Seismic Velocity Model Update via Time-Windowed Trace Pairs
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Solution Overview
Problem
Current seismic survey methods face challenges in accurately imaging subterranean regions of interest, particularly in determining seismic velocity models that accurately match observed seismic datasets, leading to incomplete characterization of hydrocarbon reservoirs.
Innovation Solution
The method involves generating a simulated seismic dataset based on a seismic velocity model and source/receiver geometry, forming time-windowed trace pairs, and using a penalty function and cross-correlation to determine seismic velocity increments, which are then used to update the seismic velocity model, thereby improving the accuracy of the subterranean image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic velocity modeling methods are used, then the processing is simpler and faster, but the accuracy of the seismic velocity model and resulting subterranean image is insufficient
Solution Approach 1:
The patent implements an iterative feedback loop where the simulated seismic dataset is compared with the observed seismic dataset, and the velocity model is updated based on the mismatch. This feedback mechanism continues until convergence, significantly improving velocity model accuracy. The objective function quantifies the mismatch and guides the updates, ensuring systematic improvement with each iteration.
Solution Approach 2:
The patent creates a simulated seismic dataset that copies the structure and characteristics of the observed seismic dataset but is generated using the current velocity model. This simulated copy is then compared with the actual observed data to identify discrepancies and improve the model, enabling accurate imaging without directly modifying the observed data.
2Reliability
If the seismic velocity model is updated iteratively to improve accuracy, then the matching between observed and simulated datasets improves, but the computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions by generating the simulated seismic dataset using the current velocity model before comparing it with observed data. This allows the system to proactively identify mismatches and prepare update directions in advance, reducing the need for extensive trial-and-error iterations and optimizing computational efficiency.
Solution Approach 2:
The patent applies partial updates to the velocity model based on the extremum of the objective function, rather than complete re-modeling at each iteration. This partial action approach achieves sufficient matching accuracy with fewer computational iterations, balancing reliability improvement with time efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the matching of observed and simulated seismic datasets, leading to a more precise seismic velocity model and improved imaging of subterranean structures, aiding in hydrocarbon reservoir characterization and drilling decisions.
Implementation Method 1
a seismic source generates seismic waves which propagate through the subterranean region of interest are and detected by seismic receivers
Implementation Method 2
forming an objective function based on a penalty function and a cross-correlation between the members of each pair
Data Source
AI summary
Methods of and systems for forming an image of a subterranean region of interest are disclosed. The method includes obtaining an observed seismic dataset and a seismic velocity model for the subterranean region of interest and generating a simulated seismic dataset based on the seismic velocity model and the source and receiver geometry of the observed seismic dataset. The method also includes forming a plurality of time-windowed trace pairs from the simulated and the observed seismic datasets, and forming an objective function based on a penalty function and a cross-correlation between the members of each pair. The method further includes determining a seismic velocity increment based on the extremum of the objective function and forming an updated seismic velocity model by combining the seismic velocity increment and the seismic velocity model, and forming the image of the subterranean region of interest based on the updated seismic velocity model.