摘要
地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高斯模拟。在贝叶斯框架下,结合地震数据的约束,利用马尔科夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法对模拟结果进行随机扰动和全局寻优,获得优化的参数反演结果。模型测试结果表明,基于地震波形约束的初始模型较为精确地刻画了地下储层的空间结构。对其进行迭代优化可以加快马尔科夫链的收敛速度,有效提高反演结果的精度。本文将提出的地质统计学反演方法用于某油田实际地震数据,在随机模拟过程和目标函数的约束中,充分挖掘了地震波形蕴含的地质信息,并为实现多数据联合约束地震反演提供了理论依据。
Seismic stochastic inversion method has received much attention because of its considerable advantage of having higher vertical resolution than deterministic inversions.However,due to the lack of cross-well data,the inversion results typically exhibit poor lateral continuity.Furthermore,the inversion efficiency is low,and the inversion result is highly random.Therefore,this study proposes a geostatistical seismic inversion method constrained by a seismic waveform.The correlation coefficient of seismic data is used to measure the similarity of the seismic waveforms,replacing the traditional variogram for sequential Gaussian simulation.Under the Bayesian framework,the Monte Carlo-Markov Chain(MCMC)algorithm is combined with the constraints of seismic data to randomly perturb and optimize the simulation results for obtaining the optimized parameter inversion results.The model data tests show that the initial model based on seismic waveform constraints can accurately describe the spatial structure of the subsurface reservoir.In addition,perturbing and optimizing initial model can increase the convergence speed of the Markov chain and effectively improve the accuracy of the inversion results.In this paper,the proposed geostatistical inversion method is applied to the actual seismic data of an oil field.Under the constraints of the stochastic simulation process and objective function,the geological information contained in the seismic waveforms is fully mined,and a theoretical foundation is provided for realizing the multidata joint-constrained seismic inversion.
作者
倪雪彬
张佳佳
陈康
张广智
王保丽
刘卓凡
蔺营
Ni Xue-Bin;Zhang Jia-Jia;Chen Kang;Zhang Guang-Zhi;Wang Bao-Li;Liu Zhuo-Fan;Lin Ying(School of Geosciences,China University of Petroleum(East China),Qingdao 266580,China;Research Institute of Exploration and Development,PetroChina Southwest Oil and Gas Field Company,Chengdu 610041,China)
基金
supported by the National Natural Science Foundation of China[Grant Nos.42174146,42074136,42174144]
Innovation Fund Project for Graduate Students of China University of Petroleum(East China)[Grant No.23CX04015A].
关键词
地震波形相关系数
序贯高斯模拟
初始模型
MCMC算法
seismic waveform correlation coefficient
sequential Gaussian simulation
initial model
Monte Carlo-Markov Chain algorithm
