Systems and methods for analyzing resource production

• US 10,565,663 B1
• Filed: 02/24/2016
• Issued: 02/18/2020
• Est. Priority Date: 06/08/2015
• Status: Active Grant

• Current Assignee: Datainfocom Usa, Inc.
• Original Assignee: Datainfocom Usa, Inc.
• Inventors: Basu, Atanu, Mohan, Daniel, Wang, Chun, Venter, Frederick
• Primary Examiner(s): Perveen, Rehana
• Assistant Examiner(s): Wechselberger, Alfred B
• Application Number:
  US15/052,621
• Time in Patent Office: 1,455 Days
• CPC Class Codes: E21B 2200/20 Computer models or simulations, e.g. for reservoirs under production, drill bits
• CPC Class Codes: E21B 2200/22 Fuzzy logic, artificial intelligence, neural networks or the like
• CPC Class Codes: E21B 43/11 Perforators; Permeators
• CPC Class Codes: E21B 43/12 Methods or apparatus for controlling the flow of the obtained fluid to or in wells E21B43/25 takes precedence; valve arrangements E21B34/00
• CPC Class Codes: E21B 43/121 Lifting well fluids monitoring of down-hole pump systems E21B47/008
• CPC Class Codes: E21B 43/122 Gas lift
• CPC Class Codes: E21B 43/128 Adaptation of pump systems with down-hole electric drives
• CPC Class Codes: E21B 43/129 Adaptations of down-hole pump systems powered by fluid supplied from outside the borehole gas-lift E21B43/122; jet pumps E21B43/124
• CPC Class Codes: E21B 43/16 Enhanced recovery methods for obtaining hydrocarbons
• CPC Class Codes: E21B 43/164 Injecting CO2 or carbonated water in combination with organic material C09K8/594
• CPC Class Codes: E21B 43/20 Displacing by water
• CPC Class Codes: E21B 43/26 by forming crevices or fractures
• 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
• CPC Class Codes: E21B 47/022 of the borehole, e.g. using geomagnetism
• CPC Class Codes: E21B 49/00 Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
• CPC Class Codes: E21B 49/003 by analysing drilling variables or conditions E21B49/005 takes precedence; systems specially adapted for monitoring a plurality of drilling variables or conditions E21B44/00
• CPC Class Codes: E21B 7/00 Special methods or apparatus for drilling
• CPC Class Codes: E21B 7/04 Directional drilling
• CPC Class Codes: E21B 7/046 horizontal drilling drilling with mechanical conveying means E21B7/003
• CPC Class Codes: G01N 2333/4706 stimulating, promoting or activating activity
• CPC Class Codes: G01V 1/00 : Seismology; Seismic or acoustic prospecting or detecting
• CPC Class Codes: G01V 11/002 : Details, e.g. power supply systems for logging instruments, transmitting or recording data, specially adapted for well logging, also if the prospecting method is irrelevant means for transmitting well survey signals E21B47/12; signal transmission systems in general G08C; transmission in general H04B
• CPC Class Codes: G01V 20/00 : Geomodelling in general
• CPC Class Codes: G01V 2210/66 : Subsurface modeling
• CPC Class Codes: G01V 2210/665 : using geostatistical modeling
• CPC Class Codes: G05B 13/028 : using expert systems only
• CPC Class Codes: G05B 13/048 : using a predictor
• CPC Class Codes: G06F 16/35 : Clustering; Classification
• CPC Class Codes: G06F 30/17 : Mechanical parametric or variational design
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/02 : Neural networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 3/126 : Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06Q 10/06 : Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06313 : Resource planning in a project environment
• CPC Class Codes: G06Q 10/06315 : Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06375 : Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/067 : Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/105 : Human resources
• CPC Class Codes: G06Q 10/1097 : Task assignment
• CPC Class Codes: G06Q 30/00 : Commerce
• CPC Class Codes: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0235 : constrained by time limit or expiration date
• CPC Class Codes: G06Q 30/0255 : based on user history
• CPC Class Codes: G06Q 30/06 : Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/00 : Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 50/02 : Agriculture; Fishing; Forestry; Mining
• CPC Class Codes: Y02P 90/80 : Management or planning

Abstract

A method for completing a well includes receiving completion parameters associated with wells within a field. The completion parameters including casing parameters, perforation parameters, and fracking parameters. The method further includes determining a field-specific model utilizing the completion parameters and prescribing a completion recipe using a completion analysis engine applied to the field-specific model. The completion recipe includes prescribed perforation parameters and prescribed fracking parameters. The completion analysis engine includes a neural network derived from the field-specific model or regression of a correlation of features in the field-specific model. The method also includes completing the well in accordance with the completion recipe including performing perforation in accordance with the perforation parameters and fracking in accordance with the fracking parameters.

Claims

1. A method for completing a well, the method comprising:

receiving completion parameters associated with wells within a field, the field having field characteristics including geological, geophysical, petrophysical, lithological, and mineralogical characteristics, the completion parameters including casing parameters, perforation parameters, and fracking parameters, a first portion of the completion parameters being received in a form of structured data, and a second portion of the completion parameters being received in a form of unstructured data;

transforming the unstructured data into transformed structured data;

deriving one or more synthetic variables from the received completion parameters in the form of the structured data and the transformed structured data, wherein the one or more synthetic variables comprise a proximity depletion synthetic variable derived from, at least, distances between and among adjacent wells, and associated depletion curves;

based on the one or more synthetic variables and further based on the received completion parameters in the form of the structured data and the transformed structured data, building a plurality of field-specific models, each of the plurality of field-specific models being specific to the field characteristics of the field;

receiving data associated with a new well associated with the field;

selecting one of the plurality of field-specific models;

based on the received data associated with the new well and the selected field-specific model, prescribing a completion recipe using a completion analysis engine, the completion recipe including prescribed perforation parameters and prescribed fracking parameters; and

completing the new well in accordance with the completion recipe including performing perforation in accordance with the prescribed perforation parameters and fracking in accordance with the prescribed fracking parameters.

2. The method of claim 1, wherein the perforation parameters include location, number of perforations, or any combination thereof.

3. The method of claim 1, wherein the fracking parameters include pressure, proppant composition, proppant amount, flow rates, or a combination thereof.

19. The method of claim 1, wherein the data associated with the new well comprises actual data or simulated data.

4. A method for completing a well, the method comprising:

receiving completion parameters associated with wells within a field, the field having field characteristics including geological, geophysical, petrophysical, lithological, and mineralogical characteristics, a first portion of the completion parameters being received in a form of structured data, and a second portion of the completion parameters being received in a form of unstructured data;

transforming the unstructured data into transformed structured data;

deriving one or more synthetic variables from the received completion parameters in the form of the structured data and the transformed structured data, wherein the one or more synthetic variables comprise a proximity depletion synthetic variable derived from, at least, distances between and among adjacent wells, and associated depletion curves;

based on the one or more synthetic variables and further based on the received completion parameters in the form of the structured data and the transformed structured data, building a plurality of field-specific models, each of the plurality of field-specific models being specific to the field characteristics of the field;

receiving data associated with a new well associated with the field;

selecting one of the plurality of field-specific models;

based on the received data associated with the new well and the selected field-specific model, prescribing a completion recipe using a completion analysis engine;

begin completing the new well in accordance with the completion recipe;

receiving additional completion data associated with the new well;

based on the received additional completion data associated with the new well and the selected field-specific model, prescribing an updated completion recipe; and

continue completing the new well in accordance with the updated completion recipe.

5. The method of claim 4, wherein the completion recipe and the updated completion recipe include casing parameters.

6. The method of claim 5, wherein the casing parameters include cement parameters.

7. The method of claim 4, wherein the completion recipe and the updated completion recipe include perforation parameters.

8. The method of claim 7, wherein the perforation parameters include location.

9. The method of claim 7, wherein the perforation parameters include number of perforations.

10. The method of claim 4, wherein the completion recipe and the updated completion recipe include fracking parameters.

11. The method of claim 10, wherein the fracking parameters include pressure.

12. The method of claim 10, wherein the fracking parameters include proppant amount.

13. The method of claim 10, wherein the fracking parameters include proppant composition.

14. The method of claim 10, wherein the fracking parameters include flow rate.

15. The method of claim 4, wherein each of the plurality of field-specific models includes a neural network, a regression model, or a combination thereof.

16. The method of claim 4, wherein selecting the one of the plurality of field-specific models comprises:

determining a test set and validating each of the plurality of field-specific models using the test set; and

based on results of the validation, selecting the one of the plurality of field-specific models.

17. The method of claim 4, wherein the unstructured data comprises one or more videos and/or one or more sound files.

18. The method of claim 4, wherein the unstructured data comprises one or more text files and/or one or more image files.

20. The method of claim 4, wherein the data associated with the new well comprises actual data or simulated data.