Windowed Decomposition for Time-Division CSEM Data Processing

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Solution Overview

Problem

Controlled-source electromagnetic (CSEM) surveys face challenges in processing time-division source waveforms due to unknown arrival times and transition effects between sub-sequences, leading to difficulties in isolating frequency components and achieving optimal signal-to-noise ratio (SNR) in geophysical prospecting, especially in hydrocarbon exploration.

Innovation Solution

The method involves decomposing electromagnetic data using windowed decomposition techniques, such as Fourier transformation or correlation-based methods, employing either a large-window or small-window approach to address arrival time uncertainties and isolate frequency components, which are then used to image earth properties for subsurface interpretation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If time-division source waveform is used to provide flexible frequency content and improved signal-to-noise ratio, then frequency content flexibility and SNR are improved, but unknown arrival times and transition effects between sub-sequences make it difficult to isolate frequency components

Engineering Contradiction:

Improvesignal-to-noise ratioVSAvoidfrequency component isolation

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The time-division source waveform is segmented into multiple sub-sequences, each with different fundamental frequencies. This segmentation allows the frequency content to be localized in time, improving the signal-to-noise ratio while enabling systematic processing of each frequency component separately through windowed decomposition methods.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The method applies preliminary windowing and decomposition operations to the electromagnetic data before final analysis. By pre-processing the data with windowed Fourier transformation or correlation-based methods, the unknown arrival times are accounted for through the windowing function, enabling effective frequency component isolation despite the time-division structure.

Inventive Principle:

Principle #10Preliminary action

2Difficulty of detecting and measuring

If windowed decomposition method is applied to deal with unknown arrival times, then frequency component isolation is improved, but processing complexity increases

Engineering Contradiction:

Improvefrequency component isolationVSAvoidprocessing complexity

Core Design Contradiction:

Difficulty of detecting and measuringVSDevice complexity

Solution Approach 1:

The window function serves as an intermediary element between the time-division waveform and the frequency analysis. By introducing the window function, the method bridges the gap between unknown arrival times and frequency component isolation, enabling systematic decomposition without requiring precise arrival time knowledge while managing processing complexity through structured algorithms.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If transition effects between sub-sequences are present in time-division waveform, then frequency content flexibility is improved, but ripple noise and edge effects increase

Engineering Contradiction:

Improvefrequency content flexibilityVSAvoidripple noise and edge effects

Core Design Contradiction:

Adaptability or versatilityVSObject-generated harmful factors

Solution Approach 1:

The transition effects between sub-sequences, which initially cause ripple noise and edge effects, are converted into beneficial frequency localization features. Through windowed decomposition and correlation-based methods, these transitions are systematically processed to isolate frequency components, transforming the harmful effects into useful information for subsurface characterization.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach effectively deals with arrival time uncertainties, reduces noise, and improves the signal-to-noise ratio, enabling accurate determination of earth properties and assisting in hydrocarbon exploration and production by isolating frequency components and minimizing ripple noise and edge effects.

Implementation Method 1

The electromagnetic ('EM') fields generated by the transmitter may be created by injecting the currents into the earth or seawater/seafloor

Methodology Applied

Scientific EffectElectromagnetic induction: Electromagnetic Induction

Implementation Method 2

decomposing the data into frequency components using a windowed decomposition method involving Fourier transformation

Methodology Applied

Scientific EffectFourier transformation:

Data Source

PatentUS10209386B2Processing methods for time division CSEM data

Publication Date: 2019.02.19 EXXONMOBIL UPSTREAM RESEARCH COMPANY(US)

AI summary

Method for inverting, in the frequency domain (42), controlled source electromagnetic survey data (41) acquired using a time-division compound waveform made up of sub-sequences of different base waveforms, for example square waves of different frequencies. A windowed Fourier decomposition method is used, with the window size and shape designed in consideration of the sub-sequences. The window length may be twice the length of the compound waveform, or more. Alternatively the window length may be comparable to the sub-sequence length, or slightly less. Window shapes include cos2, rectangular, and triangular. The method addresses the problem of unknown arrival times for each sub-sequence, and also transition transients that occur between sub-sequences.