Automated Mud Slowness Estimation via Acoustic Logging
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Solution Overview
Problem
Current techniques for measuring fluid slowness in acoustic logging are inaccurate and time-consuming, especially in slow formations, and lack a practical method for determining mud slowness at sonic frequencies.
Innovation Solution
A method and apparatus that calculate fluid slowness using monopole radial profiling if conditions permit, and Scholte wave slowness otherwise, integrating both techniques into an automated framework for both fast and slow formations, with mud slowness from Scholte waves used as prior information for probabilistic estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If indirect evaluation techniques (mud samples or manufacturer data) are used to estimate mud slowness, then the estimation process is simple, but the accuracy is poor due to differences in pressure, temperature, and gas presence between surface and downhole conditions
Solution Approach 1:
The patent uses Stoneley wave dispersion characteristics as an intermediary to indirectly determine mud slowness. The Stoneley wave, which is a borehole mode sensitive to both formation and mud properties, serves as a mediator that connects the measurable acoustic response to the desired mud slowness parameter, overcoming the limitations of direct sampling and empirical equations
Solution Approach 2:
The patent replaces mechanical/physical sampling methods (mud samples) and empirical calculation methods with an acoustic wave-based measurement system. By using Stoneley wave dispersion analysis, the system substitutes direct physical measurement with indirect acoustic characterization, enabling accurate downhole mud slowness estimation without actual mud sampling
2Measurement precision
If Prony-based dispersion analysis using Stoneley or flexural modes is used to directly evaluate mud slowness, then the accuracy can be improved, but the process becomes time-consuming and requires skilled personnel for manual analysis
Solution Approach 1:
The patent implements an automated system that performs the dispersion analysis without requiring skilled personnel. The processing circuitry automatically extracts Stoneley wave characteristics, performs dispersion analysis, and determines mud slowness, making the system self-sufficient and eliminating the need for expert manual intervention
Solution Approach 2:
The patent replaces manual analytical methods with automated digital signal processing. By using processing circuitry to automatically perform Prony-based dispersion analysis on the acoustic data, the system substitutes time-consuming manual techniques with rapid computational methods, maintaining accuracy while dramatically reducing analysis time
3Measurement precision
If manual dispersion analysis by skilled personnel is used, then accurate mud slowness estimation can be achieved, but the complexity of operation increases and productivity decreases
Solution Approach 1:
The patent creates a self-automating system where the processing circuitry independently performs all steps of mud slowness estimation. The system automatically identifies Stoneley wave arrivals, extracts dispersion characteristics, and calculates mud slowness without human intervention, thereby eliminating the trade-off between accuracy and productivity
Solution Approach 2:
The patent substitutes manual expert analysis with automated digital processing algorithms. By implementing computer-based dispersion analysis, the system replaces the need for skilled personnel with automated computational methods, achieving both high accuracy and rapid results simultaneously
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 automates mud slowness estimation, reducing the need for skilled personnel and providing accurate results in both fast and slow formations by combining probabilistic and dispersion analysis techniques.
Implementation Method 1
acoustic wave propagation measurements in a fluid filled borehole
Implementation Method 2
refracted compressional arrival time, refracted shear arrival time
Implementation Method 3
dipole flexural waves in the borehole
Implementation Method 4
the dipole mode is dispersive
Implementation Method 5
Scholte wave slowness
Data Source
AI summary
An integrated framework is described for automating some or all of mud slowness estimation for both fast and slow formations. An estimation of fluid slowness based on monopole radial profiling is calculated if conditions permit. Alternatively, an estimation of fluid slowness based on Scholte wave slowness is estimated if conditions do not permit calculation based on monopole radial profiling. Tool standoff may also be estimated based on monopole radial profiling.