Systems and methods for analyzing resource production

• US 10,577,894 B1
• Filed: 02/24/2016
• Issued: 03/03/2020
• 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 H B
• Application Number:
  US15/052,646
• Time in Patent Office: 1,469 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 maintaining a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving initial production information associated with the well; projecting a maintenance schedule associated with the well using a production analysis engine applied to the field specific model, the maintenance schedule including a maintenance event and an associated date; and implementing the maintenance schedule on the well.

Claims

1. A method for maintaining a well within a field, the method comprising:

receiving production information associated with one or more additional wells within the field, the field having field characteristics including geological, geophysical, petrophysical, lithological, and mineralogical characteristics, a first portion of the production information being received in a form of structured data, and a second portion of the production information 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 production information 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 adjacent wells, and associated depletion curves;

based on the one or more synthetic variables and further based on the received production information 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 initial production information associated with the well within the field;

selecting one of the plurality of field-specific models;

based on the received initial production information associated with the well and the selected field-specific model, projecting, using a production analysis engine, a maintenance schedule associated with the well, the maintenance schedule including a maintenance event and an associated date; and

performing maintenance on the well in accordance with the maintenance schedule.

2. The method of claim 1, wherein the maintenance event includes fracking, and wherein performing maintenance on the well comprises fracking the well.

3. The method of claim 2, wherein the maintenance schedule includes fracking parameters.

4. The method of claim 3, wherein the fracking parameters include pressure.

5. The method of claim 3, wherein the fracking parameters include proppant amount.

6. The method of claim 3, wherein the fracking parameters include proppant composition.

7. The method of claim 3, wherein the fracking parameters include flow rate.

8. The method of claim 1, wherein the maintenance event includes reworking, and wherein performing maintenance on the well comprises reworking the well.

9. The method of claim 8, wherein the maintenance schedule includes projected completion parameters for use during the reworking.

10. The method of claim 1, wherein the maintenance event includes a shut-in, and wherein performing maintenance on the well comprises shutting-in the well.

11. The method of claim 10, wherein the maintenance schedule includes a time and a duration of the shut-in.

17. The method of claim 1, wherein selecting the one of the plurality of field-specific models comprises:

receiving information associated with a further one or more wells within the field;

determining a test set of information from the received information associated with the further one or more wells within the field, and validating each of the plurality of field-specific models using the test set of information; and

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

18. The method of claim 1, wherein each of the plurality of field-specific models includes one or more machine learning models.

19. The method of claim 1, wherein the production information associated with the one or more additional wells comprises actual production information or simulated production information, and wherein the initial production information associated with the well comprises actual production information or simulated production information.

20. The method of claim 1, wherein the unstructured data comprises one or more videos and/or one or more sound files.

21. The method of claim 1, wherein the unstructured data comprises one or more text files and/or one or more image files.

12. A method for maintaining a well within a field, the method comprising:

receiving production information associated with one or more additional wells within the field, the field having field characteristics including geological, geophysical, petrophysical, lithological, and mineralogical characteristics, a first portion of the production information being received in a form of structured data, and a second portion of the production information 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 production information 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 production information 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 production information associated with the well within the field;

selecting one of the plurality of field-specific models;

based on the received production information associated with the well and the selected field-specific model, projecting, using a production analysis engine, production changes associated with fracking the well at a projected date, the production changes including a set of fracking parameters; and

fracking the well in accordance with the fracking parameters.

13. The method of claim 12, wherein the fracking parameters include pressure.

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

15. The method of claim 12, wherein the fracking parameters include proppant composition.

16. The method of claim 12, wherein the fracking parameters include flow rate.