CN-121995462-A - Gaussian beam target imaging method with variable grid constraint

CN121995462ACN 121995462 ACN121995462 ACN 121995462ACN-121995462-A

Abstract

The invention belongs to the technical field of seismic exploration, and relates to a Gaussian beam target imaging method with variable grid constraint, which comprises the steps of firstly aiming at a set imaging target, collecting seismic data of the target, observation data of an observation system and offset parameters of Gaussian beam offset; the method comprises the steps of establishing an offset velocity field model, setting an average sampling rate and a highest sampling rate value, performing first global meshing according to a meshing formula based on velocity difference of the offset velocity field model in the vertical direction, performing second meshing according to characteristics of different target imaging bodies, refining a target layer grid, and finally constructing a reverse continuation wave field by adopting an imaging point uplink ray tracking technology to realize target-oriented high-precision imaging. The invention further improves the imaging precision on the basis of Gaussian beam offset imaging.

Inventors

CHEN JUAN
WANG HAOKUN
ZHANG MENGBO
MOU YANG
ZHAO YUHUA
LIU JINTAO
ZHANG YADONG
DU GUANGHONG
ZHU JUN
LI JIAOJIAO

Assignees

中国石油天然气股份有限公司

Dates

Publication Date: 20260508
Application Date: 20241108

Claims (7)

1. The Gaussian beam target imaging method with variable grid constraint is characterized by comprising the following steps of: S1, aiming at a set imaging target, acquiring seismic data of the target, observation data of an observation system and offset parameters of Gaussian beam offset; S2, based on the data and parameters collected in S1, establishing an offset velocity field model, and setting an average sampling rate And highest sampling rate Is a value of (2); S3, performing first imaging domain global meshing according to a meshing formula based on the speed difference of the offset speed field model in the vertical direction; s4, performing second grid subdivision of an imaging domain according to the characteristics of different target imaging bodies, and refining an imaging target layer grid; s5, constructing a reverse extended wave field by adopting an imaging point uplink ray tracing technology, and realizing high-precision imaging facing to the target.
2. The variable grid constrained gaussian beam target imaging method according to claim 1, wherein said offset parameters comprise offset velocity field lateral sampling points Longitudinal sampling point Space sampling interval Time sampling interval Time sampling point number Main frequency Reference frequency Highest frequency Offset shot count for seismic recording Angle of ray emission And Is a parameter of (a).
3. The variable grid constrained gaussian beam target imaging method according to claim 1, wherein performing a first imaging domain global grid subdivision in S3 comprises the steps of: s3.1, carrying out integral scanning analysis on the initial offset velocity field to calculate the average velocity And minimum speed At the same time, dividing the velocity horizon according to the velocity variation gradient and calculating the minimum velocity arrangement of each layer ; S3.2 based on the calculation of S3.1 and the average sampling rate set in S2 And highest sampling rate The first imaging domain global meshing is performed by the following equation 1 (1) Wherein, the Representing a grid scale arrangement.
4. A method of variable grid constrained gaussian beam target imaging according to claim 3, wherein the method of dividing the velocity horizon according to the velocity varying gradient in S3.1 is as follows: Dividing a speed horizon by the speed change rate of adjacent sampling points; A horizon is considered herein to be an interface if the rate of change of speed of a sample point varies drastically, i.e., the rate of change of speed at that sample point is greater than 20% of the average of the rates of change of speeds of two adjacent sample points.
5. The method of variable grid constrained gaussian beam target imaging according to claim 1, wherein the method of performing the second grid subdivision of the imaging field in S4 is as follows: according to each layer And (3) formulating the secondary grid division size to optimize the grid scale of the target horizon, and adjusting the grid division size by combining the Gaussian beam offset imaging result of the first grid division to ensure that fine imaging is realized.
6. The method of variable grid constrained gaussian beam target imaging according to claim 5, wherein in the second grid subdivision process, the method of adjusting the grid subdivision size is: at a certain layer speed And when the speed of the layer is simultaneously greater than or simultaneously less than that of the layer adjacent to the layer, the imaging domain of the layer is subjected to grid subdivision, and is adjusted from 5x5 to 2x2 or 1x1.
7. The variable grid constrained gaussian beam target imaging method according to claim 1, wherein S5 comprises the steps of: s5.1, using Gaussian beam integration to represent a green function; s5.2, representing an up-down wave field by using a green function; S5.3, performing correlated imaging on the up-down wave field under the constraint of a Gaussian window; and S5.4, interpolating the green functions from the shot points and the detection points at the imaging points calculated in the step S5.2 according to the size of the grid of the secondary subdivision, so that the grid points of the secondary subdivision have corresponding green functions, and repeating the related imaging of the up-and-down wave field in the step S5.3 under the constraint of a Gaussian window to obtain a Gaussian beam offset imaging result of variable grid constraint.

Description

Gaussian beam target imaging method with variable grid constraint Technical Field The invention belongs to the technical field of seismic exploration, and relates to a Gaussian beam target imaging method with variable grid constraints. Background The migration imaging technology is a seismic data processing means which utilizes the geophysical theory to carry out wave field back transmission on the seismic records observed on the earth surface and can obtain the underground medium structure image after eliminating the propagation effect of the seismic waves. Offset imaging technology has been widely used in the fields of geophysical exploration, geological resource exploration, geotechnical, bridge and tunnel engineering for decades. The offset imaging is used as an indispensable key link in the seismic data processing flow, and the imaging precision of the offset imaging has important significance for seismic data interpretation and also has important guiding significance for oil and gas exploration and development. However, the process of offset imaging is a mathematical physical challenge that requires a combination of computational accuracy and computational efficiency. It is not desirable to pursue high accuracy singly and consider high efficiency singly. At present, the offset imaging method is mainly divided into rays and wave equation types, wherein the rays are mainly the ray integration method, the method has higher calculation efficiency, but the calculation accuracy is far less accurate than the differential method commonly used in wave equation types, the differential rule needs more running memory and calculation time, and the calculation efficiency is relatively lower. So, in consideration of the practical situation, the radiation type offset, that is, the radiation integral offset is still the most widely used imaging method in practical production. At present, with the continuous deep exploration of oil and gas resources, a seismic exploration target gradually changes from a large-scale structure oil and gas reservoir to a medium-scale and small-scale complex lithologic oil and gas reservoir, the accuracy requirement on exploration data is higher, and the conventional seismic exploration technology has difficulty in meeting the current requirement of oil and gas resource exploration. Therefore, how to maintain the advantage of high efficiency of ray-like deflection and improve imaging accuracy has become an important research in the academic world and industry. Currently, the most studied ray-like offset imaging methods are Kirchhoff (Kirchhoff) offset and gaussian beam offset. The kirchhoff migration method has the problem that no wave field exists in a scattered area singular value and a shadow area, so that the imaging effect is reduced. The Gaussian beam migration solves the problems that the Kirsgh migration exists due to the fact that the singular value of the scattered area and the shadow area have no wave field through the complex exponential terms which are attenuated in a Gaussian envelope manner relative to the central rays, and the paraxial rays are compared with the progressive rays, so that more propagation information is considered, and the problem of multipath cannot be limited. Therefore, with higher imaging accuracy and imaging efficiency comparable thereto, gaussian beam ray shift is often seen as an optimal choice of kirchhoff shift. How to maintain high imaging efficiency and further improve imaging accuracy on this basis is an important research direction facing the current day. Disclosure of Invention The invention aims to provide a Gaussian beam target imaging method with variable grid constraint, which is based on Gaussian beam offset imaging, further improves imaging precision and promotes the progress of the research on high-efficiency and high-precision imaging methods in the current seismic exploration technology. The invention adopts the technical scheme that the Gaussian beam target imaging method with variable grid constraint comprises the following steps: S1, aiming at a set imaging target, acquiring seismic data of the target, observation data of an observation system and offset parameters of Gaussian beam offset; S2, based on the data and parameters collected in S1, establishing an offset velocity field model, and setting an average sampling rate And highest sampling rateIs a value of (2); S3, performing first imaging domain global meshing according to a meshing formula based on the speed difference of the offset speed field model in the vertical direction; s4, performing second grid subdivision of an imaging domain according to the characteristics of different target imaging bodies, and refining an imaging target layer grid; s5, constructing a reverse extended wave field by adopting an imaging point uplink ray tracing technology, and realizing high-precision imaging facing to the target. The invention is also characterized in that the offset parameter in