Accurate and cost-effective inversion-based auto calibration methods for resistivity logging tools

Patent metadata

Field	Value
Publication number	US-11940587-B2
Application number	US-202117409518-A
Country	US
Kind code	B2
Filing date	Aug 23, 2021
Priority date	Aug 23, 2021
Publication date	Mar 26, 2024
Grant date	Mar 26, 2024

Abstract

Systems and methods of the present disclosure relate to calibration of resistivity logging tool. A method to calibrate a resistivity logging tool comprises disposing the resistivity logging tool into a formation; acquiring a signal at each logging point with the resistivity logging tool; assuming a formation model for a first set of continuous logging points in the formation; inverting all of the signals for unknown model parameters of the formation model, wherein the formation model is the same for all of the continuous logging points in the first set; assigning at least one calibration coefficient to each type of signal, wherein the calibration coefficients are the same for the first set; and building an unknown vector that includes the unknown model parameters and the calibration coefficients, to calibrate the resistivity logging tool.

First claim

What is claimed is:

1. A method to calibrate a resistivity logging tool, comprising:
disposing the resistivity logging tool into a formation, wherein the resistivity logging tool comprises a transmitter and a receiver;
transmitting a signal with the transmitter;
acquiring a return signal at each logging point with the receiver;
assuming a formation model for a first set of continuous logging points in the formation;
assigning at least one calibration coefficient to each type of signal;
building an unknown vector that includes unknown model parameters and the calibration coefficients; and
inverting the unknown vector that includes the calibration coefficients and the unknown model parameters of the formation model, to calibrate the resistivity logging tool.

2. The method of claim 1 , further comprising assigning an initial guess or constraint for the unknown vector.

3. The method of claim 2 , further comprising simulating a response with the initial guess or the constraint.

4. The method of claim 3 , further comprising calculating a misfit between a simulated response and a measured response.

5. The method of claim 4 , further comprising outputting a model parameter and a calibrated signal, wherein the misfit is below a threshold.

6. The method of claim 4 , further comprising updating the unknown model parameters and the calibration coefficient based on deterministic or indeterministic optimization, wherein the misfit exceeds a threshold.

7. The method of claim 6 , further comprising assigning an initial guess or constraint for the unknown vector.

8. The method of claim 1 , wherein the calibration coefficients are not constant for a complete field test.

9. The method of claim 1 , further comprising producing the calibration coefficient in real time.

10. The method of claim 1 , wherein assuming the same formation model comprises assuming a stratified layer model, a 2-dimensional model, or a 3-dimensional model.

11. The method of claim 1 , further comprising acquiring signals for a second set of continuous logging points with the resistivity logging tool.

12. The method of claim 1 , further comprising assuming a second formation model for the second set of continuous logging points in the formation.

13. A system for calibrating a resistivity logging tool, comprising:
the resistivity logging tool operable to acquire signals at continuous logging points in a formation, wherein the resistivity logging tool comprises a transmitter configured to transmit a signal and a receiver configured to receive a return signal; and
a computer operable to:
receive a signal from the receiver at each logging point via the resistivity logging tool;
input the signals into a formation model;
assign at least one calibration coefficient to each type of signal;
build an unknown vector that includes unknown model parameters and the calibration coefficients; and
invert the unknown vector that includes the calibration coefficients and the unknown model parameters of the formation model, to calibrate the resistivity logging tool.

14. The system of claim 13 , wherein the computer is further operable to assign an initial guess or constraint for the unknown vector.

15. The system of claim 14 , further comprising simulating a response with the initial guess or the constraint.

16. The system of claim 15 , wherein the computer is further operable to calculate a misfit between a simulated response and a measured response.

17. The system of claim 16 , wherein the computer is further operable to output a calibrated signal, wherein the misfit is below a threshold.

18. The system of claim 16 , wherein the computer is further operable to update the unknown model parameters and the calibration coefficients based on deterministic or indeterministic optimization, wherein the misfit exceeds a threshold.

19. The system of claim 18 , wherein the calibration coefficients assigned to each type of signals are the same for the continuous logging points.

20. The system of claim 19 , wherein the formation model is the same for all of the continuous logging points.

Assignees

• Halliburton Energy Services Inc

Inventors

• Pan Li
• Fan yi jing
• Wu Hsu-Hsiang
• Ma Jin

Classifications

• G01V3/28 Primary

  using induction coils · CPC title

• G01V1/50

  Analysing data · CPC title

• G01V13/00

  Manufacturing, calibrating, cleaning, or repairing instruments or devices covered by groups G01V1/00 – G01V11/00 · CPC title

• G01V3/08 Primary

  operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices (with electromagnetic waves G01V3/12) · CPC title

• G01V3/22 Primary

  using DC · CPC title