Apparatus, method and system for stochastic workflow in oilfield operations

• US 8,046,314 B2
• Filed: 07/17/2008
• Issued: 10/25/2011
• Est. Priority Date: 07/20/2007
• Status: Active Grant

• Current Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Original Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Inventors: Graf, Thomas, Zangl, Georg
• Primary Examiner(s): Holmes; Michael B
• Assistant Examiner(s): Traktovenko; Ilya
• Application Number:
  US12/175,429
• Publication Number:
  US 20090020284A1
• Time in Patent Office: 1,195 Days
• Field of Search: 706 15- 44
• US Class Current: 706/15
• CPC Class Codes: E21B 2200/22 Fuzzy logic, artificial intelligence, neural networks or the like
• CPC Class Codes: E21B 44/00 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

Abstract

The invention relates to a method for performing an oilfield operation. The method steps include obtaining oilfield data sets associated with oilfield entities, generating a stochastic database from the oilfield data sets based on an artificial neural network of the oilfield data sets, screening the oilfield data sets to identify candidates from the oilfield entities, wherein the screening is based on the stochastic database, performing a detail evaluation of each candidates, selecting an oilfield entity from the candidates based on the detail evaluation, and performing the oilfield operation for the selected oilfield entity.

Claims

1. A method of performing an oilfield operation, comprising:

obtaining a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields;

generating a stochastic database from the plurality of oilfield data sets based on an artificial neural network of the plurality of oilfield data sets, wherein the artificial neural network comprises a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps

screening the plurality of oilfield data sets to identify a plurality of candidates from the plurality of oilfield entities, wherein the screening is based on the stochastic database;

performing a detail evaluation of each of the plurality of candidates;

selecting an oilfield entity from the plurality of candidates based on the detail evaluation; and

performing the oilfield operation for the oilfield entity.

2. The method of claim 1, further comprising generating the SOM by:

assigning each of the plurality of data fields to one of the plurality of maps of the SOM; and

assigning each of the plurality of oilfield entities to one of the plurality of SOM locations based on a pre-determined SOM algorithm to represent statistical patterns in the plurality of oilfield data sets;

wherein the stochastic data base comprises probability information obtained based on the SOM and associated with at least one of the plurality of data fields, and

wherein the probability information comprises at least one selected from a group consisting of probability distribution and a combination of mean value, standard deviation, and uncertainty.

3. The method of claim 1,

wherein the oilfield operation comprises at least one selected from a group consisting of Enhanced Oil Recovery (EOR) operation and back-allocation of oilfield production from a plurality of commingled wells.

4. A method of performing an oilfield operation, comprising:

obtaining a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields, at least one data field of the plurality of data fields of at least one oilfield data set of the plurality of oilfield data sets being an un-populated data field;

generating an artificial neural network of the plurality of oilfield data sets, the artificial neural network comprising one or more relationships among the plurality of data fields;

populating the un-populated data field of the at least one oilfield data set by an estimated data based on the one or more relationships;

generating, from the plurality of oilfield data sets and in response to populating the un-populated data field, a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps;

identifying a plurality of clusters from the plurality of SOM locations based on the pre-determined SOM algorithm, each of the plurality of clusters corresponding to a portion of the plurality of oilfield entities having substantially similar parameter values in the plurality of oilfield data sets; and

performing the oilfield operation based on the portion of the plurality of oilfield entities having substantially similar parameter values in the plurality of oilfield data sets.

5. The method of claim 4,

wherein the plurality of oilfield entities comprise at least one selected from a group consisting of a reservoir, a well, and a completion.

6. The method of claim 4,

wherein the SOM is generated by:

assigning each of the plurality of data fields to one of the plurality of maps of the SOM; and

assigning each of the plurality of oilfield entities to one of the plurality of SOM locations based on a pre-determined SOM algorithm to represent statistical patterns in the plurality of oilfield data sets,

further comprising;

generating probability information of the estimated data based on the artificial neural network, wherein the probability information comprises at least one selected from a group consisting of probability distribution and a combination of mean value, standard deviation, and uncertainty.

7. The method of claim 4,

wherein the plurality of data fields comprise one or more key performance indicators (KPIs) of the oilfield operation.

8. The method of claim 7,

wherein the plurality of oilfield entities comprise at least one selected from a group consisting of a reservoir, a well, and a completion.

9. The method of claim 7,

wherein the each of the plurality of proxy models comprises a nominal model and a response surface,

wherein the nominal model models the oilfield operation of a representative oilfield entity of the portion of the plurality of oilfield entities associated with the cluster, and

wherein the response surface represents sensitivities of the oilfield operation to deviations of the portion of the plurality of oilfield entities from the representative oilfield entity.

10. The method of claim 7,

wherein the oilfield operation comprises at least one selected from a group consisting of Enhanced Oil Recovery (EOR) operation and back-allocation of oilfield production from a plurality of commingled wells.

11. The method of claim 7, further comprising:

identifying one or more objective functions of the oilfield operation;

generating a Bayesian network for modeling the one or more objective functions using at least the plurality of proxy models;

generating a ranking of the plurality of oilfield entities based on the Bayesian network; and

performing the oilfield operation based on the ranking.

12. The method of claim 11, further comprising:

generating a probability distribution for at least one of the plurality of data fields based on the artificial neural network,

wherein the Bayesian network is generated based on Monte-Carlo simulation with the probability distribution using the plurality of proxy models.

13. The method of claim 11, further comprising:

identifying one or more candidates from the plurality of oilfield entities based on the ranking;

performing detail analysis of the one or more candidates; and

selecting, based on the detail analysis, a final candidate from the one or more candidates to perform the oilfield operation.

14. A method of performing an oilfield operation, comprising:

obtaining a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields;

generating, from the plurality of oilfield data sets, a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps;

identifying a plurality of clusters from the plurality of SOM locations based on the pre-determined SOM algorithm, each of the plurality of clusters corresponding to a portion of the plurality of oilfield entities having substantially similar parameter values in the plurality of oilfield data sets;

generating a plurality of proxy models each corresponding to a cluster of the plurality of clusters and for modeling the oilfield operation of the portion of the plurality of oilfield entities associate with the cluster; and

performing the oilfield operation based on the plurality of proxy models.

15. The method of claim 14,

wherein the plurality of data fields comprise one or more key performance indicators (KPIs) of the oilfield operation, and

wherein the plurality of oilfield entities comprise at least one selected from a group consisting of a reservoir, a well, and a completion.

16. The method of claim 14,

wherein the SOM is generated by:

assigning each of the plurality of data fields to one of the plurality of maps of the SOM; and

assigning each of the plurality of oilfield entities to one of the plurality of SOM locations based on a pre-determined SOM algorithm to represent statistical patterns in the plurality of oilfield data sets,

wherein the each of the plurality of proxy models comprises a nominal model and a response surface,

wherein the nominal model models the oilfield operation of a representative oilfield entity of the portion of the plurality of oilfield entities associated with the cluster, and

wherein the response surface represents sensitivities of the oilfield operation to deviations of the portion of the plurality of oilfield entities from the representative oilfield entity.

17. The method of claim 14,

wherein the oilfield operation comprises at least one selected from a group consisting of Enhanced Oil Recovery (EOR) operation and back-allocation of oilfield production from a plurality of commingled wells.

18. The method of claim 14, further comprising:

identifying one or more objective functions of the oilfield operation;

generating a Bayesian network for modeling the one or more objective functions using at least the plurality of proxy models;

generating a ranking of the plurality of oilfield entities based on the Bayesian network; and

performing the oilfield operation based on the ranking.

19. The method of claim 18,

wherein each of the plurality of data fields is associated with a probability distribution, and

wherein the Bayesian network is generated based on Monte-Carlo simulation with the probability distribution using the plurality of proxy models.

20. The method of claim 18, further comprising:

identifying one or more candidates from the plurality of oilfield entities based on the ranking;

performing detail analysis of the one or more candidates; and

selecting, based on the detail analysis, a final candidate from the one or more candidates to perform the oilfield operation.

21. A surface unit comprising a memory and a processor, embodying instructions stored in the memory and executable by the processor to perform method steps to perform an oilfield operation, the instructions comprising functionality to:

obtain a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields;

generate a stochastic database from the plurality of oilfield data sets based on an artificial neural network of the plurality of oilfield data sets, wherein the artificial neural network comprises a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps;

screen the plurality of oilfield data sets to identify a plurality of candidates from the plurality of oilfield entities, wherein the screening is based on the stochastic database;

perform a detail evaluation of each of the plurality of candidates;

select an oilfield entity from the plurality of candidates based on the detail evaluation; and

perform the oilfield operation for the oilfield entity.

22. A surface unit comprising a memory and a processor, embodying instructions stored in the memory and executable by the processor to perform method steps to perform an oilfield operation, the instructions comprising functionality to:

obtain a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields, at least one data field of the plurality of data fields of at least one oilfield data set of the plurality of oilfield data sets being an un-populated data field;

generate a artificial neural network of the plurality of oilfield data sets, the artificial neural network comprising one or more relationships among the plurality of data fields;

populate the un-populated data field of the at least one oilfield data set by an estimated data based on the one or more relationships;

generate, from the plurality of oilfield data sets and in response to populating the un-populated data field, a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps;

identify a plurality of clusters from the plurality of SOM locations based on the pre-determined SOM algorithm, each of the plurality of clusters corresponding to a portion of the plurality of oilfield entities having substantially similar parameter values in the plurality of oilfield data sets; and

perform the oilfield operation based on the portion of the plurality of oilfield entities having substantially similar parameter values in the plurality of oilfield data sets.

23. A surface unit comprising a memory and a processor, embodying instructions stored in the memory and executable by the processor to perform method steps to perform an oilfield operation, the instructions comprising functionality to:

obtain a plurality of oilfield data sets each corresponding to one of a plurality of oilfield entities and comprising a plurality of data fields;

generate, from the plurality of oilfield data sets, a self organizing map (SOM) having a plurality of SOM locations and comprising a plurality of maps;

identify a plurality of clusters from the plurality of SOM locations based on the pre-determined SOM algorithm, each of the plurality of clusters corresponding to a portion of the plurality of oilfield entities having similar parameter values in the plurality of oilfield data sets;

generate a plurality of proxy models each corresponding to a cluster of the plurality of clusters and for modeling the oilfield operation of the portion of the plurality of oilfield entities associate with the cluster; and

perform the oilfield operation based on the plurality of proxy models.