Systems and methods for analyzing resource production

• US 10,364,662 B1
• Filed: 02/24/2016
• Issued: 07/30/2019
• 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 Johannes, Marshall, Marc, Rother, Rory Windell, Underbrink, Joseph C
• Primary Examiner(s): Rodriguez, Carlos R Ortiz
• Application Number:
  US15/052,559
• Time in Patent Office: 1,252 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 drilling a well includes prescribing a drill recipe using a drill analysis engine based on a field-specific model. The drill recipe identifies prescribed segments. Each prescribed segment of the prescribed segments includes a start depth. The method further includes initiating drilling of the well based on the drill recipe; detecting a drill event inconsistent with the drill recipe; prescribing an adjusted drill recipe using the drill analysis engine based on the field-specific model in response to the drill event, the adjusted drill recipe including adjusted prescribed segments; and drilling the well based on the adjusted drill recipe.

Claims

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

prescribing a drill recipe using a drill analysis engine using a neural network or a regression based on a correlation determined from a field-specific model, the field-specific model including a synthetic proximity depletion variable, the drill recipe identifying drilling spin rates, weights, mudflow parameters, and prescribed segments between pulling a drill string, each prescribed segment of the prescribed segments including a start depth, wherein the synthetic proximity depletion variable is derived from, at least, a distance of adjacent wells and associated depletion curves;

initiating drilling of the well based on the drill recipe;

detecting a drill event inconsistent with the drill recipe;

prescribing an adjusted drill recipe using the drill analysis engine using the neural network or the regression based on the correlation determined from the field-specific model in response to the drill event, the adjusted drill recipe including adjusted prescribed segments; and

drilling the well based on the adjusted drill recipe.

2. The method of claim 1, further comprising receiving a drill log in narrative text format, wherein detecting includes analyzing the drill log with a log analysis engine.

3. The method of claim 2, wherein the log analysis engine is a machine learned engine.

4. The method of claim 3, wherein the machine learned engine includes a neural network model or a classification model.

5. The method of claim 1, wherein the mud flow parameters include depth specific mud flow parameters.

6. The method of claim 5, wherein the mud flow parameters include mud flow rates.

7. The method of claim 5, wherein the mud flow parameters include mud viscosity.

8. The method of claim 1, wherein the drill parameters include weight applied to the drill string.

9. The method of claim 1, wherein the drill parameters include spin rate.

10. The method of claim 1, wherein the drill parameters are depth specific drill parameters.

11. The method of claim 1, wherein the drill event includes a string event.

12. The method of claim 1, wherein the drill event includes a mud loss event.

13. The method of claim 1, wherein the drill event includes a deviation in drill parameters from the prescribed drill recipe.

14. The method of claim 1, wherein the drill event includes an associated depth.

15. The method of claim 1, wherein the adjusted drill recipe includes adjusted depth specific drill parameters.

16. The method of claim 1, wherein the adjusted drill recipe includes adjusted mud flow rates.

17. The method of claim 1, wherein the synthetic proximity depletion variable is derived from Euclidean distance between two wells and associated depletion curves.