Method of predicting three-dimensional stratigraphy using inverse optimization techniques

• US 20020099504A1
• Filed: 05/15/2001
• Published: 07/25/2002
• Est. Priority Date: 01/29/1999
• Status: Active Grant

• Current Assignee: Platte River Associates, Inc.
• Original Assignee: Platte River Associates, Inc.
• Inventors: Cross, Timothy A., Lessenger, Margaret A.
• Granted Patent: US 6,754,588 B2
• Time in Patent Office: Days
• US Class Current: 702/2
• CPC Class Codes: G01V 11/00 Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
• CPC Class Codes: G01V 2210/66 Subsurface modeling

Abstract

The invention relates generally to the field of quantitative sedimentologic and stratigraphic prediction. Specifically, geological data observed or inferred from core samples, cuttings, geophysical well logs and seismic data from locations within a sedimentary basin, in conjunction with a stratigraphic forward model and an inverse optimization technique, are used to predict sedimentologic and stratigraphic attributes at locations within the basin other than those at which data were collected. The model'"'"'s output consists of a three-dimensional model of the sedimentologic and stratigraphic attributes for the specified basin volume.

Claims

1. A method to predict one or more stratigraphic attributes at a plurality of locations within a three-dimensional volume in a sedimentary basin, the method comprising the steps of:

(a) obtaining a set of real stratigraphic data comprising sedimentologic and stratigraphic attributes from at least one location within the volume;

(b) employing a stratigraphic forward model;

(c) employing a numerical inverse optimization technique that, after an initial guess of a group of forward model parameter values is established, iteratively runs the forward model, compares output from the forward model with similar data types from the set of real data, and adjusts the group of forward model parameter values such that successive iterations more closely match the sedimentologic and stratigraphic attributes from the set of real data until an optimal solution comprising a best combination of forward model parameter values is generated that satisfies a predetermined variance threshold;

(d) inputting the best combination of forward model parameter values back into the forward model to output a complete model of stratigraphic attributes a plurality of locations within the three-dimensional volume.

2. The method of claim 1, wherein the set of real data of step (a) further comprises at least one of the following types:

core samples, cuttings, geophysical well logs and seismic data.

3. The method of claim 1, wherein the set of real data of claim (a) further comprises at least one of the following types of information or inference:

rock type, including one or more compositional, constituent particles, textural, fabric, fossil and petrophysical attributes;

facies thickness and successions;

stratigraphic cycles;

cycle symmetry;

correlations of stratigraphic cycles;

stratigraphic geometry and dimensions of rock bodies;

inferred paleotopography or paleobathymetry;

inferred sea level and/or subsidence history;

lithosphere strength and flexural compensation to loads.

4. The method of claim 1, wherein the forward model of step (b) further comprises a conservation of mass constraint.

5. The method of claim 1, wherein the forward model of claim (b) further comprises a flexure algorithm to compensate for sediment and water loads.

6. The method of claim 1, wherein the numerical inverse optimization technique of step (c) is one of the group consisting of a genetic inverse algorithm, a simulated annealing inverse algorithm, a gradient descent inverse algorithm, a Lerche technique inverse algorithm, or a Monte Carlo algorithm.

7. The method of claim 1, wherein the initial guess of the group of forward model parameter values of step (c) further comprises scale and volume limitations ranging from reservoir size to basin size.

8. The method of claim 1, wherein the forward model of step (b) is a carbonate model.

9. The method of claim 8, wherein the forward model of step (b) further comprises a mathematical function that describes the distribution of kinetic and potential energy on a sea floor as a function of sea swell impinging upon and crossing a shelf, dissipation of kinetic energy through friction with the sea floor, and increase of kinetic energy as a function of shelf width.

10. The method of claim 8, wherein the forward model of step (b) further comprises a mathematical relationship between the summation of potential and kinetic within the volume and the biologic energy within the volume.

11. The method of claim 8, wherein the forward model of step (b) further comprises a mathematical function that relates carbonate productivity to water depth, kinetic energy on the sea floor, distance from shore and ecologic reproduction strategy.

12. The method of claim 8, wherein the forward model of step (b) further comprises a mathematical relationship that describes sediment redistribution and deposition on the sea floor as a function of kinetic and potential energy.

13. The method of claim 8, wherein the set of real data of step (a) further comprises at least one of the following:

inferred water depth;

sediment texture;

organic and inorganic constituent particles;

sediment fabric;

facies successions and thickness;

stratigraphic cycles;

cycle symmetry;

correlated stratigraphic units;

spatial position of ecologic units;

spatial position of paleoenvironmental or paleogeographic units;

stratigraphic and geographic position of subaerial exposure surfaces;

stratigraphic and geographic position of marine hardground surfaces.