Well-Seismic Tie Alignment Using Global and Local Time Shifts
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Solution Overview
Problem
Current well-seismic tie methods in oil and gas exploration suffer from inaccuracies due to subjective manual calibration and errors in automatic calibration, lacking a balanced approach for global and local adjustments, which affects the efficiency and accuracy of seismic data interpretation and reservoir prediction.
Innovation Solution
A system and method utilizing machine learning models for determining seismic wavelets, performing preliminary and accurate alignments through global and local time shifts, and constructing energy error matrices to optimize the well-seismic tie process, enhancing calibration accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used for well-seismic tie, then interpreters can perform detailed adjustments, but the process introduces subjective factors, has uneven accuracy, and low efficiency
Solution Approach 1:
The system performs automatic calibration without manual intervention by computing time shifts between seismic records and well logs through algorithmic correlation analysis, eliminating subjective factors and improving both efficiency and consistency of calibration results
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated computational algorithms that calculate optimal time shifts through signal correlation, substituting human interpreters with machine-based processing to eliminate subjectivity and improve efficiency
2Productivity
If automatic calibration methods are used for well-seismic tie, then calibration efficiency is improved, but the process is susceptible to noisy data and produces large errors
Solution Approach 1:
The system performs preliminary alignment using global time shifts computed from overall correlation between seismic and well data before conducting detailed local calibration, preparing the data in advance to reduce the impact of noise and improve subsequent calibration accuracy
Solution Approach 2:
The patent implements an iterative calibration process where correlation coefficients are computed, time shifts are adjusted, and the process repeats with feedback from previous results to optimize alignment accuracy while filtering out noisy data through multiple refinement cycles
3Reliability
If global matching is used for well-seismic tie calibration, then overall alignment is achieved, but the method lacks effective combination of global adjustment and local fine adjustment, resulting in suboptimal accuracy
Solution Approach 1:
The patent divides the calibration process into two distinct stages: global alignment using overall correlation to establish baseline time shifts, and local fine-tuning through detailed correlation analysis of specific seismic events, allowing both broad stability and localized precision
Solution Approach 2:
The system applies different calibration strategies to different parts of the data - global time shifts for overall alignment and localized correlation analysis for specific seismic events or depth intervals, optimizing accuracy for each region while maintaining overall consistency
Data Source
AI summary
A method and a system for seismic data processing are provided. The method includes: determining target seismic data as a seismic trace near well; determining a synthetic seismic record of a target calibration object in a time domain; determining a size of a time window based on the seismic trace near well and the synthetic seismic record; generating a truncated synthetic seismic record and a truncated seismic trace near well; calculating an amount of time deviation between a geological stratification in the time domain and a seismic horizon; determining at least one correlation between the truncated synthetic seismic record and the truncated seismic trace near well under at least one time shift; obtaining a time shift corresponding to a maximum correlation as a global time shift; and performing preliminary alignment on the synthetic seismic record through the global time shift.