Acoustic Impedance Inversion for Thick Casing Cement Evaluation
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Solution Overview
Problem
Conventional acoustic inversion techniques are inadequate for accurately determining the acoustic impedance of annular fill materials in thicker casings and wellbore environments with heavy borehole muds, leading to reduced accuracy in cement evaluation and zonal isolation assessment.
Innovation Solution
A model-based inversion method using a three-dimensional forward model to estimate casing thickness, annular acoustic impedance, and mud acoustic impedance, which iteratively updates estimates based on misfit calculations until convergence, accounting for tool positioning and eccentering effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional acoustic inversion techniques are used, then the measurement process is simple, but the measurement precision deteriorates in thick casings and heavy borehole muds
Solution Approach 1:
The inversion process is segmented into distinct stages: initial parameter estimation, iterative misfit calculation, and convergence checking. Each stage handles specific aspects of the inversion, allowing the complex problem to be managed through modular, sequential processing steps that improve precision without overwhelming system complexity
Solution Approach 2:
The inversion method employs dynamic iterative updating of parameter estimates based on misfit calculations. The algorithm adapts the estimates of casing thickness, annular acoustic impedance, and mud acoustic impedance through multiple iterations, allowing the solution to evolve dynamically toward optimal accuracy for thick casings and heavy muds
2Measurement precision
If conventional acoustic inversion techniques are used, then the processing time is short, but the measurement precision deteriorates for thick casings
Solution Approach 1:
Initial estimates of key parameters (casing thickness, mud acoustic impedance, annular acoustic impedance) are obtained before the main iterative inversion process. This preliminary action provides a closer starting point for the iteration, reducing the number of cycles needed to achieve convergence and thereby reducing processing time while maintaining high precision for thick casings
Solution Approach 2:
The inversion algorithm incorporates feedback through misfit calculation between measured and modeled acoustic responses. This feedback mechanism guides the iterative updates of parameter estimates, ensuring that each iteration moves the solution closer to the true values, thus achieving high precision efficiently without excessive processing time
3Reliability
If conventional acoustic inversion techniques are used, then the method is easy to implement, but the reliability deteriorates in heavy borehole muds
Solution Approach 1:
The inversion method extends the analysis into the frequency domain by computing model spectra and comparing them with measured spectra across multiple frequencies. This dimensional extension from simple time-domain analysis to frequency-domain spectral analysis provides additional constraints that improve reliability for determining annular acoustic impedance in heavy muds, while the structured approach manages the increased complexity
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 enhances the accuracy of acoustic impedance determination in thick casings and heavy borehole muds, improving the evaluation of cement integrity and zonal isolation by providing precise measurements of casing thickness, annular fill acoustic impedance, and mud impedance.
Implementation Method 1
an acoustic logging tool, acoustic data comprising acoustic waves reflected from the casing, the annular fill material, the formation, one or more interfaces between any of the casing, the annular fill material, and the formation
Implementation Method 2
transmit a broadband pulse, usually between 200 and 700 kHz, to the casing wall to excite a thickness resonance mode in the casing
Data Source
AI summary
Techniques involve obtaining acoustic data from an acoustic logging tool, where the acoustic data includes waves reflected from the casing, the annular fill material, the formation, and/or interfaces between any of the casing, the annular fill material, and the formation. A crude casing thickness, tool position (e.g., eccentering), mud sound velocity may be estimated using the acoustic data. Techniques also involve computing a model spectra and an estimated casing thickness using a forward model and based on a crude casing thickness, an initial mud acoustic impedance, and an initial annular acoustic impedance, estimating a specular signal using the model spectra and the acoustic data in a first time window, computing a calibrated model signal using the estimated specular signal and computed model spectra, computing a misfit of the computed calibrated model signal and acoustic data in a second time window comprising the initial time window, and computing a correction update to one or more of the estimated casing thickness an estimated apparent annular acoustic impedance and an estimated mud acoustic impedance, based on the misfit. Techniques involve iteratively estimating the model spectra and the Jacobian curve, computing the specular signal, computing the misfit, and computing the update until the update is below a threshold. Outputs may include one or more of a casing thickness, an apparent acoustic impedance of the annular fill material, and the acoustic impedance of mud.