System and method for automating exploration or production of subterranean resource

• US 8,606,734 B2
• Filed: 07/23/2009
• Issued: 12/10/2013
• Est. Priority Date: 07/23/2008
• Status: Active Grant

• Current Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Original Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Inventors: Du Castel, Bertrand, Barrow, Harry
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Smith, Paulinho E
• Application Number:
  US13/054,039
• Publication Number:
  US 20110155462A1
• Time in Patent Office: 1,601 Days
• Field of Search: None
• US Class Current: 706/45
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method and system for automating exploration and production of subterranean resources, using data collected from exploration and production activities, including drilling rig data and/or the like is described. The method and system include the use of a knowledge representation containing representation of uncertainty in the exploration and production operations.

Claims

1. A method for automating exploration or production of subterranean resources, comprising:

representing a first body of knowledge relevant to exploration or production of subterranean resources in an ontology, the first body of knowledge comprises a Bayesian Belief Network comprising conditional probabilities linking exploration or production problems to observable or detectable physical quantities;

representing a second body of knowledge relevant to exploration or production of subterranean resources in the ontology, wherein the second body of knowledge is a stochastic grammar comprising rules to interpret a data stream in terms of activities performed during an exploration or production operation;

producing a first interpretation product from the first body of knowledge, wherein the first interpretation product comprises a first set of indexed values;

producing a second interpretation product from the second body of knowledge, wherein the second interpretation product comprises a second set of indexed values;

correlating the first and second interpretation products to produce rules reflecting how the first set of indexed values affect the second set of indexed values; and

using the rules to automate at least one task associated with the exploration or production of subterranean resources.

2. The method of claim 1, wherein:

the first interpretation product is produced by a first interpretation program derived from a representation of the Bayesian Belief Network useable by a Bayesian Belief Network interpreter from the ontology representation of the Bayesian Belief Network; and

the second interpretation product is produced by a second interpretation program derived from an interpretation program for exploration or production equipment data from the stochastic grammar, and wherein the interpretation program computes first probabilities of states reflecting activities from transitions from a one or more of the states or between the states and computes second probabilities of states reflecting activities from the equipment data, and

combining the first probabilities with the second probabilities to produce the second interpretation results.

3. The method of claim 1, wherein the at least one automated task is an operational task comprising operation of a subterranean exploration or production equipment.

4. The method of claim 1, wherein the at least one automated task is an automated training operation for the operation of subterranean exploration or production equipment.

5. The method of claim 1, wherein the subterranean exploration or production comprises drilling for petroleum resources, wherein the first body of knowledge is a Bayesian Belief Network linking drilling problems to observable physical conditions, and wherein the second body of knowledge is a stochastic grammar for interpreting a drilling states data channel relating to activities of a driller;

wherein the correlation comprises correlation of probabilities assigned to various drilling problems to probabilities of the activities of the driller, thereby producing rules indicative of how the driller reacts to particular drilling problems given particular conditions.

6. The method of claim 5 further comprises:

receiving the data stream;

interpreting the data stream to compute probabilities of drilling problems;

applying the rules indicative of how a driller reacts to the interpretation of the data stream to derive a suggested action.

7. The method of claim 6, further comprising:

suggesting the suggested action to the driller.

8. The method of claim 6 further comprising:

using the suggested action to control the operation of drilling equipment.

9. The method of claim 6 further comprising:

comparing the suggested action to an action suggested by a student driller and using the comparison to provide feedback to the student.