Method of modelling the production of an oil reservoir

• US 20060047489A1
• Filed: 08/22/2005
• Published: 03/02/2006
• Est. Priority Date: 08/30/2004
• Status: Active Grant

• Current Assignee: Institut Francais Du Petrole (IFP Energies Nouvelles)
• Original Assignee: Institut Francais Du Petrole (IFP Energies Nouvelles)
• Inventors: Scheidt, Celine, Zabalza-Mezghani, Isabelle, Feraille, Mathieu, Collombier, Dominique
• Granted Patent: US 7,788,074 B2
• Time in Patent Office: Days
• US Class Current: 703/10
• CPC Class Codes: E21B 43/00 Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells applicable only to water E03B

Abstract

The invention stimulates the production of an oil reservoir by carrying out a sequence of steps of constructing a flow simulator from physical data measured in the oil reservoir; determining a first analytical model relating the production of the reservoir as a function of time by taking account of parameters having an influence on the production of the reservoir, the first model best adjusting to a finite number of production values obtained by the reservoir simulator; selecting at least one new production value, this new value being obtained by the reservoir simulator; and determining a second model by adjusting the first model so that the second model interpolates the new production value.

Claims

1. ) A method for simulating the production of an oil reservoir, comprising:

a) constructing a flow simulator from physical data measured in the oil reservoir;

b) determining a first analytical model expressing production of the reservoir as a function of time by taking into account parameters having an influence on the production of the reservoir, the first model best adjusting to a finite number of production values obtained by the flow simulator;

c) selecting at least one new production value associated with a point located in an area of the reservoir selected as a function of the non-linearity of the reservoir production in the area, the new value being obtained by the flow simulator; and

d) determining a second model by adjusting the first model so that the response of the second model at the point corresponds to the new production value.

2. ) A method as claimed in claim 1 wherein, in step c), the following steps are carried out:

determining a sub-model that best adjusts to the finite number of production values, except for a test value selected from among the finite number of production values;

calculating a prediction residue associated with the test value by carrying out the difference between the response of the sub-model and the test value;

calculating a prediction residue associated with each one of the prediction values by repeating determining a sub-model and calculating a prediction residue by assigning successively to the test value each one of the values contained within said finite number of production values; and

selecting a new production value in an area of the reservoir close to a point associated with a production value having a greatest prediction residue.

3. ) A method as claimed in claim 2, wherein the new production value is selected by taking into account of a production gradient at a point associated with a production value having a greatest prediction residue.

4. ) A method as claimed in claim 2, wherein a new value is selected in step c) and step d) is carried out, provided that a greatest prediction residue is greater than a previously set value.

5. ) A method as claimed in claim 1 wherein, in step c), the following steps are carried out:

determining a first kriging variance of the first model for the finite number of production values obtained by the flow simulator;

selecting a first pilot point in the reservoir in the place where the first kriging variance is maximum

determining a second kriging variance of the first model for the finite number of production values obtained by the flow simulator and the first pilot point;

selecting a second pilot point in the reservoir in the place where the second kriging variance is maximum; and

assigning a value to each one of the pilot points by carrying out the following five operations for each pilot point;

(1) determining a sub-model that best adjusts to a finite number of production values and to a value associated with one of the pilot points, except for a test value selected from among a finite number of production values and a value associated with the pilot point;

(2) calculating a prediction residue associated with a test value by carrying out a difference between the response of the sub-model and the test value;

(3) calculating a prediction residue associated with each one of the sub-model responses by repeating the determining a sub-model and calculating a prediction residue by assigning successively to the test value each one of the values contained in the set of the finite number of production values and the value associated with the pilot point;

(4) calculating a sum of absolute values of prediction residues calculated for each test value; and

(5) assigning to the pilot point the value that minimizes the sum, determining a second sub-model that best adjusts to the finite number of production values and to the values of the pilot points, for each pilot point, carrying out the difference between a response of the second sub-model and a response of the first model;

associating the new production value of step c) with a pilot point for which the difference is greatest.

6. ) A method as claimed in claim 5 wherein, in step d), the second model is determined by adjusting the first model so that the response of the second model at said pilot point selected corresponds to the new production value and, furthermore, to the values assigned to the other pilot points.

7. ) A method as claimed in claim 1 wherein, in step c), the following steps are carried out:

determining an analytical model expressing the derivative of reservoir production as a function of time, the model best adjusting to the derivatives at points associated with the production values used in step b); and

from the model expressing the derivative, selecting at least one new production value associated with a point whose response of the model expressing the derivative is zero.

8. ) a method as claimed in claim 7, wherein a new value is selected in step c) and step d) is carried out, provided that a prediction residue of the new value selected is greater than a previously set value.

9. ) A method as claimed in claim 7 wherein, after step d), the following stages are carried out:

determining a third analytical model expressing the derivative of the reservoir production as a function of time, the third model best adjusting to the derivatives at the points associated with said finite number of production values and the production values selected in step c);

if the response of the third analytical model at the point selected in step c) is greater than zero, determining a point associated with the maximum value of the response of the second model in the vicinity of the point selected in step c);

if the response of the third analytical model at the point selected in step c) is less than zero, determining a point associated with the minimum value of the response of the second model in the vicinity of the point selected in step c);

determining a new production value by the flow simulator at the point associated with the previously determined minimum or maximum value; and

determining a fourth model by adjusting the second model so that the response of the fourth model corresponds to a new value determined in the previous step.

10. ) A method as claimed in claim 1 wherein steps c) and d) are repeated.

11. ) A method as claimed in claim 1 wherein, in step b), the production values are selected using an experimental design.

12. ) A method as claimed in claim 1 wherein, in step b), the first model is adjusted using one of the following approximation methods:

polynomial approximation, neural networks, support vector machines.

13. ) A method as claimed in claim 1 wherein, in step d), one of the following interpolation methods is used:

kriging method and spline method.