Nonlinear Beamforming for Seismic First-Break Picking

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Solution Overview

Problem

Existing methods for picking first-break times in seismic data are inaccurate and unreliable, especially in noisy land data, due to varying signal amplitudes and noise characteristics, making manual picking infeasible and automatic methods unsatisfactory.

Innovation Solution

The method involves generating a pre-processed seismic dataset and an initial refraction velocity model, followed by nonlinear beamforming to create a first-break energy-enhanced dataset, estimating a refined refraction velocity model, and determining first-break times from the post-processed dataset to improve the accuracy of seismic imaging and hydrocarbon reservoir location.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If nonlinear beamforming is applied to enhance first-break energy, then the signal-to-noise ratio is improved, but the computational complexity increases

Engineering Contradiction:

Improvefirst-break picking reliabilityVSAvoidprocessing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

An initial refraction velocity model is generated from the pre-stack seismic dataset before applying nonlinear beamforming. This preliminary model guides the beamforming process to focus computational resources on enhancing first-break energy along expected ray paths, improving reliability while managing complexity through structured preprocessing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The refraction velocity model serves as an intermediary that connects the raw seismic data to the nonlinear beamforming process. It acts as a guide for the beamforming algorithm to selectively enhance first-break energy, thereby improving picking reliability without requiring exhaustive processing of all possible wave paths

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated first-break picking is used, then productivity increases, but measurement precision decreases due to noise and varying signal amplitudes

Engineering Contradiction:

Improvepicking efficiencyVSAvoidfirst-break time accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The method extracts and enhances only the first-break energy from the seismic dataset using nonlinear beamforming. By isolating and enhancing this specific signal component, automated picking can operate on enhanced data with improved signal-to-noise ratio, maintaining high productivity while achieving better measurement precision

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The nonlinear beamforming process transforms the seismic data by changing the amplitude parameters of first-break energy relative to other signals. This parameter transformation enhances the detectability of first-break arrivals, allowing automated algorithms to achieve both high efficiency and improved accuracy in picking times

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If manual first-break picking is performed, then measurement precision may be maintained, but productivity decreases and the process becomes infeasible for large datasets

Engineering Contradiction:

Improvefirst-break time accuracyVSAvoidprocessing throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system creates an enhanced seismic dataset where first-break energy is automatically emphasized through nonlinear beamforming. This self-enhancing process allows subsequent automated picking to achieve precision comparable to manual methods while maintaining high productivity, as the data structure itself facilitates accurate automated detection

Inventive Principle:

Principle #25Self-service

4Reliability

If traditional processing methods are used, then device complexity remains low, but the signal-to-noise ratio is insufficient for reliable first-break picking

Engineering Contradiction:

Improvefirst-break picking reliabilityVSAvoidprocessing methodology complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The method performs preliminary processing steps including generating an initial velocity model and pre-processing the seismic dataset before applying nonlinear beamforming. This structured preliminary action prepares the data in a form that enables reliable first-break picking while organizing the complexity into manageable, sequential steps

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11947063B2Method of conditioning seismic data for first-break picking using nonlinear beamforming

Publication Date: 2024.04.02 SAUDI ARABIAN OIL CO

AI summary

A method and system for picking first-break times for a seismic dataset are disclosed. The method includes generating a pre-processed seismic dataset and an initial refraction velocity model from the pre-stack seismic dataset and generating a first-break energy-enhanced seismic dataset using nonlinear beamforming applied to the pre-processed seismic dataset and the initial refraction velocity model. The methods further include estimating a refined refraction velocity model from the first-break energy-enhanced seismic dataset, and generating a post-processed seismic dataset from the refined refraction velocity model and first-break energy-enhanced seismic dataset. The methods still further include, for each pre-stack trace, determining a first-break time from the post-processed seismic dataset and the refined refraction velocity model. The methods also include generating a seismic image based on the first-break time for each pre-stack trace and determining a location of a hydrocarbon reservoir based on the seismic image.