Acoustic Logging Mud Density Inversion for Anisotropy Accuracy
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Solution Overview
Problem
Accurate anisotropy measurements in acoustic logging are sensitive to mud slowness and mud density in the wellbore, leading to large estimation errors, which affect the accuracy of seismic imaging and reservoir characterization.
Innovation Solution
An acoustic logging method that involves acquiring waveforms for multiple acoustic wave modes, deriving position-dependent mode dispersion curves, and inverting these curves to jointly estimate shear wave anisotropy, vertical shear wave slowness, mud slowness, and mud density as a function of depth, using a computed library of dispersion curves and adaptive weighting to minimize errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic logging measurements are used to determine velocity structure and anisotropy, then seismic imaging accuracy is improved, but measurement precision deteriorates due to sensitivity to mud slowness and mud density errors
Solution Approach 1:
The patent combines the inversion of mud slowness, mud density, and formation anisotropy parameters into a single joint inversion process. Instead of treating these parameters separately, the method simultaneously inverts all three from the acoustic logging measurements, allowing the constraints from multiple parameters to mutually reinforce each other and reduce individual parameter uncertainties.
Solution Approach 2:
The patent extends the inversion from traditional single-parameter or two-parameter inversions to a three-dimensional parameter space involving mud slowness, mud density, and formation anisotropy. This additional dimensional approach allows the system to resolve ambiguities that would be present in lower-dimensional inversions by distributing information across multiple parameters.
2Ease of manufacture
If small errors in mud slowness and mud density are present, then processing is simplified, but anisotropy estimation accuracy deteriorates significantly
Solution Approach 1:
The joint inversion process implements a feedback mechanism where the estimated values of mud slowness, mud density, and anisotropy are continuously refined based on their mutual relationships. The inversion algorithm iteratively adjusts all three parameters, using the feedback from how changes in one parameter affect the others, to converge on a consistent solution that satisfies all measurement constraints simultaneously.
Solution Approach 2:
The patent fundamentally changes the approach from using fixed or assumed mud parameters to treating them as variable parameters to be determined from the data. By allowing mud slowness and mud density to vary and be inverted from the acoustic measurements, the method adapts the parameters to match the actual subsurface conditions rather than forcing the data to conform to assumed parameter values.
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 improves the accuracy of anisotropy measurements and reduces errors in mud slowness and density estimation, enhancing the precision of seismic imaging and reservoir characterization.
Implementation Method 1
Acoustic logging tools provide measurements of acoustic wave propagation speeds through the formation
Implementation Method 2
Acoustic logging measurements are also valuable for determining the velocity structure of subsurface formations
Implementation Method 3
inverting these curves to jointly estimate shear wave anisotropy, vertical shear wave slowness, mud slowness, and mud density
Data Source
AI summary
An acoustic logging method that may comprise acquiring waveforms for multiple acoustic wave modes as a function of tool position in a borehole; deriving position-dependent mode dispersion curves from the waveforms; accessing a computed library of dispersion curves for a vertical shear slowness (s) and a Thomsen gamma (γ) of a given acoustic wave mode as a function of frequency; interpolating dispersion curves in the computed library to an assumed known compressional wave slowness, a borehole radius, a formation density, a mud density, and a mud slowness; computing an adaptive weight; and inverting dispersion curve modes jointly for a shear wave anisotropy, a vertical shear wave slowness, an inverted mud slowness, and an inverted mud density as a function of depth. An acoustic logging system may comprise a logging tool, a conveyance attached to the logging tool, at least one sensor, and at least one processor.