Determining stimulation design parameters using artificial neural networks optimized with a genetic algorithm

• US 20090182693A1
• Filed: 01/14/2008
• Published: 07/16/2009
• Est. Priority Date: 01/14/2008
• Status: Abandoned Application

• Current Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Original Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Inventors: Fulton, Dwight David, Stephenson, Stanley V.
• Application Number:
  US11/986,763
• Publication Number:
  US 20090182693A1
• Time in Patent Office: Days
• US Class Current: 706/16
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A method for generating an artificial neural network ensemble for determining stimulation design parameters. A population of artificial neural networks is trained to produce one or more output values in response to a plurality of input values. The population of artificial neural networks is optimized to create an optimized population of artificial neural networks. A plurality of ensembles of artificial neural networks is selected from the optimized population of artificial neural networks and optimized using a genetic algorithm having a multi-objective fitness function. The ensemble with the desired prediction accuracy based on the multi-objective fitness function is then selected.

Claims

1. A method for generating an artificial neural network ensemble comprising:

training a population of artificial neural networks to produce one or more output values in response to a plurality of input values;

optimizing the population of artificial neural networks to create an optimized population of artificial neural networks;

selecting a plurality of ensembles of artificial neural networks selected from the optimized population of artificial neural networks;

optimizing the plurality of ensembles of artificial neural networks using a genetic algorithm having a multi-objective fitness function;

selecting an ensemble with the desired prediction accuracy based on the multi-objective fitness function.

2. The method of claim 1 wherein the optimization of the population of artificial neural networks is performed using a genetic algorithm having a multi-objective fitness function.

3. The method of claim 2 wherein the optimization of the plurality of ensembles of artificial neural networks comprises testing of the ensembles with actual input values and output values to calculate the multi-objective fitness function.

4. The method of claim 3 wherein the plurality of inputs used to train the population of artificial neural networks comprises an open hole log parameter.

5. The method of claim 4 wherein the ensemble with the highest prediction accuracy produces as output a synthetic log, wherein the synthetic log comprises a synthetic log parameter.

6. The method of claim 5 wherein the open hole log parameter is selected from the group consisting of a triple combo log parameter, neutron porosity, bulk density, formation resistivity, GR, SP, Cal, PE, a combination thereof, and a derivative thereof.

7. The method of claim 5 wherein the synthetic log parameter is selected from the group consisting of a NMR log parameter, a MRIL log parameter, MBVI parameter, a MPHI parameter, a MSWE parameter, a MSWI parameter, a MPERM parameter, a combination thereof, and a derivative thereof.

8. The method of claim 5 wherein a design for a stimulation treatment of a well is created in part in response to at least one synthetic log parameter.

9. The method of claim 1 wherein the plurality of ensembles of artificial neural networks comprise a plurality of optimized artificial neural networks.

10. The method of claim 1 wherein the ensemble with the desired prediction accuracy produces as output a stimulation treatment design parameter.

11. The method of claim 1 wherein the population of artificial neural networks have a heterogeneous mix of hidden layers.

12. A computer program, stored in a tangible medium, for producing a synthetic open hole log in response to an actual open hole log parameter, comprising an artificial neural network ensemble, the program comprising executable instruction that cause a computer to:

train a population of artificial neural networks to produce one or more synthetic open hole log parameters in response to a plurality of measured open hole log parameters;

optimize the population of artificial neural networks to create an optimized population of artificial neural networks;

select a plurality of ensembles of artificial neural networks selected from the optimized population of artificial neural networks;

optimize the plurality of ensembles of artificial neural networks using a genetic algorithm having a multi-objective fitness function;

select an ensemble with the desired prediction accuracy based on the multi-objective fitness function.

13. The computer program of claim 12 wherein the executable instructions cause a computer to optimize the population of artificial neural networks using a genetic algorithm having a multi-objective fitness function.

14. The computer program of claim 13 wherein the executable instructions cause a computer to select the measured open hole log parameters from the group consisting of a triple combo log parameter, neutron porosity, bulk density, formation resistivity, GR, SP, Cal, PE, a combination thereof, and a derivative thereof.

15. The computer program of claim 13 wherein the executable instructions cause a computer to select the synthetic open hole log parameter from the group consisting of a NMR log parameter, MRIL log parameter, a MBVI parameter, a MPHI parameter, a MSWE parameter, a MSWI parameter, a MPERM parameter, a combination thereof, and a derivative thereof.

16. The computer program of claim 12 wherein the executable instructions cause a computer to create a design for a stimulation treatment of a well in part in response to at least one synthetic open hole log parameter.

17. The computer program of claim 13 wherein the executable instructions cause a computer to use a different multi-objective fitness function in the optimization of the population of artificial neural networks than the multi-objective fitness function used in optimizing the plurality of ensembles of artificial neural networks.

18. A method for creating an artificial neural network ensemble for generating a synthetic MRIL and acoustic log parameter comprising:

training a population of artificial neural networks to produce one or more synthetic NMR and acoustic log parameters in response to a plurality of measured open hole log parameters;

optimizing the population of artificial neural networks to create an optimized population of artificial neural networks using a genetic algorithm having a multi-objective fitness function;

selecting a plurality of ensembles of artificial neural networks selected from the optimized population of artificial neural networks;

optimizing the plurality of ensembles of artificial neural networks using a genetic algorithm having a multi-objective fitness function;

selecting an ensemble with the desired prediction accuracy based on the multi-objective fitness function.

19. The method of claim 18 wherein the plurality of measured open hole log parameter are selected from the group consisting of a triple combo log parameter, neutron porosity, bulk density, formation resistivity, GR, SP, Cal, PE, a combination thereof, and a derivative thereof.

20. The method of claim 18 wherein the synthetic NMR and acoustic log parameter is selected from the group consisting of a MBVI parameter, a MPHI parameter, a MSWE parameter, a MSWI parameter, a MPERM parameter, a combination thereof, and a derivative thereof.

21. The method of claim 18 wherein the synthetic NMR and acoustic log parameters are used at least in part to create a design for a stimulation treatment of a well.