The Research Of Least-squares Reverse Time Migration In Elastic Media

With the development of oil and gas exploration work,the main target of oil and gas exploration is gradually turning to small and complex medium subtle reservoirs,which puts forward higher requirements for seismic exploration technology.The type of reservoir has developed from conventional clastic and carbonate reservoirs to unconventional tight oil and shale oil reservoirs.Because reverse time migration(RTM)has not inclination limitation when imaging complex structures,it is considered as a better imaging method,which can simulate the propagation process of seismic waves more accurately and adapt to drastic underground velocity changes.However,reverse time migration has some problems.For example,the imaging results have low-frequency noises,and it is not able to provide accurate amplitude information and high resolution.Least squares inverse time migration(LSRTM)treats the migration imaging as an inversion problem in the sense of least squares,which is a correction of the migration imaging process and results,and can overcome the problems of low resolution and uneven amplitude of the deep reservoir imaging by RTM.Taking the results of conventional migration imaging as the first step of the least-squares inversion,LSRTM constantly updates the imaging results to obtain the imaging results with higher resolution and better amplitude.It can effectively improve the exploration accuracy of unconventional oil and gas exploration and carbonate reservoir exploration in deep layer.Seismic waves propagating in the underground media include not only compressional waves but also shear waves.Due to the existence of fluid,the shear wave velocity or shear modulus is the more sensitive elastic medium parameter for oil and gas reservoir.In addition,based on the obtained multi-parameter inversion results of longitudinal and shear wave velocities and in combination with petrophysical experiments,the reservoir parameters such as porosity and oil-gas saturation can be further estimated,providing more geophysical evidence for the prediction,description and evaluation of oil-gas reservoirs.To some extent,the inversion method under the assumption of elastic medium can make up for the shortcomings of conventional seismic data processing methods,provide more accurate estimation of underground medium parameters and more geophysical evidence for the fine description of oil and gas reservoirs.Starting with least square inverse time migration method,this paper develops the least square inverse time migration method in elastic media under the premise of maintaining its computational efficiency,and further expands its application range.The research content of this paper mainly includes the following aspects:(1)According to the wave impedance information provided by logging data,the reflectance information is calculated and added into the function as a regularized constraint term to realize least-squares inverse time migration with wellconstrain regularization algorithm.Finally,the imaging test is conducted through the theoretical model;(2)The causes of low-frequency noise in reverse time migration in acoustic medium are analyzed,and a backscattering imaging condition satisfying the energy conservation is derived in this paper.Combining with the least square method,a least-square inverse time migration based on angle filtering imaging(ALSRTM)is realized,which is verified by the imaging test of the model;(3)On the basis of the traditional least squares reverse-time migration,this paper uses the first-order velocity-stress equation of elastic medium and analyzes the influence of the slowness perturbation on the forward process,then the migration operator is obtained by adjoint state method,and develops least-squares reverse time migration in elastic media;(4)In this paper,the expression of Hessian matrix is derived from the error function,and the inverse of Hessian matrix is used as the gradient precondition operator to realize preconditioned elastic least squares reverse time migration algorithm(P-ELSRTM).Tests show the effectiveness and advantages of P-ELSRTM when images complex model,and the adaptability of conventional ELSRTM and P-ELSRTM to low-shot data is compared and analyzed.