Dynamic Wavelet Estimation for Time-Lapse Seismic Inversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wavelet estimation methods for seismic inversion in hydrocarbon exploration fail to accurately characterize subsurface properties over time, particularly in time-lapse seismic surveys, due to assumptions of constant phase and lack of consideration for fluid dynamics and pressure responses, leading to increased uncertainty.
Innovation Solution
The method involves wavelet estimation using pseudo well logs derived from petro-elastic modeling (PEM) techniques, constrained by seismic data and well log data, to generate accurate mapping of formation properties, incorporating phase and amplitude spectra and reservoir simulation models to account for dynamic changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wavelet estimation methods are used for seismic inversion, then the process is simpler and faster, but the accuracy of subsurface property characterization over time deteriorates due to constant phase assumptions and lack of fluid dynamics consideration
Solution Approach 1:
The patent applies dynamics by transitioning from static wavelet estimation (constant phase assumptions) to dynamic wavelet estimation that adapts to changing subsurface conditions over time. The method incorporates time-varying phase and amplitude spectra that respond to fluid dynamics and pressure changes, making the wavelet model dynamic rather than fixed. This resolves the contradiction by accepting increased methodological complexity to achieve accurate time-lapse subsurface characterization.
Solution Approach 2:
The patent changes key parameters of the wavelet model including phase spectrum, amplitude spectrum, and timing relationships to reflect dynamic subsurface conditions. By allowing these parameters to vary with time and respond to fluid dynamics, the method achieves improved measurement precision for time-lapse seismic surveys while systematically managing the complexity through parameter-based adaptation.
2Reliability
If conventional wavelet estimation with constant phase assumptions is used, then the computational process is simpler, but the timing relationships and fluid dynamics are not accurately captured
Solution Approach 1:
The patent applies preliminary action by performing reservoir simulation and petro-elastic modeling before final wavelet estimation. This pre-computation of fluid dynamics, pressure responses, and rock property changes provides accurate timing relationships and phase variations that are then incorporated into the wavelet model. This approach ensures reliable timing capture while managing complexity through staged computation.
Solution Approach 2:
The patent introduces petro-elastic modeling as an intermediary between seismic data and wavelet estimation. This intermediary layer translates fluid dynamics and pressure changes into rock property variations that affect seismic wavelets, thereby accurately capturing timing relationships and fluid dynamics effects without directly solving complex fluid dynamics equations in the wavelet estimation process.
3Measurement precision
If dynamic simulation models incorporating fluid dynamics are used, then the subsurface characterization becomes more accurate, but the computational time and resources increase
Solution Approach 1:
The patent segments the computational process into distinct modules: reservoir simulation, petro-elastic modeling, and wavelet estimation. Each module handles specific aspects of the problem independently, allowing for optimized computation and potential parallel processing. This segmentation maintains high measurement precision by preserving the full dynamic simulation capability while reducing overall computational time through modular architecture.
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most critical aspects of fluid dynamics and pressure responses that most significantly affect wavelet characteristics. Rather than simulating all possible subsurface phenomena, the method identifies and models only those factors that have the greatest impact on seismic wavelet variations, thereby achieving accurate reservoir characterization with reduced computational burden.
Data Source
AI summary
Wavelet estimation may be performed in a reservoir simulation model that is constrained by seismic inversion data and well logs. A synthetic seismic trace is generated along with an estimated wavelet. The reservoir simulation model is revised based on results from model comparisons to actual data or base seismic data and is then used to perform a wavelet estimation. The estimated wavelet may then be used to plan further production at the well site environment, additional production at additional well site environments or any other production and drilling operation for any given present or future well site environment.