Seismic Wavelet Estimation for 4D Reservoir Characterization
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Solution Overview
Problem
Current methods for estimating seismic wavelets in 3D seismic data are inaccurate, particularly in 4D seismic processing, due to instability, inadequacy for 4D processing, and challenges with vertical and horizontal well configurations, leading to errors in interpreting subtle changes in reservoirs.
Innovation Solution
A method that aligns base and monitor seismic surveys, subtracts the base survey from the monitor survey to identify seismic changes, and optimizes seismic traces to simultaneously determine the seismic wavelet and reflectivity changes, using 4D reflectivities and sparse data to constrain the inversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wavelet estimation methods are used in 3D seismic data processing, then the process is relatively simple, but the accuracy of wavelet estimation deteriorates, particularly in 4D seismic processing
Solution Approach 1:
The method segments the wavelet estimation problem by separating the estimation of wavelet parameters from reflectivity changes. It divides the seismic data into different components (trace amplitudes, trace timings, wavelet parameters) and estimates them independently through optimization, allowing for more accurate 4D wavelet estimation without excessive complexity
Solution Approach 2:
The invention transitions from traditional 3D seismic processing to 4D seismic processing by adding the time dimension. It estimates wavelet parameters that vary over time (across base and monitor surveys) and incorporates this temporal variation into the convolution model, improving accuracy for detecting subtle reservoir changes over time
2Measurement precision
If traditional inversion techniques are applied to align base and monitor surveys, then the processing is straightforward, but measurement precision of subtle changes deteriorates
Solution Approach 1:
The method uses an optimization framework that incorporates feedback loops. It iteratively adjusts wavelet parameters and reflectivity changes to minimize the difference between observed and synthetic seismic traces. This feedback mechanism allows for precise detection of subtle changes by continuously refining the estimation based on the misfit between data and model
Solution Approach 2:
The invention changes the approach from fixed wavelet assumptions to variable wavelet parameters that can change over time. It estimates wavelet parameters (such as frequency, duration, amplitude) as variables that differ between base and monitor surveys, allowing the model to adapt to actual changes in seismic wavelet characteristics due to reservoir conditions
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 provides a more accurate estimation of the seismic wavelet, reducing errors in 4D seismic data interpretation and enhancing the ability to detect subtle changes in reservoir conditions.
Implementation Method 1
performing an optimisation operation simultaneously on said plurality of seismic traces so as to optimise for said seismic wavelet by simultaneously optimising for said seismic wavelet and reflectivity change data
Implementation Method 2
determining a seismic wavelet which links observed seismic data to a sequence of reflectivities
Data Source
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AI summary
Disclosed is a method for characterising the evolution of a reservoir by determining a seismic wavelet which links observed seismic data to a sequence of reflectivities. The method comprises obtaining seismic data representing seismic changes which have occurred between a first time and a second time, said seismic data comprising a plurality of seismic traces; and performing an optimisation operation simultaneously on the seismic traces so as to optimise for said seismic wavelet. The optimisation operation may be performed without using known reflectivity data as an input.