Method of correcting nonunimodality of dipmeter traces by uniquely transforming individual traces or intervals

• US 4,939,649 A
• Filed: 07/29/1988
• Issued: 07/03/1990
• Est. Priority Date: 07/29/1988
• Status: Expired due to Fees

• Current Assignee: Amoco Corporation (BP PLC)
• Original Assignee: Amoco Corporation (BP PLC)
• Inventors: Duffy, John A.
• Primary Examiner(s): Smith, Jerry
• Assistant Examiner(s): Hayes, Gail O.
• Application Number:
  US07/226,353
• Time in Patent Office: 704 Days
• Field of Search: 364/421-422, 324/333
• US Class Current: 702/10
• CPC Class Codes: E21B 47/026 of penetrated ground layers

Abstract

A set of dipmeter data comprising monunimodal-symmetric datasets is processed prior to correlation and dip computation and display by transforming the nonunimodal-symmetric datasets into unimodal-symmetric datasets while maintaining the subsets which are already unimodal-symmetric.

Claims

1. A method for preprocessing dipmeter traces for subsequent correlation, dip computation, and display of dip angle and direction of structural features intersecting a borehole, the method comprising:

transforming data in a first trace using transformation(s) selected for the first trace;

transforming data in a second trace using transformation(s) selected for the second trace;

wherein the first trace and the second trace are traces in a set of dipmeter traces obtained during one traverse of a dipmeter along a borehole;

wherein the transformation(s) selected for the first trace differ from transformation(s) selected for the second trace; and

wherein the thus transformed first trace and second trace are characterized by data distribution(s) having symmetry and optionally modality more closely approximating a normal data distribution relative respectively to the first and second traces prior to transformation.

2. The method of claim 1

wherein the thus transformed first trace and second trace are characterized by data distributions having a modality more closely approximating a normal data distribution.

3. The method of claim 1

wherein the step of transforming data in a first trace comprises scanning the first trace for intervals having data distributions not having generally unimodal-symmetric characteristics of a normal data distribution and transforming said intervals to provide data distributions therein which have symmetry and unimodality more closely approximating a normal data distribution;

and

wherein the step of transforming data in a second trace comprises scanning the second trace for intervals having data distributions not having generally unimodal-symmetric characteristics of a normal data distribution and transforming said intervals to provide data distributions therein which have symmetry and unimodality more closely approximating a normal distribution.

4. The method of claim 1 further comprising removing structural disinformation from trace(s) prior to such transforming.

6. The method of claim 1 further comprising:

generating significantly correlated displacement for pairs of traces; and

computing and displaying dip angle and direction of formations adjacent the borehole from the significantly correlated displacements.

5. The method of claim I wherein the step of transforming data in a first trace comprises:

scanning the first trace for interval(s) having a data distribution not having generally unimodal-symmetric characteristics of a normal data distribution;

resolving such intervals into further subpopulations; and

transforming such further subpopulations to provide resulting data distribution(s) having symmetry and unimodality more closely approximating a normal data distribution; and

wherein the step of transforming data in a second trace comprises scanning the second trace for interval(s) having data distribution(s) not having generally unimodal-symmetric characteristics of a normal data distribution;

resolving such intervals into further subpopulations; and

transforming such further subpopulations to provide resulting data distribution(s) having symmetry and unimodality more closely approximating a normal data distribution.