Seismic First-Arrival Picking for Locating Hydrocarbon Formations

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Solution Overview

Problem

Conventional seismic data processing techniques for first-arrival picking are time-consuming, require significant human effort, and often result in inaccurate picks, while existing automated methods lead to false detections.

Innovation Solution

A method utilizing texture segmentation and fuzzy c-means clustering to identify first arrival picks in seismic data, employing descriptors like mean, second difference moment, energy, and contrast variance, followed by an energy ratio enhancement to improve accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual picking is used for first arrival identification, then accuracy can be maintained through human decision making, but significant time and human effort are required

Engineering Contradiction:

Improvefirst arrival picking accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The seismic shot record is divided into multiple windows along the time axis, with each window processed independently to identify first arrivals. This segmentation allows parallel processing of different time segments, significantly reducing overall processing time while maintaining accurate identification through localized analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The manual mechanical picking process is replaced with an automated computational system that uses energy ratio calculations and clustering algorithms. This substitution eliminates human labor while maintaining high accuracy through objective mathematical criteria for identifying first arrivals

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated picking methods are used to reduce time and human effort, then processing speed improves, but false picking and inaccurate results occur

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidpicking accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system calculates energy ratios for each window and uses clustering algorithms to identify patterns across multiple windows. The feedback mechanism compares energy ratios against clustered results to confirm first arrival picks, reducing false positives while maintaining high processing speed through automated decision criteria

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The method transforms the seismic data into energy ratio parameters that highlight first arrival characteristics. By changing from raw amplitude data to energy ratio parameters and applying clustering on these transformed parameters, the system achieves both speed and accuracy in automated picking

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If conventional seismic data processing techniques are applied, then comprehensive analysis is achieved, but the process becomes complex and expensive

Engineering Contradiction:

Improveinformation extraction completenessVSAvoidprocessing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The method extracts only the essential first arrival information from seismic data using energy ratio calculations and clustering, rather than applying comprehensive conventional processing. This extraction approach obtains the necessary information while avoiding the complexity and cost of full conventional processing workflows

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12411257B1Method for locating a hydrocarbon-containing geologic formation and extracting oil and/or gas

Publication Date: 2025.09.09 KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS

AI summary

A method for first arrival picking of seismic data includes receiving a seismic shot record data set from a seismic event, extracting texture features from the seismic shot record data set, calculating an energy ratio matrix from the seismic shot record data set, using fuzzy c-means to cluster the texture features into a strong linear cluster, a moderate linear cluster, and a random noise cluster, calculating a first arrival class matrix from the moderate linear cluster, multiplying the first arrival class matrix by the energy ratio matrix to obtain a first arrival pick matrix, and identifying first arrival picks from the first arrival pick matrix.