ying1, ZHANG Guangzhi1,2, YIN Xingyao1,2
1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China
Abstract：The underground media generally have heterogeneous characteristics, which means the elastic, petrophysical, and fluid parameters that characterize the oil and gas reservoirs are spatially inhomogeneous. The conventional seismic stochastic inversion mainly uses the variogram function obtained from well log data to characterize the spatial structure of underground formations, and it is difficult to effectively describe the spatial variation characteristics of underground complex heterogeneous reservoirs. Therefore, this paper first describes the characteristics of the spatial disturbance caused by different inhomogeneous media characteristic parameters to the media. Then, guided by the Bayesian theory, this paper makes use of the underground formation information contained in the known well log data and seismic data and proposes the stochastic inversion method based on the characteristic parameters of inhomogeneous media. This method integrates the given well log data and seismic data and estimates the characteristic parameters of inhomogeneous media which can better describe the spatial structure characteristics of underground reservoirs based on random medium theory. Next, these parameters are utilized to build a prior information model required for the subsequent inversion process. Finally, a very fast quantum annealing algorithm is adopted to optimize the objective function, and the stochastic inversion results are obtained. The model test shows that the prior model of heterogeneous characteristic parameters can describe the heterogeneous characteristics of the reservoirs and provide reliable geostatistical prior information for subsequent inversion. The case analysis further shows that this method can better achieve high-resolution inversion of complex underground reservoirs and obtain more reliable inversion results.
Bosch M, Mukerji T, Gonzalez E F.Seismic inversion for reservoir properties combining statistical rock physics and geostatistics:a review[J]. Geophysics, 2010, 75(5):A165-A176.
Francis A. Limitations of deterministic seismic inversion data as input for reservoir model conditioning[C].SEG Technical Program Expanded Abstracts, 2010, 29:2396-2400.
[4]
Francis A.Limitations of deterministic and advantages of stochastic seismic inversion[J]. CSEG Recorder, 2005, 30(2):5-11.
[5]
Sams M.Comparison of lithology and net pay uncertainty between deterministic and geostatistical inversion workflows[J]. First Break, 2010, 28(2):35-44.
Fokker P A, Borello E S, Serazio C, et al.Estimating reservoir heterogeneities from pulse testing[J]. Journal of Petroleum Science and Engineering, 2012, doi:10.1016/j.petrol.2012.03.017.
[8]
陈欢庆, 王珏, 杜宜静.储层非均质性研究方法进展[J]. 高校地质学报, 2017, 23(1):104-116.CHEN Huanqing, WANG Jue, DU Yijing.Advances of research methods on reservoir heterogeneity[J]. Geological Journal of China Universities, 2017, 23(1):104-116.
[9]
Ikelle LT, Yung S K, and Daube F.2-D random media with ellipsoidal autocorrelation functions[J]. Geophysics, 1993, 58(9):1359-1372.
[10]
奚先, 姚姚.随机介质模型的模拟与混合型随机介质[J]. 地球科学——中国地质大学学报, 2002, 27(1):67-71.XI Xian, YAO Yao.Simulations of random medium model and interimxed random medium[J]. Earth Science-Journal of China University of Geosciences, 2002, 27(1):67-71.
[11]
顾元, 朱培民, 李辉, 等.二维叠后地震数据的平稳随机介质参数估计[J]. 地球物理学报, 2014, 57(7):2291-2301.GU Yuan, ZHU Peimin, LI Hui, et al.Estimation of 2D stationary random medium parameters from post-stack seismic data[J]. Chinese Journal of Geophysics, 2014, 57(7):2291-2301.
[12]
奚先, 姚姚.非平稳随机介质模型[J]. 石油地球物理勘探, 2005, 40(1):71-75.XI Xian, YAO Yao.Non-stationary random medium model[J]. Oil Geophysical Prospecting, 2005, 40(1):71-75.
纪永祯, 张渝悦, 朱立华, 等.多道随机稀疏反射系数反演[J]. 石油物探, 2020, 59(6):912-917.JI Yongzhen, ZHANG Yuyue, ZHU Lihua, et al.Multi-trace stochastic sparse-spike inversion for reflectivity[J]. Geophysical Prospecting for Petroleum, 2020, 59(6):912-917.
[15]
Debeye H W, Sabbah E, Made P M. Stochastic inversion[C].SEG Technical Program Expanded Abstracts, 1996, 15:1212-1215.
[16]
Yin X Y, Sun R Y, Wang B L, et al.Simultaneous inversion of petrophysical parameters based on geostatistical a priori information[J]. Applied Geophysics, 2014, 11(3):321-330.
[17]
王保丽, 印兴耀, 丁龙翔, 等.基于FFT-MA谱模拟的快速随机反演方法研究[J]. 地球物理学报, 2015, 58(2):664-673.WANG Baoli, YIN Xingyao, DING Longxiang, et al.Study of fast stochastic inversion based on FFT-MA spectrum simulation[J]. Chinese Journal of Geophy-sics, 2015, 58(2):664-673.
[18]
孙瑞莹, 印兴耀, 王保丽, 等.基于随机地震反演的Russell流体因子直接估算方法[J]. 地球物理学报, 2016, 59(3):1143-1150.SUN Ruiying, YIN Xingyao, WANG Baoli, et al.A direct estimation method for the Russell fluid factor based on stochastic seismic inversion[J]. Chinese Journal of Geophysics, 2016, 59(3):1143-1150.
[19]
王保丽, 孙瑞莹, 印兴耀, 等.基于Metropolis抽样的非线性反演方法[J]. 石油地球物理勘探, 2015, 50(1):111-117.WANG Baoli, SUN Ruiying, YIN Xingyao, et al.Nonlinear inversion based on Metropolis sampling algorithm[J]. Oil Geophysical Prospecting, 2015, 50(1):111-117.
[20]
郭同翠, 姜明军, 纪迎章, 等.叠前地质统计学反演在页岩甜点和薄夹层预测中的应用——以西加拿大盆地W区块为例[J]. 石油地球物理勘探, 2020, 55(1):167-175.GUO Tongcui, JIANG Mingjun, JI Yingzhang, et al.The application of prestack geostatistical inversion in the prediction of shale sweet spots and thin interbeds:a case study of Block W in Western Canada Basin[J]. Oil Geophysical Prospecting, 2020, 55(1):167-175.
[21]
Azevedo L, Nunes R. Geostatistical seismic AVO inversion directly for facies:real case application[C].Extended Abstracts of 77th EAGE Conference & Exhibition, 2015, 423-426.
[22]
De Figueiredo L P, Grana D, Bordignon F, et al.Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties[J]. Geophysics, 2018, 83(5):M49-M61.
[23]
De Figueiredo L P, Grana D, Santos M, et al.Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies[J]. Journal of Computational Physics, 2017, 336(5):128-142.
[24]
Mohamed I A, John P, Mohamed S E, et al.Integrated seismic attributes and stochastic inversion for reservoir characterization:Insights from Wadi field[J]. Journal of African Earth Sciences, 2020, doi:10.1016/j.jafrearsci.2019.103661.
Pereira P, Azevedo L, Nunes R.Integrating of initial guess models into geostatistical seismic inversion methodologies[C]. SEG Technical Program Expanded Abstracts, 2016, 35:326-330.
[27]
Erasmus K O, James I, and Thomas H.Basis-constrained Bayesian Markov-chain Monte Carlo diffe-rence inversion for geoelectrical monitoring of hydrogeologic processes[J]. Geophysics, 2019, 84(4):M37-M42.
[28]
Heidi K, Ragnar H, Odd K.Bayesian Monte Carlo method for seismic predrill prospect assessment[J]. Geophysics, 2010, 75(2):O9-O19.
[29]
Grana D.Bayesian petroelastic inversion with multiple prior models[J]. Geophysics, 2020, 85(5):M57-M71.
[30]
Francis A.Understanding stochastic inversion:Part 1[J]. First Break, 2006, 24(11):69-77.
[31]
Francis A.Understanding stochastic inversion:Part 2[J]. First Break, 2006, 24(12):79-84.
[32]
Tarantola A.Inverse Problem Theory and Methods for Model Parameter Estimation[M].SIAM, Paris, 2005.
