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Wavelet Transform for Upstream Operations

Fracwave research group

University of Houston

Petroleum Engineering Department

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Cullen College of Engineering

University of HOUSTON | Petroleum Engineering

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Motivation: Enhancing Oilfield Operations

  • Many industry solutions still depend on simplified assumptions, which reduces the accuracy of models and predictions.
  • A large amount of oilfield data is collected but often not fully analyzed—it’s mostly used for basic reports.
  • Current workflows misinterpret system complexities like:
    • Nonlinear behaviors.
    • Changing flow conditions.
    • Interactions across different reservoir scales.

As a result, this leads to:

    • Incomplete analysis.
    • Poor decision-making.
    • Higher risks.
    • Less efficient field development and management.

Current Oilfield Operational Challenges

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Standardization

Automation 

Integration 

Scalability 

Continuous improvement

To solve complex physical problems , Scientists simplified it with pre-assumptions to solve it with our old limited capabilities.

Need data driven solutions

Need generic solutions for machine learning scalability

Motivation : Machine Learning Needs Generic Solutions Without Assumptions

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Signal And Systems

System = transformation that maps an input signal x(t) into an output signal y(t).

Signal = physical quantity that conveys information over independent variables

=

In The

Age Of

Digitalization,

The

Oilfield

Is

Full

Of

Signals

And

Systems

=

Hydraulic Fracture is a system

Waterflooding is a system

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Signal Processing

Signal (data vs. time)

Fourier Transform

Frequency Analysis with no time domain analysis

Wavelet Transform

High resolution frequency and time domain analysis

Signal Processing

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Types Of Wavelet Transform

Discrete Wavelet Transform (DWT)

Decomposition to discrete composition

Continuous Wavelet Transform (CWT)

Decomposition over range of frequencies

Convolution of signal with a short wavy signal (wavelet)

Wavelet Transform

Used for looking for certain feature

Used to magnify all signal details.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Continuous Wavelet Transform (CWT)

Can be moved along signal

Simply, it is flexible mathematical microscope

Can be stretched/compressed

Can be real/complex

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Complex Microscope

Our mathematical microscope is designed to capture the complexities of oilfield systems and subsurface formations.

We can see a lot of complicated details in smooth treating pressures

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Widely Used Medical And Engineering Fields

Engineering Applications

  • Fault detection in rotating machinery and bearings.
  • Gearbox vibration analysis under load.
  • Structural health monitoring of bridges and pipelines.
  • Crack propagation analysis using acoustic signals.
  • Turbine blade damage diagnosis.
  • Signal denoising in experimental strain measurements.
  • Condition monitoring of mechanical components.

Medical Applications

  • ECG signal denoising and arrhythmia detection.
  • EEG signal analysis for seizure localization.
  • MRI and CT image compression and enhancement.
  • Detection of brain tumors and tissue anomalies.
  • Real-time heartbeat classification in wearable devices.
  • Lung sound analysis for respiratory disorders.
  • Ultrasound image texture segmentation.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Widely Used in Geophysics

Geophysics

  • Seismic signal denoising and enhancement.
  • Time-frequency decomposition of seismic traces.
  • Fracture and fault detection in seismic images.
  • Identification of thin beds using CWT attributes.
  • Microseismic event localization.
  • Reservoir characterization and stratigraphic analysis.

Geophysics

  • Wavelet-based compression of seismic volumes.
  • Decomposition of VSP and cross-well data.
  • Lithofacies classification using wavelet features.
  • Time-lapse (4D) seismic analysis.
  • Salt body boundary detection.
  • Multiscale feature extraction from gravity and magnetic data.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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We Develop Field Scale Technologies

Technology

Paper

Summary

Dynamic Fracture Propagation Characterization

Normalized CWT Scalogram tracked fracture growth calibrated using DFOS strain.

Normalized CWT Scalogram tracked fracture growth calibrated using microseismic data.

CWT + Deep Learning predicted microseismic events cloud from pressure.

Fracture Closure analysis

New fracture closure detection technique was validated using simulation and flow regimes.

New fracture closure detection technique identified closure pressure in lab DFIT tests.

New fracture closure detection technique estimated closure stress from field DFIT and strain.

Water Hammer modelling and induced fracture complexity characterization

CWT with Ridge Detection detected fracture complexity in field scale water hammer.

Inter-well connectivity

CrWTC identified the inter-well connectivity using injection and production data

Click on paper link to download each paper

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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20 Years Of Development

  • Prof. Mohamed Y. Soliman pioneered the application of wavelet transforms for well test analysis in 2003.
  • Building on his work, his students Ibrahim Eltaleb and Unal Ebru and others published several advanced techniques between 2019 and 2022—applying Discrete Wavelet Transform (DWT) to Diagnostic Fracture Injection Test (DFIT) interpretation, fracture propagation analysis, and inter-well connectivity assessment.

Click on paper link to download each paper.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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More Papers Will Be Published In SPE ATCE 2025

  • Several innovative technologies leveraging Continuous Wavelet Transform (CWT) will be presented at SPE ATCE 2025, with applications spanning Drilling, Enhanced Geothermal Systems (EGS), Carbon Capture, Utilization, and Storage (CCUS), and Stimulated Reservoir Volume (SRV) analysis.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Watch us

Several Industry webinars were held introducing our new technologies.

Click on link to watch the webinar.

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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You can propose a new research direction based on industry problems.

Think Beyond Technology, Wavelet Transform Is A New Lens On Complexity

Academia Industry Collaboration

University of HOUSTON | Petroleum Engineering

University of HOUSTON | Petroleum Engineering

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Together we can achieve more

  • To Join our consortium…contact us

Fracwave Research Group

Prof. Mohamed Y. Soliman

Petroleum Engineering Department

University of Houston

Office: 713-743-8640

msoliman@Central.UH.EDU

Mohamed Adel Gabry

magabry@cougarnet.uh.edu

University of HOUSTON | Petroleum Engineering