SYSTEM AND METHOD FOR DETERMINING DRILLING ACTIVITY

• US 20110166789A1
• Filed: 07/23/2009
• Published: 07/07/2011
• Est. Priority Date: 07/23/2008
• Status: Abandoned Application

• Current Assignee: Schlumberger Technology Corporation (SLB)
• Original Assignee: Schlumberger Technology Corporation (SLB)
• Inventors: Barrow, Harry, Du Castel, Bertrand
• Application Number:
  US13/054,086
• Publication Number:
  US 20110166789A1
• Time in Patent Office: Days
• US Class Current: 702/6
• 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 interpreting oilfield process data, including drilling rig data and/or the like, is described, the method and system including use of a knowledge representation containing a representation of uncertainty in the oilfield process operations.

Claims

1. A method of processing oilfield process status data in a data channel, comprising;

representing oilfield process knowledge in a knowledge representation, the knowledge representation including representations of uncertainty values in the oilfield process knowledge; and

generating an interpretation of the oilfield process status data from the oilfield process knowledge representation.

2. The method of claim 1, wherein:

the step of representing oilfield process knowledge in a knowledge representation comprises representing oilfield process knowledge in a knowledge representation of activities and transitions between subactivities of activities, including representing probabilities for transitions between subactivities of activities; and

the step of generating an interpretation comprises computing activity probability values corresponding to each of a set of activities from transitional probabilities and data values in the data channel.

3. The method of claim 2, wherein the step of computing activity probability values comprises;

determining detailed activities corresponding to each possible subactivity;

computing activity-to-data probability values for each ordered pair (a,d) of detailed activity (a) and data channel value (d), wherein for the given the activity (a), the data channel would indicate the data channel value (d);

for each data sample (n) in the data channel computing an activity probability vector (n) for a set of activities, wherein the probability value for each activity in the set of activities indicates the probability that the drilling rig carried out the each activity at time n, wherein

each of the activity probability vectors (205) is computed by applying the transition probabilities (203) to an activity vector (n−

1) (201);

selecting an activity-to-data probability vector (207) from a computed activity-to-data probability values, wherein the elements of the activity-to-data probability vector correspond to the activities in the set of activities and the data sample (n); and

computing an output probability vector (209) by applying the activity-to-data probability vector (207) to the first probability vector (205) and by applying Bayes Theorem to the result.

4. A method of interpreting oilfield process state data, comprising:

representing oilfield process knowledge in an activity grammar containing activity states;

transitional probabilities for transitioning from one activity to another;

configuration variables; and

leaf activity with assigned values for each configuration variable; and

interpreting input data using the activity grammar to compute probabilities for each of a set of activities defined as possible activities of an oilfield process state.

5. A method of processing drilling rig data in a data channel, comprising;

representing drilling knowledge in a knowledge representation including representing uncertainty values in the drilling knowledge; and

generating an interpretation of the drilling rig data from the drilling knowledge representation including the representation of uncertainty values.

6. The method of processing drilling rig data in the data channel of claim 5, wherein:

the representing comprises;

representing drilling knowledge in a knowledge representation of activities and transitions between subactivities of activities, including representing probabilities for transitions between subactivities of activities; and

the generating an interpretation comprises;

computing activity probability values corresponding to each of a set of activities from the transitional probabilities and data values in the data channel.

7. The method of interpreting drilling rig data in a data channel of claim 6, wherein the step of computing activity probability values comprises;

determining detailed activities corresponding to each possible subactivity;

computing activity-to-data probability values for each ordered pair (a,d) of detailed activity (a) and data channel value (d) that given the activity (a), the data channel would indicate the data channel value (d);

for each data sample (n) in the data channel computing an activity probability vector (n) for a set of activities, wherein the probability value for a each activity in the set indicates the probability that the drilling rig carried out the each activity at time n, by;

computing a first probability vector (205) by applying the transition probabilities (203) to the activity vector (n−

1) (201);

selecting an activity-to-data probability vector (207) from the computed activity-to-data probability values, wherein the elements of the activity-to-data probability vector correspond to the activities in the set of activities and the data sample (n); and

computing an output probability vector (209) by applying the activity-to-data probability vector (207) to the first probability vector (205) and by applying Bayes Theorem to the result.

8. A method of interpreting drilling rig data, comprising:

representing drilling knowledge in an activity grammar containing;

activity states;

transitional probabilities for transitioning from one activity to another;

configuration variables;

leaf activity with assigned values for each configuration variable; and

interpreting input data using the activity grammar to compute probabilities for each of a set of activities defined as possible activities of a drilling rig.

9. The method of interpreting drilling rig data of claim 8, wherein the representing drilling knowledge in an activity grammar further comprises:

for each activity that is not a leaf activity;

a start state and a finish state;

at least one subactivity;

at least one transition from the start state to at least one subactivity; and

at least one transition from at least one subactivity to the finish state.

10. A computer system for interpreting drilling data comprising:

sensors for collecting drilling data;

a storage for storing data and program instructions;

a processor having a data input/output mechanism and operable to input data on the input/output mechanism and to output data onto the input/output mechanism, and to process data according to instructions stored in the program storage;

wherein the storage contains;

a representation of drilling knowledge including representation of uncertainty values in the drilling knowledge; and

instructions that when executed causes the processor to generate an interpretation of the drilling rig data from the drilling knowledge representation including the representation of uncertainty values.

11. The computer system for interpreting drilling data of claim 10, wherein:

the representation of drilling knowledge comprises a knowledge representation of activities and transitions between subactivities of activities, including representing probabilities for transitions between subactivities of activities; and

the instructions to generate an interpretation comprises instructions to cause the processor to;

compute activity probability values corresponding to each of a set of activities from the transitional probabilities and data values in the data channel.

12. The computer system for interpreting drilling data according to claim 11, wherein the instructions to cause the processor to compute activity probability values comprises instructions to cause the processor to:

determine detailed activities corresponding to each possible subactivity;

compute activity-to-data probability values for each ordered pair (a,d) of detailed activity (a) and data channel value (d) that given the activity (a), the data channel would indicate the data channel value (d);

for each data sample (n) in the data channel compute an activity probability vector (n) for a set of activities, wherein the probability value for a each activity in the set indicates the probability that the drilling rig carried out the each activity at time n, by;

computing a first probability vector (205) by applying the transition probabilities (203) to the activity vector (n−

1) (201);

selecting an activity-to-data probability vector (207) from the computed activity-to-data probability values, wherein the elements of the activity-to-data probability vector correspond to the activities in the set of activities and the data sample (n);

computing an output probability vector (209) by applying the activity-to-data probability vector (207) to the first probability vector (205) and normalizing the result.

13. A method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities, comprising:

receiving a sequence of data values from a subterranean resource exploration operation; and

determining the probability that the data values correspond to each of several activity states using a function derived from a knowledge representation containing probabilities of transitioning from one activity state to each other of the several activity states, and a function providing a probability mapping of particular data values to possible activity states.

14. The method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities of claim 13, wherein the function derived from a knowledge representation containing probabilities of transitioning from one activity state to each other of the several activity states is a transition probability matrix in which each element is the probability of transitioning from one activity state to another activity state.

15. The method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities of claim 14 further comprising:

adjusting the function providing a probability mapping of particular data values to possible activity states to account for confusion in regard to whether particular data values correspond to actual conditions.

16. The method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities of claim 13 wherein the knowledge representation containing probabilities of transitioning from one activity state to each other of the several activity states is a representation of a stochastic grammar having rules describing possible transitions in a sequence of activities that may correspond to the sequence of oilfield data values.

17. The method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities of claim 13, wherein the function providing a probability mapping of particular data values to possible activity states is a data-to-activity state probability matrix in which each element is the probability that a given data value corresponds to a particular activity state.

18. The method for determining probabilities of data collected during exploration or production of subterranean resources corresponding to particular activities of claim 13 further comprising:

determining a first vector of probability values corresponding to a particular data item in the sequence of oilfield data values by applying the function derived from a knowledge representation containing probabilities of transitioning from one activity state to each other of the several activity states to a preceding vector of probability values corresponding to a data item preceding the particular data item to determine the probability of each of the several activity states given the probability in the preceding vector of probability values and the probabilities of transitioning from one activity state to each other of the several activity states;

determining a second vector of probability values corresponding to a particular data item in the sequence of oilfield data values by applying the function providing a probability mapping of particular data values to possible activity states to the particular data item in the sequence of oilfield data values; and

determining a probability vector for the particular data item by combining the first vector of probability values and the second vector of probability values.

19. A method of evaluating alternative hypothesis in regard to origin of data collected during exploration or production of subterranean resources, comprising:

receiving a sequence of data values from a subterranean resource exploration operation;

for each of a plurality hypothesis, determining the probability that the data values correspond to each of several activity states using;

a first function providing probabilities for transitioning from each of the several activity states to each other of the several activity states wherein the first function is derived from a knowledge representation containing probabilities of transitioning from one activity state to each other of the several activity states according to rules specified for the each of a plurality of hypothesis, and

a second function providing a probability mapping of particular data values to possible activity states wherein the second function is derived from a knowledge representation containing hypothesis specific mapping of traits to activity states and traits to data values according to rules specified for the each of a plurality of hypothesis; and

rejecting any hypothesis in which the determined probability that the data values correspond to each of several activity states is indicative of the rules specified for the hypothesis provide a poor match of the data sequence.

20. The method of evaluating alternative hypothesis in regard to origin of data collected during exploration or production of subterranean resources according to claim 19, further comprising:

storing a plurality of stochastic grammars in a knowledge representation for exploration of subterranean resources, wherein each stochastic grammar reflects data-origin-particular activities encountered in the exploration of subterranean resources and the probabilities of transitioning between those activities; and

generating the first function from the stochastic grammar.