High Resolution LWD Imaging via Deconvolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current borehole imaging techniques, such as LWD, often result in low-resolution images due to data averaging, which limits the ability to detect fine geological features like fractures and bedding layers, especially in non-conductive drilling fluids.
Innovation Solution
The method involves receiving LWD data, mapping it to a selected borehole grid with interpolation to fill empty pixels, using a Gaussian smoothing operation to compute weighted averages of nearest neighbor pixel values, and applying image enhancement techniques like histogram equalization to produce high-resolution images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data averaging techniques (binning or sectoring) are used to reduce statistical variations and enable real-time transmission, then image transmission capability and noise reduction are improved, but high spatial frequency content (fine geological structure) is irretrievably destroyed
Solution Approach 1:
The patent performs preliminary actions by collecting raw LWD data with high spatial frequency content preserved, then applies deconvolution processing to reconstruct fine details before final image formation. This preliminary reconstruction step recovers the high spatial frequency information that would otherwise be lost in traditional averaging methods.
Solution Approach 2:
The patent creates a mathematical model (point spread function) that represents the blurring effect of data averaging, then uses this model to generate a deconvolved image that copies and enhances the original fine details. The deconvolution process essentially creates a corrected copy of the image that restores the high spatial frequency content.
2Measurement precision
If microresistivity imaging techniques are used to obtain high resolution images, then fine formation features can be detected, but the presence of non-conductive drilling fluid or thin non-conductive films severely impedes current flow and degrades image quality
Solution Approach 1:
The patent introduces an intermediary mathematical model (point spread function) that characterizes the measurement system's response. This model serves as a mediator between the raw data and the final image, enabling deconvolution processing that recovers fine details without requiring actual current flow through the non-conductive drilling fluid.
Solution Approach 2:
The patent replaces the mechanical/electrical current flow mechanism with a mathematical signal processing approach. Instead of relying on electrical current to penetrate non-conductive drilling fluid, the invention uses deconvolution algorithms to reconstruct fine details from the available data, substituting the physical measurement mechanism with a computational one.
3Productivity
If conventional LWD imaging is used in non-conductive drilling fluid, then drilling operations can proceed, but image quality is severely degraded due to impeded current flow
Solution Approach 1:
The patent creates a mathematical representation (point spread function) of the measurement system's behavior in non-conductive fluid, then uses this copy/model to deconvolve the data and recover image quality. This allows conventional LWD operations to continue in non-conductive fluid while maintaining image quality through post-processing.
Data Source
AI summary
A method for forming a high resolution logging while drilling image includes receiving a logging while drilling data set and selecting a borehole grid. The borehole grid includes a plurality of pixels that define discrete azimuthal positions and depth positions in the borehole. The LWD data set is mapped to the selected borehole grid such that an azimuthal position and a depth position are assigned to each data point in the data set. An interpolation routine is used to fill empty pixels. Disclosed embodiments enable high resolution LWD imaging that may resolve various fine formation features such as fractures, fine bedding layers, breakout, and vugs.