JOINT INVERSION OF TIME DOMAIN CONTROLLED SOURCE ELECTROMAGNETIC (TD-CSEM) DATA AND FURTHER DATA

• US 20090254320A1
• Filed: 03/27/2009
• Published: 10/08/2009
• Est. Priority Date: 04/07/2008
• Status: Active Grant

• Current Assignee: WesternGeco LLC
• Original Assignee: WesternGeco LLC
• Inventors: Lovatini, Andrea, Rovetta, Diego, Mazzucchelli, Paolo, Bernasconi, Giancarlo, Watts, Michael D.
• Granted Patent: US 8,275,592 B2
• Time in Patent Office: Days
• US Class Current: 703/5
• CPC Class Codes: G01V 3/083 Controlled source electromagnetic [CSEM] surveying
• CPC Class Codes: G01V 3/12 operating with electromagnetic waves operating with millimetre waves G01V8/005

Abstract

To perform inversion based on electromagnetic (EM) data acquired in a subterranean survey, time domain controlled source EM (TD-CSEM) data is acquired by at least one EM receiver in response to EM signals from a controlled EM source. Further data is received, where the further data is selected from among magnetotelluric (MT) data and DC data acquired by the at least one receiver. A probabilistic joint inversion is applied on the TD-CSEM data and the further data to produce a model representing a subterranean structure that is a target of the subterranean survey.

Claims

1. A method of performing inversion based on electromagnetic (EM) data acquired in a subterranean survey, comprising:

receiving time domain controlled source EM (TD-CSEM) data acquired by at least one EM receiver in response to EM signals from a controlled EM source;

receiving further data selected from among magnetotelluric (MT) data and DC data acquired by the at least one receiver; and

applying probabilistic joint inversion on the TD-CSEM data and the further data to produce a model representing a subterranean structure that is a target of the subterranean survey.

2. The method of claim 1, wherein the probabilistic joint inversion is applied on the TD-CSEM data and DC data.

3. The method of claim 1, wherein the probabilistic joint inversion is applied on the TD-CSEM data and MT data.

4. The method of claim 1, wherein the probabilistic joint inversion is applied on the TD-CSEM data, MT data, and DC data.

5. The method of claim 1, wherein applying the probabilistic joint inversion is performed iteratively until a stopping criterion is satisfied.

6. The method of claim 5, wherein repeated iterative performance of the probabilistic joint inversion produces corresponding solutions for the model, the method further comprising determining if the stopping criterion is satisfied based on comparing a current solution of the model with a prior solution of the model.

7. The method of claim 1, wherein applying the probabilistic joint inversion uses at least one probability density that indicates uncertainties associated with observed data and/or model parameters, wherein the observed data includes at least the TD-CSEM data.

8. The method of claim 1, wherein applying the probabilistic joint inversion uses a probability density that indicates uncertainties associated with observed data and a probability density that indicates uncertainties associated with model parameters, wherein the observed data includes at least the TD-CSEM data.

9. The method of claim 8, wherein applying the probabilistic joint inversion further comprises representing a solution of an inverse problem of the probabilistic joint inversion using a posterior probability density that combines the probability densities with information obtained from a forward model representing the subterranean structure.

10. The method of claim 1, wherein producing the model comprises producing a one-dimensional model.

11. The method of claim 1, wherein producing the model comprises producing a two-dimensional model or three-dimensional model.

12. An article comprising at least one computer-readable storage medium containing instructions that, when executed, cause a computer to:

receive time domain controlled source electromagnetic (TD-CSEM) data acquired by at least one EM receiver in response to EM signals from a controlled EM source;

receive further data selected from among magnetotelluric (MT) data and DC data acquired by the at least one receiver; and

apply probabilistic joint inversion on the TD-CSEM data and the further data to produce a model representing a subterranean structure that is a target of the subterranean survey, wherein the probabilistic joint inversion uses a first probability density that represents uncertainties associated with observed data including at least the TD-CSEM data.

13. The article of claim 12, wherein applying the probabilistic joint inversion is performed iteratively until a stopping criterion is satisfied.

14. The article of claim 12, wherein applying the probabilistic joint inversion comprises applying the probabilistic joint inversion that further uses a second probability density that represents uncertainties associated with parameters of the model.

15. The article of claim 12, wherein the model comprises one of a one-dimensional model, a two-dimensional model, and a three-dimensional model.

16. A computer comprising:

a processor; and

processing software executable on the processor to;

receive electromagnetic (EM) data acquired by at least one EM receiver in response to EM signals from a controlled EM source;

receive further data selected from among magnetotelluric (MT) data and DC data acquired by the at least one receiver; and

apply probabilistic joint inversion on the TD-CSEM data and the further data to produce a model representing a subterranean structure that is a target of the subterranean survey.

17. The computer of claim 16, wherein the received EM data comprises time-domain controlled source EM data.

18. The computer of claim 16, wherein the probabilistic joint inversion uses probability densities that represent uncertainties associated with parameters of the model and observed data including at least the EM data.

19. The computer of claim 18, wherein the probabilistic joint inversion comprises representing a solution of an inverse problem of the probabilistic joint inversion using a posterior probability density that combines the probability densities with information obtained from a forward model representing the subterranean structure.