Systems and methods for the quantitative estimate of production-forecast uncertainty

Patent metadata

Field	Value
Publication number	US-10436940-B2
Application number	US-201514928435-A
Country	US
Kind code	B2
Filing date	Oct 30, 2015
Priority date	Sep 25, 2009
Publication date	Oct 8, 2019
Grant date	Oct 8, 2019

Abstract

Systems and methods for updating posterior geological models by integrating various reservoir data to support dynamic-quantitative data-inversion, stochastic-uncertainty-management and smart reservoir-management.

First claim

The invention claimed is:

1. A computer-implemented method, comprising:
computing, using one or more processors, an exact likelihood of objective function using one or more new geological realizations for a prior geological model;
defining an initial state for a sequential Monte Carlo chain based on the exact likelihood of objective function;
defining a new sample based on the initial state for a sequential Monte Carlo chain and a random sample from the prior geological model;
computing an approximate likelihood of objective function using the new sample;
repeating the step of defining a new sample based only on another random sample from the prior geological model if the approximate likelihood of objective function does not meet an acceptance criteria;
computing another exact likelihood of objective function using the new sample if the new sample meets the acceptance criteria;
repeating the step of defining a new sample based only on another random sample from the prior geological model if the another exact likelihood of objective function does not meet another acceptance criteria;
repeating the step of defining a new sample based only on another random sample from the prior geological model until a convergence criteria is met;
storing each new sample that meets the acceptance criteria and the another acceptance criteria, each new sample representing a respective updated posterior geological model for the prior geological model, and each updated posterior geological model being generated using a non-linear technique;
performing a full-physics simulation for each updated posterior geological model;
dynamically ranking each updated posterior geological model based on the full-physics simulation;
parameterizing each updated posterior geological model, the parameterization using a Discrete Cosine Transform (DCT) to characterize the updated posterior geological model in terms of one or more parameters; and
displaying a three-dimensional (3D) petrophysical realization using the one or more parameters of each parameterized updated posterior model, the use of the one or more parameters to display the 3D petrophysical realization causing a computational load processed at the one or more processors to be reduced.

2. The method of claim 1 , further comprising defining a number (N) of new geological realizations to compute for the prior geological model.

3. The method of claim 1 , wherein the new geological realizations are computed using parameters for the prior geological model.

4. The method of claim 1 , further comprising:
evaluating the ranked updated posterior geological models; and
executing a business decision based on the evaluation.

5. The method of claim 1 , further comprising:
dynamically ranking the new geological realizations; and
selecting an acceptable number of the new geological realizations based upon the dynamic ranking of the new geological realizations.

6. The method of claim 1 , further comprising:
dynamically ranking each updated posterior geological model;
selecting a best ranked updated posterior geological model;
replacing the prior geological model with the best ranked updated posterior geological model;
renaming the best ranked updated posterior geological model as the prior geological model;
defining a number (N) of new geological realizations to compute for the prior geological model; and
repeating the steps of claim 1 .

7. The method of claim 1 , further comprising executing a business decision based on each updated posterior geological model.

8. The method of claim 6 , wherein the best ranked updated posterior geological model represents a highest ranked updated posterior geological model.

9. The method of claim 1 , wherein each updated posterior geological model is ranked by an ultimate recovery factor corresponding to each respective updated posterior geological model.

10. A non-transitory computer-readable medium for storing computer executable instructions for updating posterior geological models, the instructions being executable to implement:
computing an exact likelihood of objective function using one or more new geological realizations for a prior geological model;
defining an initial state for a sequential Monte Carlo chain based on the exact likelihood of objective function;
defining a new sample based on the initial state for a sequential Monte Carlo chain and a random sample from the prior geological model;
computing an approximate likelihood of objective function using the new sample;
repeating the step of defining a new sample based only on another random sample from the prior geological model if the approximate likelihood of objective function does not meet an acceptance criteria;
computing another exact likelihood of objective function using the new sample if the new sample meets the acceptance criteria;
repeating the step of defining a new sample based only on another random sample from the prior geological model if the another exact likelihood of objective function does not meet another acceptance criteria;
repeating the step of defining a new sample based only on another random sample from the prior geological model until a convergence criteria is met;
storing each new sample that meets the acceptance criteria and the another acceptance criteria, each new sample representing a respective updated posterior geological model for the prior geological model, and each updated posterior geological model being generated using a non-linear technique;
performing a full-physics simulation for each updated posterior geological model; and
dynamically ranking each updated posterior geological model based on the full-physics simulation;
parameterizing each updated posterior geological model, the parameterization using a Discrete Cosine Transform (DCT) to characterize the updated posterior geological model in terms of one or more parameters; and
displaying a three-dimensional (3D) petrophysical realization using the one or more parameters of each parameterized updated posterior model, the use of the one or more parameters to display the 3D petrophysical realization causing a computational load processed at one or more processors to be reduced.

11. The non-transitory computer-readable medium of claim 10 , further comprising defining a number (N) of new geological realizations to compute for the prior geological model.

12. The non-transitory computer-readable medium of claim 10 , wherein the new geological realizations are computed using parameters for the prior geological model.

13. The non-transitory computer-readable medium of claim 10 , further comprising:
evaluating the ranked updated posterior geological models; and
executing a business decision based on the evaluation.

14. The non-transitory computer-readable medium of claim 10 , further comprising:
dynamically ranking the new geological realizations; and
selecting an acceptable number of the new geological realizations based upon the dynamic ranking of the new geological realizations.

15. The non-transitory computer-readable medium of claim 10 , further comprising:
dynamically ranking each updated posterior geological model;
selecting a best ranked updated posterior geological model;
replacing the prior geological model with the best ranked updated posterior geological model;
renaming the best ranked updated posterior geological model as the prior geological model;
defining a number (N) of new geological realizations to compute for the prior geological model; and
repeating the steps of claim 10 .

16. The non-transitory computer-readable medium of

Assignees

• Landmark Graphics Corp

Inventors

• Maucec Marko
• Cullick Alvin Stanley

Classifications

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06G7/48

  Analogue computers for specific processes, systems or devices, e.g. simulators · CPC title

• G01V1/30 Primary

  Analysis (G01V1/50 takes precedence) · CPC title

• G06G7/60

  for living beings, e.g. their nervous systems {; for problems in the medical field} · CPC title

• G01V99/005 Primary

  Physics · mapped topic