SYSTEMS AND METHODS FOR EXPERT SYSTEMS FOR UNDERBALANCED DRILLING OPERATIONS USING BAYESIAN DECISION NETWORKS

• US 20140124265A1
• Filed: 03/14/2013
• Published: 05/08/2014
• Est. Priority Date: 11/02/2012
• Status: Abandoned Application

• Current Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia), Texas A&M University System
• Original Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia)
• Inventors: Al-Yami, Abdullah Saleh Hussain, Schubert, Jerome
• Application Number:
  US13/827,746
• Publication Number:
  US 20140124265A1
• Time in Patent Office: Days
• US Class Current: 175/24
• 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: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems and methods are provided for an underbalanced drilling (UBD) expert system that provides underbalanced drilling recommendations, such as best practices. The UBD expert system may include one or more Bayesian decision network (BDN) model that receive inputs and output recommendations based on Bayesian probability determinations. The BDN models may include: a general UBD BDN model, a flow UBD BDN model, a gaseated (i.e., aerated) UBD BDN model, a foam UBD BDN model, a gas (e.g., air or other gases) UBD BDN model, a mud cap UBD BDN model, an underbalanced liner drilling (UBLD) BDN model, an underbalanced coil tube (UBCT) BDN model, and a snubbing and stripping BDN model.

Claims

1. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising an underbalanced drilling Bayesian decision network (BDN) model, the underbalanced drilling BDN model comprising;

a first section, comprising;

a formation indicators uncertainty node configured to receive one or more formation indicators from the one or more inputs;

a formation considerations decision node configured to receive one or more formation considerations from the one or more inputs; and

a first consequences node dependent on the formation indicators uncertainty node and the formation considerations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more formation indicators and the one or more formation considerations;

a second section, comprising;

a planning phases uncertainty node configured to receive one or more planning phases from the one or more inputs;

a planning phases recommendations decision node configured to receive one or more planning phases recommendations from the one or more inputs; and

a second consequences node dependent on the planning phases uncertainty node and the planning phases recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more planning phases and the one or more planning phases recommendations; and

a third section, comprising;

an equipment requirements uncertainty node configured to receive one or more equipment requirements from the one or more inputs;

an equipment recommendations decision node configured to receive one or more equipment recommendations from the one or more inputs; and

a third consequences node dependent on the equipment requirements uncertainty node and the equipment recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more equipment requirements and the one or more equipment recommendations.

2. The system of claim 1, wherein the UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, and the third consequences node.

3. The system of claim 1, comprising a user interface configured to display the UBD BDN model and receive user selections of the one or more inputs.

4. The system of claim 1, wherein the one or more formation indicators, the one or more planning phases, and the one or more equipment requirements are each associated with a respective plurality of probabilities.

5. A computer-implemented method for an underbalanced drilling expert system having an underbalanced drilling Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more nodes of a first section of the underbalanced drilling BDN model, the one or more nodes comprising;

a formation indicators uncertainty node;

a formation considerations decision node;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

6. The method of claim 5, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBD BDN model.

7. The computer-implemented method of claim 5, comprising:

providing the one or more inputs to one or more nodes of a second section of the underbalanced drilling BDN model, the one or more nodes comprising;

a planning phases uncertainty node configured to receive one or more planning phases;

a planning phases recommendations decision node configured to receive one or more planning phases recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

8. The computer-implemented method of claim 7, comprising:

providing the one or more inputs to one or more nodes of a third section of the underbalanced drilling BDN model, the one or more nodes comprising;

an equipment requirements uncertainty node configured to receive one or more equipment requirements;

an equipment recommendations decision node configured to receive one or more equipment recommendations;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

9. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more flow underbalanced drilling recommendations based on one or more inputs, the flow underbalanced drilling expert system comprising a flow underbalanced drilling Bayesian decision network (BDN) model, the flow underbalanced drilling BDN model comprising;

a first section, comprising;

a tripping types uncertainty node configured to receive one or more tripping types from the one or more inputs;

a permeability level uncertainty node configured to receive one or more permeability levels from the one or more inputs;

a tripping options decision node configured to receive one or more tripping options from the one or more inputs;

a first consequences node dependent on the tripping uncertainty node, the permeability level uncertainty node, and the tripping options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more tripping types, the one or more permeability levels, and the one or more tripping options;

a second section, comprising;

a connection types uncertainty node configured to receive one or more connection types from the one or more inputs;

a connection options decision node configured to receive one or more connection options from the one or more inputs;

a second consequences node dependent on the connection uncertainty node and the connection options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more connection types and the one or more connection options;

a third section, comprising;

a flow drilling types uncertainty node configured to receive one or more flow drilling types from the one or more inputs;

a flow drilling options decision node configured to receive one or more flow drilling options from the one or more inputs;

a third consequences node dependent on the flow drilling uncertainty node and the flow drilling options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more flow drilling types and the one or more flow drilling options.

10. The system of claim 9, wherein the flow UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, and the third consequences node.

11. The system of claim 9, comprising a user interface configured to display the flow UBD BDN model and receive user selections of the one or more inputs.

12. The system of claim 9, wherein the one or more tripping types, the one or more permeability levels, the one or connection types, and the one or more flow drilling types are each associated with a respective plurality of probabilities.

13. A computer-implemented method for an underbalanced drilling expert system having a flow underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the flow underbalanced drilling BDN model, the one or more nodes comprising;

a tripping uncertainty node configured to receive one or more tripping types;

a permeability level uncertainty node configured to receive one or more permeability levels; and

a tripping options decision node a tripping options decision node configured to receive one or more tripping options;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the flow underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

14. The computer-implemented method of claim 13, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the flow UBD BDN model.

15. The computer-implemented method of claim 13, comprising:

providing the one or more inputs to one or more nodes of a second section of the flow UBD BDN model, the one or more nodes comprising;

a connection types uncertainty node configured to receive one or more connection types;

a connection options decision node configured to receive one or more connection options;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

16. The computer-implemented method of claim 15, comprising:

providing the one or more inputs to one or more nodes of a third section of the flow UBD BDN model, the one or more nodes comprising;

a flow drilling types uncertainty node configured to receive one or more flow drilling types;

a flow drilling options decision node configured to receive one or more flow drilling options;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the flow UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

17. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory accessible by the one or more processors, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a gaseated underbalanced drilling Bayesian decision network (BDN) model, the gaseated underbalanced drilling BDN model comprising;

a first section, comprising;

a gas injection process uncertainty node configured to receive one or more gas injection process types from the one or more inputs;

a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics from the one or more inputs;

a first consequences node dependent on the gas injection process uncertainty node and the gas infection processes considerations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas injection process types and the one or more gas injection process characteristics;

a second section, comprising;

a fluid volume limits uncertainty node configured to receive one or more fluid volume limits from the one or more inputs;

a fluid volume limits requirements decision node configured to receive one or more fluid volume limits requirements from the one or more inputs;

a second consequences node dependent on the fluid volume limits uncertainty node and the fluid volume limits requirements decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more fluid volume limits and the one or more fluid volume limits requirements;

a third section, comprising;

a kick type uncertainty node configured to receive one or more kick types from the one or more inputs;

a kicks recommendations decision node configured to receive one or more kicks recommendations from the one or more inputs;

a third consequences node dependent on the kick type uncertainty node and the kicks recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more kick types and the one or more kicks recommendations; and

a fourth section, comprising;

an operational considerations uncertainty node configured to receive one or more operational considerations from the one or more inputs;

an operational recommendations decision node configured to receive one or more operational recommendations from the one or more inputs; and

a fourth consequences node dependent on the operational considerations uncertainty node and the operational recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more operational recommendations and the one or more operational recommendations.

18. The system of claim 17, wherein the gaseated UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, the third consequences node, and the fourth consequences node.

19. The system of claim 17, comprising a user interface configured to display the gaseated UBD BDN model and receive user selections of the one or more inputs.

20. The system of claim 17, wherein the one or more gas injection process types, the one or more fluid volume limits, the one or kick types, and the one or more operational considerations are each associated with a respective plurality of probabilities

21. A computer-implemented method for an underbalanced drilling expert system having a gaseated underbalanced drilling Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the gaseated underbalanced drilling (UBD) BDN model, the one or more nodes comprising;

a gas injection process uncertainty node configured to receive one or more gas injection process types;

a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

22. The computer-implemented method of claim 21, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the gaseated UBD BDN model.

23. The computer-implemented method of claim 21, comprising:

providing the one or more inputs to one or more nodes of a second section of the gaseated UBD BDN model, the one or more nodes comprising;

a fluid volume limits uncertainty node configured to receive one or more fluid volume limits from the one or more inputs;

a fluid volume limits requirements decision node configured to receive one or more fluid volume limits requirements;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

24. The computer-implemented method of claim 23, comprising:

providing the one or more inputs to one or more nodes of a third section of the gaseated UBD BDN model, the one or more nodes comprising;

a kick type uncertainty node configured to receive one or more kick types;

a kicks recommendations decision node configured to receive one or more kicks recommendations;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

25. The computer-implemented method of claim 24, comprising:

providing the one or more inputs to one or more nodes of a fourth section of the gaseated UBD BDN model, the one or more nodes comprising;

an operational considerations uncertainty node configured to receive one or more operational considerations;

an operational recommendations decision node configured to receive one or more operational recommendations;

determining the one or more underbalanced drilling recommendations at a fourth consequences node of the third section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

26. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory accessible by the one or more processors, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model, the foam UBD BDN model comprising;

a first section, comprising;

a foam systems considerations uncertainty node configured to receive one or more foam systems considerations from the one or more inputs;

a foam systems recommendations decision node configured to receive one or more foam systems recommendations from the one or more inputs; and

a first consequences node dependent on the foam systems considerations uncertainty node and the foam systems recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam systems considerations and the one or more foam systems recommendations; and

a second section, comprising;

a foam systems designs uncertainty node configured to receive one or more foam system designs from the one or more inputs;

a foam system designs recommendations decision node configured to receive one or more foam system designs recommendations from the one or more inputs; and

a second consequences node dependent on the foam systems designs uncertainty node and the foam system designs recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam system designs and the one or more foam system designs recommendations.

27. The system of claim 26, wherein the foam UBD BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.

28. The system of claim 26, comprising a user interface configured to display the foam UBD BDN model and receive user selections of the one or more inputs.

29. The system of claim 26, wherein the one or more foam systems considerations and the one or more foam systems designs are each associated with a respective plurality of probabilities.

30. A computer-implemented method for an underbalanced drilling expert system having a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the foam UBD BDN model, the one or more nodes comprising;

a foam systems considerations uncertainty node configured to receive one or more foam systems considerations; and

a foam systems recommendations decision node configured to receive one or more foam systems recommendations;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the foam UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and

providing the one or more underbalanced drilling recommendations to a user.

31. The computer-implemented method of claim 30, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the foam UBD BDN model.

32. The computer-implemented method of claim 30, comprising:

providing the one or more inputs to one or more nodes of a second section of the foam UBD BDN model, the one or more nodes comprising;

a foam systems designs uncertainty node configured to receive one or more foam system designs;

a foam system designs recommendations decision node configured to receive one or more foam system designs recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the foam UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

33. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model, the gas underbalanced drilling BDN model comprising;

a first section, comprising;

a rotary and hammer drilling uncertainty node configured to receive one or more rotary and hammer drilling types from the one or more inputs;

a rotary and hammer drilling recommendations decision node configured to receive one or more rotary and hammer drilling recommendations from the one or more inputs; and

a first consequences node dependent on the rotary and hammer drilling uncertainty node and the rotary and hammer drilling recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more rotary and hammer drilling types and the one or more rotary and hammer drilling recommendations;

a second section, comprising;

a gas drilling considerations uncertainty node configured to receive one or more gas drilling considerations from the one or more inputs;

a gas drilling considerations recommendations decision node configured to receive gas drilling considerations recommendations from the one or more inputs; and

a second consequences node dependent on the gas drilling considerations uncertainty node and the gas drilling considerations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling considerations and the one or more gas drilling considerations recommendations;

a third section, comprising;

a gas drilling operations uncertainty node configured to receive one or more gas drilling operations from the one or more inputs;

a gas drilling operations recommendations decision node configured to receive gas drilling operations recommendations from the one or more inputs; and

a third consequences node dependent on the gas drilling operations uncertainty node and the gas drilling operations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling operations and the one or more gas drilling operations recommendations; and

a fourth section, comprising;

a gas drilling rig equipment uncertainty node configured to receive one or more gas drilling rig equipment from the one or more inputs;

a gas drilling rig equipment recommendations decision node configured to receive gas drilling rig equipment recommendations from the one or more inputs; and

a fourth consequences node dependent on the gas drilling rig equipment uncertainty node and the gas drilling rig equipment recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling rig equipment and the one or more gas drilling rig equipment recommendations.

34. The system of claim 33, wherein the gas UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, the third consequences node, and the fourth consequences node.

35. The system of claim 33, comprising a user interface configured to display the gas UBD BDN model and receive user selections of the one or more inputs.

36. The system of claim 33, wherein the one or more rotary and hammer drilling types, the one or more gas drilling considerations, the one or more gas drilling operations, and the one or more gas drilling rig equipment are each associated with a respective plurality of probabilities.

37. A computer-implemented method for an underbalanced drilling expert system having a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the gas underbalanced drilling BDN model, the one or more nodes comprising;

a rotary and hammer drilling uncertainty node;

a rotary and hammer recommendations decision node;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gas underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

38. The computer-implemented method of claim 37, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the gas UBD BDN model.

39. The computer-implemented method of claim 37, comprising:

providing the one or more inputs to one or more nodes of a second section of the gas UBD BDN model, the one or more nodes comprising;

a gas drilling considerations uncertainty node configured to receive one or more gas drilling considerations;

a gas drilling considerations recommendations decision node configured to receive gas drilling considerations recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

40. The computer-implemented method of claim 39, comprising:

providing the one or more inputs to one or more nodes of a third section of the gas UBD BDN model, the one or more nodes comprising;

a gas drilling operations uncertainty node configured to receive one or more gas drilling operations;

a gas drilling operations recommendations decision node configured to receive gas drilling operations recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the third section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

41. The computer-implemented method of claim 40, comprising:

providing the one or more inputs to one or more nodes of a fourth section of the gas UBD BDN model, the one or more nodes comprising;

a gas drilling rig equipment uncertainty node configured to receive one or more gas drilling rig equipment from the one or more inputs;

a gas drilling rig equipment recommendations decision node configured to receive gas drilling rig equipment recommendations;

determining the one or more underbalanced drilling recommendations at a fourth consequences node of the fourth section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

42. A system, comprising,

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

a mud cap underbalanced drilling expert system executable by the one or more processors and configured to provide one or more mud cap underbalanced drilling recommendations based on one or more inputs, the mud cap underbalanced drilling expert system comprising a mud cap underbalanced drilling Bayesian decision network (BDN) model, the mud cap underbalanced drilling BDN model comprising;

a first section, comprising;

a mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types from the one or more inputs;

a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations from the one or more inputs; and

a first consequences node dependent on the mud cap drilling types uncertainty node and the mud cap drilling types recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more mud cap drilling types and the one or more mud cap drilling types recommendations;

a second section, comprising;

a mud cap drilling problems uncertainty node configured to receive one or more mud cap drilling problems from the one or more inputs;

a mud cap drilling problems recommendations decision node configured to receive one or more mud cap drilling problems recommendations from the one or more inputs; and

a second consequences node dependent on the mud cap drilling problems uncertainty node and the mud cap drilling problems recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more mud cap drilling problems and the one or more mud cap drilling problems recommendations; and

a third section, comprising;

a floating mud cap drilling considerations uncertainty node configured to receive one or more floating mud cap drilling considerations types from the one or more inputs;

a floating mud cap drilling recommendations decision node configured to receive one or more floating mud cap drilling recommendations from the one or more inputs; and

a third consequences node dependent on the floating mud cap drilling considerations uncertainty node and the floating mud cap drilling recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more floating mud cap drilling considerations types and the one or more floating mud cap drilling recommendations.

43. The system of claim 42, wherein the mud cap UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, and the third consequences node.

44. The system of claim 42, comprising a user interface configured to display the mud cap UBD BDN model and receive user selections of the one or more inputs.

45. The system of claim 42, wherein the one or more mud cap drilling types, the one or more mud cap drilling problems, and the one or more floating mud cap considerations are each associated with a respective plurality of probabilities.

46. A computer-implemented method for an underbalanced drilling expert system having a mud cap underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the mud cap UBD BDN model, the one or more nodes comprising;

a mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types;

a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the mud cap UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and

providing the one or more underbalanced drilling recommendations to a user.

47. The computer-implemented method of claim 46, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the mud cap UBD BDN model.

48. The computer-implemented method of claim 46, comprising:

providing the one or more inputs to one or more nodes of a second section of the mud cap UBD BDN model, the one or more nodes comprising;

a mud cap drilling problems uncertainty node configured to receive one or more mud cap drilling problems;

a mud cap drilling problems recommendations decision node configured to receive one or more mud cap drilling problems recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

49. The computer-implemented method of claim 48, comprising:

providing the one or more inputs to one or more nodes of a third section of the mud cap UBD BDN model, the one or more nodes comprising;

a floating mud cap drilling considerations uncertainty node configured to receive one or more floating mud cap drilling considerations;

a floating mud cap drilling recommendations decision node configured to receive one or more floating mud cap drilling recommendations;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the mud cap UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

50. A system, comprising,

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

a underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced expert system comprising an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model, the UBLD BDN model comprising;

a first section, comprising;

a UBLD plans uncertainty node configured to receive one or more UBLD plans from the one or more inputs;

a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations from the one or more inputs; and

a first consequences node dependent on the UBLD planning uncertainty node and the UBLD planning recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD plans and the one or more UBLD plans recommendations;

a second section, comprising;

a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems from the one or more inputs;

a UBLD advantages decision node configured to receive one or more UBLD advantages from the one or more inputs; and

a second consequences node dependent on the UBLD problems uncertainty node and the UBLD advantages decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD solvable problems and the one or more UBLD advantages; and

a third section, comprising;

a UBLD considerations uncertainty node configured to receive one or more UBLD considerations from the one or more inputs;

a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations from the one or more inputs; and

a third consequences node dependent on the UBLD considerations uncertainty node and the UBLD recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD considerations and the one or more UBLD considerations recommendations.

51. The system of claim 50, wherein the UBLD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, and the third consequences node.

52. The system of claim 50, comprising a user interface configured to display the UBLD BDN model and receive user selections of the one or more inputs.

53. The system of claim 50, wherein the one or more UBLD plans, the one or more UBLD solvable problems, and the one or more UBLD considerations are each associated with a respective plurality of probabilities.

54. A computer-implemented method for an underbalanced drilling expert system having an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the UBLD BDN model, the one or more nodes comprising;

a UBLD plans uncertainty node configured to receive one or more UBLD plans;

a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

55. The computer-implemented method of claim 54, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBLD BDN model.

56. The computer-implemented method of claim 54, comprising:

providing the one or more inputs to one or more nodes of a second section of the UBLD BDN model, the one or more nodes comprising;

a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems;

a UBLD advantages decision node configured to receive one or more UBLD advantages;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

57. The computer-implemented method of claim 56, comprising:

providing the one or more inputs to one or more nodes of a third section of the UBLD BDN model, the one or more nodes comprising;

a UBLD considerations uncertainty node configured to receive one or more UBLD considerations;

a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

58. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

an underbalanced drilling (UBD) expert system executable by the one or more processors and configured to provide one or more UBD recommendations based on one or more inputs, the UBD expert system comprising an underbalanced coil tube (UBCT) Bayesian decision network (BDN) model, the UBCT BDN model comprising;

a first section, comprising;

a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans from the one or more inputs;

a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements from the one or more inputs; and

a first consequences node dependent on the UBCT preplanning uncertainty node and the UBCT preplanning recommendations decision node and configured to output the one or more UBCT drilling requirements based on one or more Bayesian probabilities calculated from the one or more UBCT preplans and the one or more UBCT preplan requirements; and

a second section, comprising;

a UBCT considerations uncertainty node configured to receive one or more UBCT considerations from the one or more inputs;

a UBCT recommendations decision node configured to receive one or more UBCT recommendations from the one or more inputs; and

a second consequences node dependent on the UBCT considerations uncertainty node and the UBCT recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBCT considerations and the one or more UBCT recommendations.

59. The system of claim 58, wherein the UBCT BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.

60. The system of claim 58, comprising a user interface configured to display the UBCT BDN model and receive user selections of the one or more inputs.

61. The system of claim 58, wherein the one or more UBCT preplans and the one or more UBCT considerations are each associated with a respective plurality of probabilities.

62. A computer-implemented method for an underbalanced drilling expert system having an underbalanced coil tube (UBCT) drilling Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the UBCT BDN model, the one or more nodes comprising;

a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans;

a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBCT BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;

providing the one or more underbalanced drilling recommendations to a user.

63. The computer-implemented method of claim 62, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBCT BDN model.

64. The computer-implemented method of claim 62, comprising:

providing the one or more inputs to one or more nodes of a second section of the UBCT BDN model, the one or more nodes comprising;

a UBCT considerations uncertainty node configured to receive one or more UBCT considerations;

a UBCT recommendations decision node configured to receive one or more UBCT recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the UBCT BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

65. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a snubbing and stripping Bayesian decision network (BDN) model, the snubbing and stripping BDN model comprising

a first section, comprising;

a snubbing types uncertainty node configured to receive one or more snubbing types from the one or more inputs;

a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations from the one or more inputs; and

a first consequences node dependent on the snubbing types uncertainty node and the snubbing types recommendations decision node and configured to output the one or more underbalanced recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing types and the one or more snubbing types recommendations; and

a second section, comprising;

a snubbing units uncertainty node configured to receive one or more snubbing units from the one or more inputs;

a snubbing units recommendations decision node configured to receive one or more snubbing units recommendations from the one or more inputs; and

a second consequences node dependent on the snubbing units uncertainty node and the snubbing units recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing units types and the one or more snubbing units recommendations; and

a third section, comprising;

a snubbing operations uncertainty node configured to receive one or more snubbing operations from the one or more inputs;

a snubbing operations recommendations decision node configured to receive one or more snubbing operations recommendations from the one or more inputs; and

a third consequences node dependent on the snubbing operations uncertainty node and the snubbing operations recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing operations and the one or more snubbing operations recommendations; and

a fourth section, comprising;

a stripping procedures uncertainty node configured to receive one or more stripping procedures from the one or more inputs;

a stripping procedures recommendations decision node configured to receive one or more stripping procedures recommendations from the one or more inputs; and

a fourth consequences node dependent on the stripping procedures uncertainty node and the stripping procedures recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more stripping procedures and the one or more stripping procedures recommendations.

66. The system of claim 65, wherein the snubbing and stripping BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.

67. The system of claim 65, comprising a user interface configured to display the snubbing and stripping BDN model and receive user selections of the one or more inputs.

68. The system of claim 65, wherein the one or more snubbing types, the one or more snubbing units, the one or more snubbing operations, and the one or more stripping procedures are each associated with a respective plurality of probabilities.

69. A computer-implemented method for an underbalanced drilling expert system having a snubbing and stripping Bayesian decision network (BDN) model, the method comprising:

receiving one or more inputs;

providing the one or more inputs to one or more nodes of a first section of the snubbing and stripping BDN model, the one or more nodes comprising;

a snubbing types uncertainty node configured to receive one or more snubbing types;

a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations;

determining one or more underbalanced drilling recommendations at a consequences node of the first section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and

providing the one or more underbalanced drilling recommendations to a user.

70. The computer-implemented method of claim 69, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the snubbing and stripping BDN model.

71. The computer-implemented method of claim 69, comprising:

providing the one or more inputs to one or more nodes of a second section of the snubbing and stripping BDN model, the one or more nodes comprising;

a snubbing units uncertainty node configured to receive one or more snubbing units;

a snubbing units recommendations decision node configured to receive one or more snubbing units recommendations;

determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

72. The computer-implemented method of claim 71, comprising:

providing the one or more inputs to one or more nodes of a third section of the snubbing and stripping BDN model, the one or more nodes comprising;

a snubbing operations uncertainty node configured to receive one or more snubbing operations;

a snubbing operations recommendations decision node configured to receive one or more snubbing operations recommendations;

determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.

73. The computer-implemented method of claim 72, comprising:

providing the one or more inputs to one or more nodes of a fourth section of the snubbing and stripping BDN model, the one or more nodes comprising;

a stripping procedures uncertainty node configured to receive one or more stripping procedures;

a stripping procedures recommendations decision node configured to receive one or more stripping procedures recommendations;

determining the one or more underbalanced drilling recommendations at a fourth consequences node of the fourth section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.