Wellbore Reflector Depth Detection with STFT Predictive Deconvolution
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Solution Overview
Problem
Existing methods for determining wellbore reflectors in hydraulic fracturing operations are prone to inaccuracies due to sensitivity to low wellbore resonance frequencies and contamination by pump noise, leading to unreliable detection of pressure oscillations and misinterpretation of reflectors.
Innovation Solution
A method utilizing predictive deconvolution of wellhead pressure oscillations, employing a Wiener least squares prediction filter to suppress noise and enhance the detection of wellbore reflectors by transforming pressure signals into time-frequency representations and applying a predictive deconvolution filter to determine reflection times and depths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If cepstrum algorithm is used to process pressure oscillation data, then automatic detection of reflectors is enabled, but sensitivity to pump noise and low resonance frequencies causes misinterpretation and reduced reliability
Solution Approach 1:
The patent extracts and removes the harmful pump noise signal from the pressure oscillation data before processing. By separating the useful reflector signal from the contaminating pump noise, the method achieves reliable automatic detection without the misinterpretation problems that plague the cepstrum algorithm
Solution Approach 2:
The patent introduces an intermediary processing step that transforms the raw pressure oscillation data into a noise-reduced representation before reflector detection. This intermediary processing layer acts as a mediator that preserves the reflector information while eliminating pump noise interference
2Measurement precision
If cepstrum algorithm amplifies periodic pump noise signal, then multiple strong cepstral peaks appear, but these peaks overlap with wellbore response and cause misinterpretation of reflectors
Solution Approach 1:
The patent converts the harmful pump noise signal into a benefit by using its periodic characteristics as a fingerprint for identification and removal. The known periodicity of pump noise is exploited to selectively eliminate it while preserving the aperiodic reflector signals
Solution Approach 2:
The patent extracts and removes the harmful pump noise signal from the pressure oscillation data before processing. By separating the useful reflector signal from the contaminating pump noise, the method achieves reliable automatic detection without the misinterpretation problems that plague the cepstrum algorithm
3Ease of operation
If cepstrum algorithm is used with small changes in time interval boundaries, then processing is simplified, but results become unstable and different interpretations occur
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor the processing results and adjust the time interval selection to maintain optimal performance. This feedback loop ensures that small variations in interval boundaries do not lead to unstable results, as the system automatically compensates for such variations
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
The method provides robust and accurate detection of wellbore reflectors with reduced sensitivity to noise, enabling real-time monitoring and post-job evaluation of hydraulic fracturing operations with improved precision and reduced computational complexity.
Implementation Method 1
performing a wellbore operation that produces a pressure wave and its reflections from wellbore reflectors; registering a pressure wave and its reflections with a high-frequency pressure sensor at wellhead
Implementation Method 2
transforming the registered pressure wave and its reflections from the frequency domain into time-frequency representation with Short Time Fourier Transform (STFT)
Implementation Method 3
The oscillations are caused by pressure pulses propagating along the wellbore also called tube waves. The wellbore reflector is defined as a site of changing the hydraulic impedance for the travelling pressure signal
Data Source
AI summary
This disclose relates to a robust method for automatic real-time monitoring or post-job evaluation of hydraulic fracturing operations based on predictive deconvolution of the wellhead pressure oscillations. Described are a method and system for determining depth of wellbore reflectors, implemented by performing a wellbore operation producing a pressure wave and its reflections from the wellbore reflectors; registering a pressure wave and its reflections; preprocessing the registered pressure wave and its reflections with a bandwidth filter; transforming the registered pressure wave and its reflections from the frequency domain into time-frequency representation with Short Time Fourier Transform (STFT); applying of a predictive deconvolution filter to STFT representation; identifying of a reflected signal on a plot in coordinates “reflection time—physical time” and determining the reflection time for the signal reflected from the wellbore reflector; determining the depth of wellbore reflector by multiplying the reflection time by pressure wave speed.