IP Library › Granted Patent US 10,415,362
Granted Patent B1
US 10,415,362 · App. 15/052,567 · Granted Sep 17, 2019

Systems and methods for analyzing resource production

Inventors: Atanu Basu (Austin, TX); Daniel Mohan (Austin, TX); Chun Wang (Austin, TX); Frederick Johannes Venter (Driftwood, TX); Marc Marshall (Austin, TX); Rory Windell Rother (Austin, TX); Joseph C. Underbrink (Round Rock, TX)
Assignee: DataInfoCom USA Inc.
E21B44/00G05B13/028G05B13/048
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Quick Facts
Patent No.
US 10,415,362
App. No.
15/052,567
Granted
Sep 17, 2019
Kind
B1
Abstract

A system includes a field-specific model; a log analysis engine; a drill analysis engine; an interface; and computational circuitry in communication with the field-specific model, the log analysis engine, the drill analysis engine, and the interface. The computational circuitry is to: prescribe a drill recipe using the drill analysis engine based on the field-specific model, the drill recipe identifying prescribed segments, mud flow parameters, and drill parameters, each prescribed segment of the prescribed segments including an associated depth; receive a drill log in narrative text format via the interface; detect a drill event inconsistent with the drill recipe by analyzing the drill log with the log analysis engine; and prescribe 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.

Claims (29)

1. A system comprising:

a field-specific model;

a log analysis engine;

a drill analysis engine incorporating a classification or a regression derived from a correlation determined from the field specific model;

an interface; and

computational circuitry in communication with the field-specific model, the log analysis engine, the drill analysis engine, and the interface, the computational circuitry to:

prescribe a drill recipe using the drill analysis engine based on the field-specific model, the drill recipe identifying prescribed segments, mud flow parameters, and drill parameters, each prescribed segment of the prescribed segments including an associated depth;

receive a drill log in narrative text format via the interface;

detect a drill event inconsistent with the drill recipe by analyzing the drill log with the log analysis engine; and

prescribe 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.

2. The system of claim 1 , wherein the mud flow parameters include depth specific mud flow parameters.

3. The system of claim 2 , wherein the mud flow parameters include mud flow rates.

4. The system of claim 1 , wherein the drill parameters include weight.

5. The system of claim 1 , wherein the drill parameters include spin rate.

6. The system of claim 1 , wherein the drill parameters are depth specific drill parameters.

7. The system of claim 1 , wherein the drill event includes a string event.

8. The system of claim 1 , wherein the drill event includes a mud loss event.

9. The system of claim 1 , wherein the drill event includes a drill rate.

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

11. The system of claim 1 , wherein the drill event includes an associated depth.

12. The system of claim 1 , wherein the adjusted drill recipe includes adjusted drill parameters.

13. The system of claim 1 , wherein the adjusted drill recipe includes adjusted mud flow rates.

14. The system of claim 1 , wherein the log analysis engine is a machine learned engine.

15. The system of claim 14 , wherein the machine learned engine includes a neural network model or a classification model.

16. The system of claim 1 , wherein the field specific model includes a synthetic proximity depletion variable.

17. The system of claim 16 , wherein the synthetic proximity depletion variable is derived from Euclidean distance between two wells and associated depletion curves.

18. The system of claim 1 , wherein prescribing with the drill analysis engine further includes determining a correlation between drill recipe variables, geological variables and objectives using the drill analysis engine and based on the field specific data model.

19. The system of claim 1 , wherein prescribing with the drill analysis engine further includes applying a neural network drill analysis engine receiving drill recipe variables, geological variables and objectives based on the field specific data model.

20. The system of claim 1 , further comprising a drilling rig configured to drill a well in accordance with the drill recipe.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 2, 2019
From: BASU, ATANU; MOHAN, DANIEL; WANG, CHUN; VENTER, FREDERICK JOHANNES
To: DATAINFOCOM USA INC.
Reel/Frame 049063/0501 →
Continuity (3)
Provisional Application 62172735 · Jun 8, 2015
Provisional Application 62195775 · Jul 22, 2015
Provisional Application 62246121 · Oct 25, 2015
Cited By (1)
US 12,473,814