Probablistic subsurface modeling for improved drill control and real-time correction

Patent metadata

Field	Value
Publication number	US-9291735-B2
Application number	US-201213362754-A
Country	US
Kind code	B2
Filing date	Jan 31, 2012
Priority date	Jun 10, 2011
Publication date	Mar 22, 2016
Grant date	Mar 22, 2016

Abstract

A system, method and computer program product for generating probabilistic models of a subsurface region of the earth of interest. The system, method can be implemented efficiently to enable real-time imaging of a sub-surface structure. The system, method can provide users with the ability to assess where their subsurface images are reliable and where they are not in order to assist in the selection of low-risk, high-reward sights indicated as having oil potential for drilling. The system, method allows users to estimate a degree of uncertainty to be expected when drilling in a specific location. The knowledge of this uncertainly can be used to guide drilling in real-time to reduce the time to oil (and thereby the cost of drilling), increase the efficiency of drill maintenance and reduce the risk associated with incorrectly identifying the depth at which pressure might spike.

First claim

Having thus described our invention, what we claim as new, and desire to secure by Letters Patent is:

1. A method for creating seismic models of subsurface structures comprising:
collecting survey shot data of a sub-surface area of interest, said area having sub-surface structure;
using the collected shot data and one or more seismic modeling algorithms to generate a set of models describing the sub-surface structure of a part or the entire area;
for each of said generated models, conducting, in a computer system, a forward modeling simulation of said generated model to generate shot data from the model, the conducting including:
dividing the forward modeling simulation across a plurality of computing nodes, each computing node processing a portion of the forward modeling simulation of the said generated model;
combining partial images, which are results of the processing from the plurality of nodes, the combined partial images representing the generated shot data;

measuring the error value between the collected survey shot data and the generated shot data from the simulated model;
computing, using a programmed processor device, a certainty measure from the error value to assess a confidence degree of the generated model; and further
computing an objective function based on the generated model as a function of said collected survey shot data and a set of model parameters, said objective function defined as a set of partial derivative equations; and
iteratively solving said set of partial derivative equations to reach an optimal set of model parameters for use in the generated model, and
measuring a progress of an optimization process after each iteration according to

P
k

-

P

k
-
1

P

k
-
1

>
T

where k is an index of iterations, and a difference between an immediately past probability measure of a prior iteration, P k−1 and a current probability measure of a current iteration P k is compared against a fixed number threshold T, such that if an improvement in probability is greater than T, then adjusting the model parameters, incrementing index k and begin the next iteration; otherwise, if an improvement in probability is less than T, then terminate the modeling process.

2. The method as claimed in claim 1 , wherein said computed certain measure of the generated model indicates areas having a low degree of certainty of the sub-surface structure and areas having high degrees of certainty of the sub-surface structure.

3. The method as claimed in claim 2 , wherein an area having a high degree of certainty relates to a potential presence of oil in or beneath said sub-surface structure.

4. The method as claimed in claim 3 , further comprising:
determining, by a programmed processor device, a set of drilling instructions for obtaining said oil at said location in an area indicating high degrees of certainty of the sub-surface structure, said drilling instructions including one or more of: a drilling location and a drilling direction.

5. The method as claimed in claim 4 , further comprising:
performing a drilling process at said location according to said drilling instructions; and
collecting in real-time, while performing said drilling process, data representing said sub-surface structure.

6. The method as claimed in claim 5 , further comprising:
comparing the shot data collected with the shot data generated from the simulation; and
using comparison results of said comparing to update said model.

7. The method as claimed in claim 5 , further comprising,
comparing the shot data collected with the shot data generated from the simulation; and
using comparison results of said comparing to derive new drilling instructions.

8. The method as claimed in claim 6 , further comprising,
updating the error value and the certainty measure based on said updated model describing an updated subsurface structure, and,
re-evaluating the updated subsurface structure and certainty of the updated model for determining oil potential in said area.

9. The method as claimed in claim 1 , wherein said computing the certain measure includes using a linear function or a nonlinear function relating a probability measure to said error.

10. The method as claimed in claim 1 , further comprising:
generating a minimization of an error function of the survey collected data and the model, said model being a function of model parameters; and,
solving, using said programmed processor device, said error minimization function with respect to the model parameters according to one or more partial derivative equations.

11. The method as claimed in claim 1 , further comprising:
generating a maximization of a probability function of the survey collected data and the model, said model being a function of model parameters; and,
solving, using said programmed processor device, said probability maximization function with respect to the model parameters according to one or more partial derivative equations.

12. A system for creating seismic models of subsurface structures comprising:
one or more processor devices; a local memory storage device associated with at least one said processor device, wherein each processor device is configured to perform a method to:
store, at the associated local memory storage device, collected survey shot data of a subsurface area of interest, said area having a sub-surface structure,
apply one or more seismic modeling algorithms using the collected shot data to generate a set of models describing the sub-surface structure of a part or the entire area;
conduct a forward modeling simulation of each generated model to generate shot data from a generated model, the conducting including
divide the forward modeling simulation across a plurality of computing nodes, each computing node processing a portion of the forward modeling simulation of the generated model;
combine partial images, which are results of the processing from the plurality of nodes, the combined partial images representing the generated shot data;

measure the error value between the collected survey shot data and the generated shot data from the simulated model; and,
compute a certainty value from the error measure to assess a confidence degree of the generated model; and further
compute an objective function based on the generated model as a function of said collected survey shot data and a set of model parameters, said objective function defined as a set of partial derivative equations; and
iteratively solve said set of partial derivative equations to reach an optimal set of model parameters for use in the generated model, and
measure a progress of an optimization process after each iteration according to

P
k

-

P

k
-
1

Assignees

• Lu Ligang
• Perrone Michael P
• Globalfoundries Inc

Inventors

• Lu Ligang
• Perrone Michael P

Classifications

• G01V99/00 Primary

  Subject matter not provided for in other groups of this subclass · CPC title

• G01V1/282 Primary

  Application of seismic models, synthetic seismograms · CPC title

• E21B44/00

  Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions · CPC title

• G01V2210/667

  Determining confidence or uncertainty in parameters · CPC title

• G01V2210/614

  Synthetically generated data · CPC title