Converted-Wave Statics Estimation Using PP-Guided PS Corrections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Estimating converted-wave statics, particularly converted-wave receiver statics, is challenging due to the complex near-surface layer effects, which distort seismic imaging and require manual effort that is not feasible for large seismic datasets, and existing methods do not adequately address the need for accurate static corrections in shear-wave seismic data.
Innovation Solution
A method for estimating converted-wave statics by combining converted-wave receiver statics with pressure-wave source statics to form a complete static solution, using multicomponent seismic datasets to generate statics-corrected seismic images that facilitate hydrocarbon exploration and production, and determining wellbore paths based on these images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to estimate converted-wave statics, then accuracy can be maintained, but the process becomes too time-consuming and labor-intensive for large seismic datasets
Solution Approach 1:
The system performs self-service by automatically estimating converted-wave receiver statics through an objective function that uses PP-target events as guidance. The method autonomously processes large seismic datasets without manual intervention, using algorithms to determine statics corrections that flatten PS-events while leveraging the more accurately imaged PP-events as references.
Solution Approach 2:
PP-target events serve as an intermediary to guide the estimation of PS-receiver statics. The method uses the well-imaged PP-events as a reference framework to constrain and guide the statics correction process for PS-events, transferring information from the pressure wave domain to the converted wave domain to improve accuracy.
2Loss of information
If converted-wave data is used to improve seismic imaging and reservoir characterization, then additional information is obtained, but near-surface effects distort the data and require complex static corrections
Solution Approach 1:
The method converts the harmful near-surface statics effects into a solvable optimization problem. By formulating an objective function that measures event flatness and using automated statics estimation, the system transforms the distortion problem into a mathematical optimization task that can be systematically solved across large datasets.
Solution Approach 2:
The system performs preliminary statics correction by determining PS-receiver statics before final imaging. The method pre-processes the converted-wave data by estimating and applying statics corrections based on PP-guided objective function optimization, preparing the data for subsequent high-quality imaging and reservoir characterization.
3Ease of operation
If PS-receiver statics are determined independently without PP-guidance, then processing simplicity is maintained, but statics estimation accuracy deteriorates due to cycle-skipping and near-surface complexity
Solution Approach 1:
The method implements feedback by using PP-target event information to guide and constrain PS-receiver statics estimation. The objective function incorporates PP-event flatness measurements as feedback to adjust PS-statics, creating a closed-loop system that continuously refines the statics corrections based on observed data quality.
Solution Approach 2:
The system changes parameters by transitioning from independent PS-statics estimation to PP-guided PS-statics estimation. The method modifies the estimation approach by introducing PP-event parameters as constraints in the objective function, fundamentally changing how statics are determined from a standalone process to a coupled, guided process.
Data Source
AI summary
Methods and systems for estimating converted-wave statics are disclosed. The methods include obtaining a multicomponent seismic dataset for a subterranean region, determining an array of PP-source statics and an array of PP-receiver statics for the PP-seismic dataset, generating a PP-receiver stack based on the PP-seismic dataset, the array of PP-source statics, and the array of PP-receiver statics, and generating a PS-receiver stack based on the PS-seismic dataset and the array of PP-source statics. The methods also include identifying a PP-target event on the PP-receiver stack, forming a space-time window of the PS-receiver stack guided by the PP-target event, determining an objective function, and determining an array of PS-receiver statics based on an extremum of the objective function. The methods further include forming a statics-corrected PS-seismic dataset based on the array of PS-receiver statics and the array of PP-source statics, and forming a seismic image based on the statics-corrected PS-seismic dataset.