Wellbore completion and hydraulic fracturing optimization methods and associated systems

• US 9,262,713 B2
• Filed: 09/05/2012
• Issued: 02/16/2016
• Est. Priority Date: 09/05/2012
• Status: Active Grant

• Current Assignee: StrataGen, Inc. (Carbo Ceramics Incorporated)
• Original Assignee: Carbo Ceramics Incorporated
• Inventors: Shelley, Robert, Guliyev, Nijat, Nejad, Amir
• Primary Examiner(s): Fernandez Rivas, Omar
• Assistant Examiner(s): Day, Herng-Der
• Application Number:
  US13/604,367
• Publication Number:
  US 20140067353A1
• Time in Patent Office: 1,259 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: E21B 43/26 by forming crevices or fractures
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks

Abstract

Methods and systems for optimizing wellbore completion and, in particular, methods and systems for optimizing hydraulic fracturing parameters are disclosed. In some embodiments, a method of optimizing wellbore completion includes gathering wellbore data, screening and processing the gathered wellbore data, utilizing the screened and processed wellbore data to define an optimized model, and utilizing the optimized model to evaluate combinations of available wellbore completion parameters. In some instances, the optimized model is defined using artificial neural networks, genetic algorithms, and/or boosted regression trees. Further, in some embodiments the combinations of available wellbore completion parameters include hydraulic fracturing parameters, such as number of fractures, fracturing fluid type, proppant type, fracturing volume, and/or other parameters.

Claims

1. A method, comprising:

gathering a first data collection regarding a plurality of completed horizontal wellbores, the first data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof;

gathering a second data collection regarding an uncompleted horizontal wellbore, the second data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof;

applying a first portion of the first data collection to a plurality of artificial neural networks stored onto a non-transitory computer readable medium, wherein the first portion is gathered from a first set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the first set of the plurality of completed horizontal wellbores is less than all of the plurality of completed horizontal wellbores;

applying one or more genetic algorithms stored onto the non-transitory computer readable medium to train each of the plurality of artificial neural networks in order to generate a plurality of predictive models, each of the plurality of predictive models providing an estimate of wellbore production based on the first data collection;

identifying a best predictive model from the plurality of predictive models by applying a second portion of the first data collection to the plurality of predictive models, wherein the second portion is gathered from a second set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the second set of completed horizontal wellbores is different from the first set of completed horizontal wellbores;

applying the gathered second data collection regarding the uncompleted horizontal wellbore and a plurality of available wellbore completion parameters to the best predictive model to identify a set of wellbore completion parameters from the plurality of available wellbore completion parameters that optimizes estimated wellbore production based on the best predictive model;

outputting the set of wellbore completion parameters identified using the best predictive model in human-intelligible form; and

hydraulically fracturing the uncompleted horizontal wellbore based on the set of wellbore completion parameters identified using the best predictive model.

2. The method of claim 1, wherein the gathered data regarding the plurality of completed horizontal wellbores comprises mud log data.

3. The method of claim 2, wherein the gathered data regarding the uncompleted horizontal wellbore comprises mud log data.

4. The method of claim 1, wherein identifying the best predictive model from the plurality of predictive models further comprises evaluating each of the plurality of predictive models relative to one or more evaluation factors.

5. The method of claim 4, wherein the one or more evaluation factors include factors selected from the group of factors consisting of number of data input parameters and whether the predictive model utilizes hydrocarbon ratios.

6. The method of claim 5, wherein predictive models with a lower number of data input parameters are favored.

7. The method of claim 5, wherein predictive models that utilize at least one hydrocarbon ratio are favored.

8. The method of claim 5, wherein the one or more evaluation factors further includes parameter sensitivity.

9. The method of claim 1, wherein the set of wellbore completion parameters that optimizes estimated wellbore production includes a total number of hydraulic fractures.

10. The method of claim 9, wherein the set of wellbore completion parameters that optimizes estimated wellbore production further includes hydraulic fracture spacings along the uncompleted horizontal wellbore.

11. The method of claim 10, wherein the hydraulic fracture spacings vary along the uncompleted horizontal wellbore.

12. The method of claim 1, wherein the set of wellbore completion parameters that optimizes estimated wellbore production further includes hydraulic fracture volumes.

13. The method of claim 1, wherein the set of wellbore completion parameters that optimizes estimated wellbore production further includes hydraulic fracture fluid type.

14. The method of claim 1, wherein the set of wellbore completion parameters that optimizes estimated wellbore production further includes hydraulic fracture proppant type.

15. The method of claim 1, wherein the set of wellbore completion parameters that optimizes estimated wellbore production further includes hydraulic fracture staging type.

16. The method of claim 1, wherein utilizing the gathered data regarding the plurality of completed horizontal wellbores Vie-field to define the plurality of predictive models includes identifying correlating factors.

17. The method of claim 16, wherein identifying correlating factors includes applying multivariate analysis.

18. The method of claim 16, wherein identifying correlating factors includes applying cluster analysis.

19. The method of claim 16, wherein identifying correlating factors includes generating one or more visual representations of the gathered data selected from the group of visual representations consisting of a scatter plot, a bi-plot, a dendrogram, and a self-organizing map.

20. The method of claim 1, wherein the uncompleted horizontal wellbore and the plurality of completed horizontal wellbores are within a single field.

21. The method of claim 1, wherein the uncompleted wellbore and the plurality of completed horizontal wellbores are from a plurality of fields.

22. The method of claim 21, wherein at least one of the plurality of completed horizontal wellbores is from a first field and the uncompleted horizontal wellbore is from a second field, the second field being different than the first field.

23. A computer-implemented system for optimizing wellbore completion, the system including a non-transitory, computer readable medium having a plurality of instructions stored thereon for executing the following steps:

receiving a first data collection regarding a plurality of completed horizontal wellbores in a field, the first data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof;

receiving a second data collection regarding an uncompleted horizontal wellbore, the second data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof;

applying a first portion of the first data collection to a plurality of artificial neural networks stored onto the non-transitory computer readable medium, wherein the first portion is gathered from a first set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the first set of the plurality of completed horizontal wellbores is less than all of the plurality of completed horizontal wellbores;

applying one or more genetic algorithms stored onto the non-transitory computer readable medium to train each of the plurality of artificial neural networks in order to generate a plurality of predictive models, each of the plurality of predictive models providing an estimate of wellbore production based on the first data collection;

identifying a best predictive model from the plurality of predictive models by applying a second portion of the first data collection to the plurality of predictive models, wherein the second portion is gathered from a second set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the second set of completed horizontal wellbores is different from the first set of completed horizontal wellbores;

applying the gathered second data collection regarding the uncompleted horizontal wellbore and a plurality of available wellbore completion parameters to the best predictive model to identify a set of wellbore completion parameters from the plurality of available wellbore completion parameters that optimizes estimated wellbore production based on the best predictive model; and

outputting the set of wellbore completion parameters identified using the best predictive model in human-intelligible form and hydraulically fracturing the uncompleted horizontal wellbore based on the set of wellbore completion parameters identified using the best predictive model.

24. A method, comprising:

gathering a first data collection regarding a plurality of completed horizontal wellbores in a single field, the first data collection selected from the group consisting of chromatograph measurements of alkanes, mud log data, rate of penetration and any combination thereof;

gathering a second data collection regarding an uncompleted horizontal wellbore in the field, the second data collection comprising chromatograph measurements of alkanes, mud log data, rate of penetration and any combination thereof;

applying a first portion of the first data collection to a plurality of artificial neural networks stored onto a non-transitory computer readable medium, wherein the first portion is gathered from a first set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the first set of the plurality of completed horizontal wellbores is less than all of the plurality of completed horizontal wellbores;

applying one or more genetic algorithms stored onto the non-transitory computer readable medium to train each of the plurality of artificial neural networks in order to generate a plurality of predictive models, each of the plurality of predictive models providing an estimate of wellbore production based on the first data collection;

identifying a best predictive model from the plurality of predictive models by;

applying a second portion of the first data collection to the plurality of predictive models, wherein the second portion is gathered from a second set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the second set of completed horizontal wellbores is different from the first set of completed horizontal wellbores; and

evaluating each of the plurality of predictive models relative to one or more evaluation factors selected from the group of factors consisting of number of data input parameters and whether the predictive model utilizes hydrocarbon ratios, wherein predictive models that utilize at least one hydrocarbon ratio are favored;

applying the gathered second data collection regarding the uncompleted horizontal wellbore and a plurality of available wellbore completion parameters to the best predictive model to identify a set of wellbore completion parameters from the plurality of available wellbore completion parameters that optimizes estimated wellbore production based on the best predictive model;

outputting the set of wellbore completion parameters identified using the best predictive model in human-intelligible form; and

hydraulically fracturing the uncompleted horizontal wellbore within the set of wellbore completion parameters identified using the best predictive model.

25. The method of claim 24, wherein utilizing the gathered data regarding the plurality of completed horizontal wellbores in the field to define the plurality of predictive models includes identifying correlating factors selected from the group consisting of applying multivariate analysis and applying cluster analysis and any combination thereof.

26. The method of claim 25, wherein identifying correlating factors further includes generating one or more visual representations of the gathered data selected from the group of visual representations consisting of a scatter plot, a bi-plot, a dendrogram, and a self-organizing map.