CA2511203A1 - System and method for rig state detection - Google Patents
System and method for rig state detection Download PDFInfo
- Publication number
- CA2511203A1 CA2511203A1 CA002511203A CA2511203A CA2511203A1 CA 2511203 A1 CA2511203 A1 CA 2511203A1 CA 002511203 A CA002511203 A CA 002511203A CA 2511203 A CA2511203 A CA 2511203A CA 2511203 A1 CA2511203 A1 CA 2511203A1
- Authority
- CA
- Canada
- Prior art keywords
- drilling
- rig
- state
- detected
- event
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract 73
- 238000001514 detection method Methods 0.000 title claims abstract 16
- 238000005553 drilling Methods 0.000 claims abstract 69
- 238000001914 filtration Methods 0.000 claims abstract 6
- 238000005259 measurement Methods 0.000 claims abstract 6
- 239000002245 particle Substances 0.000 claims abstract 5
- 230000000694 effects Effects 0.000 claims 5
- 230000001066 destructive effect Effects 0.000 claims 1
- 230000003068 static effect Effects 0.000 claims 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Geology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Geochemistry & Mineralogy (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Fluid Mechanics (AREA)
- Environmental & Geological Engineering (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Earth Drilling (AREA)
- Geophysics And Detection Of Objects (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Sampling And Sample Adjustment (AREA)
- Circuits Of Receivers In General (AREA)
Abstract
A method and system is disclosed for automatically detecting the state of a drilling rig during the drilling process of a wellbore. Two or more, but preferably four independent input data channels are received, each input data channel representing a series of measurements made over time during the drilling process. Based on the input channels the most likely state of the drilling rig is detected from at least three possible rig states. The detection method is preferably probabilistic and even more preferably based on particle filtering techniques. The preferred systems and methods disclosed are also capable of detecting events and displaying or notifying drilling personnel of the detected events and suggesting corrective action.
Claims (65)
1. A Method for drilling while automatically detecting the state of a drilling rig during the drilling process of a wellbore comprising the steps of:
receiving two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process; and automatically detecting the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels.
receiving two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process; and automatically detecting the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels.
2. A method according to claim 1 further comprising the step of altering activity relating to drilling based on the detection of the most likely state of the drilling rig.
3. A method according to claim 1 further comprising the step of investigating data relating to the drilling process and/or characteristics of the earth surrounding the wellbore collected during the drilling process.
4. A method according to claim 1 wherein the two or more input data channels are represent measurements of equipment on the drilling rig.
5. A method according to claim 1 wherein the two or more input data channels include two or more of the following input channels: hookload, block position, torque, and stand pipe pressure.
6. A method according to claim 1 wherein the step of receiving includes at least three independent input channels and the detection is based on the at least three input channels.
7. A method according to claim 5 wherein the step of receiving includes at least four independent input channels and the detection is based on the at least four input channels.
8. A method according to claim 1 wherein the step of detecting further comprises generating a probability associated with each possible rig state.
9. A method according to claim 1 further comprising the step of predicting a future rig state based in part on the current rig state.
10. A method according to claim 1 wherein the most likely state is detected from at least four possible rig states.
11. A method according to claim 10 wherein the most likely state is detected from at least six possible rig states.
12. A method according to claim 11 wherein the most likely state is detected from at least 10 possible rig states.
13. A method according to claim 1 wherein the at least three possible rig states include three or more of the following rig states: DrillRot, DrillSlide, RihPumpRot, RihPump, Rih, PoohPumpRot, PoohPump, Pooh, StaticPumpRot, StaticPump, Static, InSlips, and Unclassified.
14. A method according to claim 1 wherein the step of automatically detecting makes use of a fuzzy logic algorithm.
15. A method according to claim 1 wherein the step of automatically detecting makes use of a probabilistic technique.
16. A method according to claim 1 wherein the step of automatically detecting make use of a Bayesian technique.
17. A method according to claim 1 wherein the step of automatically detecting make use of a sequential Bayesian technique.
18. A method according to claim 1 wherein a particle filtering technique is used in the step of automatic detection.
19. A method according to claim 1 wherein a parametric particle filtering technique is used in the step of automatic detection.
20. A method according to claim 1 wherein the most likely state of the drilling rig is detected by calculating a probability distribution of the possible rig states using a probabilistic model, the probabilistic model being based on changepoints and parameters between the changepoints, where the changepoints are derived from the two or more input channels.
21. A method according to claim 20 wherein the changepoints are detected separately for each input channel.
22. A method according to claim 21 wherein the changepoints are detected using a sequential Bayesian technique.
23. A method according to claim 1 wherein a Kalman filtering technique is used in the step of automatic detection.
24. A method according to claim l wherein the step of automatically detecting is based at least in part on binary indicators from drilling acquisition system.
25. A method according to claim 24 wherein the binary indicators include bit on bottom, and bit not on bottom.
26. A method according to claim 24 wherein the binary indicators include in slips, not in slips.
27. A method according to claim 1 further comprising the step of detecting a drilling event based at least in part on (i) the automatically detected most likely state of the drilling rig, and (ii) other information.
28. A method according to claim 27 wherein said step of detecting a drilling event is performed automatically.
29. A method according to claim 27 wherein the step of detecting a drilling event involves comparing values derived from the other information with threshold values, and different threshold values are used depending upon the automatically detected most likely state.
30. A method according to claim 28 wherein the drilling event being detected is a washout.
31. A method according to claim 28 wherein the drilling event being detected is a stuck pipe.
32. A method according to claim 28 wherein the drilling event being detected is a predetermined level of bit wear.
33. A method according to claim 28 wherein the step of detecting a drilling event involves tendency analysis.
34. A method according to claim 28 wherein the step of detecting a drilling event involves torque and drag analysis.
35. A method according to claim 28 further comprising the step of notifying drilling personnel of detected event.
36. A method according to claim 35 wherein the detected event is undesirable, and the notification is a warning of the undesirable event,
37. A method according to claim 28 further comprising the step of suggesting to drilling personnel activity in response to the detected event.
38. A method according to claim 27 wherein the detecting of the drilling event is in part a manual process.
39. A method according to claim 38 wherein the other information includes MWD data.
40. A method according to claim 39 wherein the event detected is destructive vibration modes.
41. A method according to claim 28 wherein the event is detected in part using information from an earth model.
42. A method according to claim 1 wherein the automatic detection is based in part on knowledge base information.
43. A method according to claim 1 wherein said steps of receiving and automatically detecting are repeated such that the most likely states of drilling rig is detected over a period of time.
44. A system for drilling while automatically detecting the state of a drilling rig during the drilling process of a wellbore comprising:
a storage system adapted to receive two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process; and a processing system adapted and programmed to automatically detect the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels.
a storage system adapted to receive two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process; and a processing system adapted and programmed to automatically detect the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels.
45. A system for drilling according to claim 44 further comprising a user interface to display information based on the detected most likely state of the drilling rig to drilling personnel such that drilling activity can be altered.
46. A system for drilling according to claim 44 wherein the two or more input data channels are represent measurements of equipment on the drilling rig.
47. A system for drilling according to claim 44 wherein the storage system is adapted to receive at least three independent input channels and the detection is based on the at least three input channels.
48. A system for drilling according to claim 44 the processing system is further adapted and programmed to generate a probability associated with each possible rig state, and the detection of future rig states based in part on the current rig state probability.
49. A system for drilling according,to claim 44 wherein the processing system detects the most likely state of the drilling rig using a probabilistic technique.
50. A system for drilling according to claim 49 wherein the probabilistic technique includes a sequential Bayesian technique based on particle filtering.
51. A for drilling according to claim 49 wherein the processing system detects the most likely state of the drilling rig by calculating a probability distribution of the possible rig states using a probabilistic model, the probabilistic model being based on changepoints and parameters between the changepoints, where the changepoints are derived from the two or more input channels.
52. A system for drilling according to claim 51 wherein the changepoints are detected separately for each input channel.
53. A system for drilling according to claim 52 wherein the changepoints are detected using a sequential Bayesian technique.
54. A system for drilling according to claim 44 wherein the processing system is further adapted and programmed to detect a drilling event based at least in part on (i) the automatically detected most likely state of the drilling rig, and (ii) other information.
55. A system for drilling according to claim 54 wherein the detecting of a drilling event is performed automatically.
56. A system for drilling according to claim 55 wherein the detecting of a drilling event is event is in part performed using information from an earth model.
57. A computer readable medium capable of causing a computer system to carry out the following steps during a the drilling process of a wellbore:
receiving two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process;
automatically detecting the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels; and displaying information based on the detected most likely state of the drilling rig to drilling
receiving two or more independent input data channels, each input data channel representing a series of measurements made over time during the drilling process;
automatically detecting the most likely state of the drilling rig from at least three possible rig states, the detection based on the two or more input channels; and displaying information based on the detected most likely state of the drilling rig to drilling
58 personnel such that drilling activity can be altered.
58. A computer readable medium according to claim 57 further capable of causing the computer system to carry out the step of altering activity relating to drilling based on the detection of the most likely state of the drilling rig.
58. A computer readable medium according to claim 57 further capable of causing the computer system to carry out the step of altering activity relating to drilling based on the detection of the most likely state of the drilling rig.
59. A computer readable medium according to claim 57 wherein the detection is based on at least three input channels.
60. A computer readable medium according to claim 57 wherein a probability associated with each possible rig state is generated by the computer system.
61. A computer readable medium according to claim 57 wherein the computer system detects the most likely state of the drilling rig using a probabilistic technique.
62. A computer readable medium according to claim 61 wherein the probabilistic technique includes a sequential Bayesian technique based on particle filtering.
63. A computer readable medium according to claim 61 wherein the probabilistic technique includes analyzing changepoints and parameters for segments between the changepoints derived from the two or more input channels.
64. A computer readable medium according to claim 57 wherein the computer system is further caused to detect a drilling event based at least in part on (i) the automatically detected most likely state of the drilling rig, and (ii) other information.
65. A computer readable medium according to claim 57 wherein the detecting of a drilling event is performed automatically.
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US33063402A | 2002-12-27 | 2002-12-27 | |
US10/330,634 | 2002-12-27 | ||
US10/400,125 | 2003-03-26 | ||
US10/400,125 US7128167B2 (en) | 2002-12-27 | 2003-03-26 | System and method for rig state detection |
PCT/GB2003/005596 WO2004059123A1 (en) | 2002-12-27 | 2003-12-22 | System and method for rig state detection |
Publications (2)
Publication Number | Publication Date |
---|---|
CA2511203A1 true CA2511203A1 (en) | 2004-07-15 |
CA2511203C CA2511203C (en) | 2011-12-06 |
Family
ID=29423314
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CA2511203A Expired - Lifetime CA2511203C (en) | 2002-12-27 | 2003-12-22 | System and method for rig state detection |
Country Status (5)
Country | Link |
---|---|
AU (1) | AU2003290305A1 (en) |
CA (1) | CA2511203C (en) |
GB (2) | GB2396697A (en) |
NO (1) | NO337843B1 (en) |
WO (1) | WO2004059123A1 (en) |
Families Citing this family (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7302346B2 (en) | 2005-12-19 | 2007-11-27 | Schlumberger Technology Corporation | Data logging |
US8042624B2 (en) | 2008-04-17 | 2011-10-25 | Baker Hughes Incorporated | System and method for improved depth measurement correction |
EA201391458A1 (en) * | 2011-04-04 | 2014-03-31 | Лэндмарк Графикс Корпорейшн | MONITORING PROTECTION MEANS |
CA2836830C (en) * | 2011-06-29 | 2017-05-09 | The Governors Of The University Of Calgary | Autodriller system |
CN103437751B (en) * | 2013-08-23 | 2016-03-16 | 中国石油集团川庆钻探工程有限公司 | Real-time automatic correction system for drill bit position |
US10094210B2 (en) | 2013-10-01 | 2018-10-09 | Rocsol Technologies Inc. | Drilling system |
GB2540283B (en) * | 2014-05-27 | 2020-07-22 | Halliburton Energy Services Inc | Elastic pipe control and compensation with managed pressure drilling |
CN104234702B (en) * | 2014-08-27 | 2017-02-01 | 中国电子科技集团公司第二十二研究所 | Method and device for acquiring position of storage-type logging instrument |
US10400549B2 (en) | 2015-07-13 | 2019-09-03 | Halliburton Energy Services, Inc. | Mud sag monitoring and control |
US11422999B2 (en) | 2017-07-17 | 2022-08-23 | Schlumberger Technology Corporation | System and method for using data with operation context |
CN108166967B (en) * | 2017-12-25 | 2023-10-03 | 江苏徐工工程机械研究院有限公司 | Control system of rotary jetting drilling machine and rotary jetting drilling machine |
US10890060B2 (en) | 2018-12-07 | 2021-01-12 | Schlumberger Technology Corporation | Zone management system and equipment interlocks |
US10907466B2 (en) | 2018-12-07 | 2021-02-02 | Schlumberger Technology Corporation | Zone management system and equipment interlocks |
Family Cites Families (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3605919A (en) * | 1969-05-16 | 1971-09-20 | Automatic Drilling Mach | Drilling rig control |
GB2035554B (en) * | 1978-10-10 | 1983-08-17 | Dresser Ind | Well logging system and method |
GB8411361D0 (en) | 1984-05-03 | 1984-06-06 | Schlumberger Cambridge Researc | Assessment of drilling conditions |
US4756188A (en) * | 1986-06-30 | 1988-07-12 | Exploration Logging, Inc. | Method and apparatus for compensating for drilling line stretch in determining equipment depth in a well and for measurement of hookload on the traveling block of a drilling rig |
US5062048A (en) * | 1987-12-17 | 1991-10-29 | Halliburton Logging Services, Inc. | Stretch corrected wireline depth measuring error and log quality indicator method and apparatus |
GB9621871D0 (en) | 1996-10-21 | 1996-12-11 | Anadrill Int Sa | Alarm system for wellbore site |
US6347282B2 (en) * | 1997-12-04 | 2002-02-12 | Baker Hughes Incorporated | Measurement-while-drilling assembly using gyroscopic devices and methods of bias removal |
US6237404B1 (en) * | 1998-02-27 | 2001-05-29 | Schlumberger Technology Corporation | Apparatus and method for determining a drilling mode to optimize formation evaluation measurements |
EP1143405B1 (en) * | 2000-04-04 | 2016-06-01 | EM Microelectronic-Marin SA | Driving method and apparatus for a multiplexed display with normal working mode and standby mode |
US6438495B1 (en) | 2000-05-26 | 2002-08-20 | Schlumberger Technology Corporation | Method for predicting the directional tendency of a drilling assembly in real-time |
US6516663B2 (en) * | 2001-02-06 | 2003-02-11 | Weatherford/Lamb, Inc. | Downhole electromagnetic logging into place tool |
US6450259B1 (en) * | 2001-02-16 | 2002-09-17 | Halliburton Energy Services, Inc. | Tubing elongation correction system & methods |
US6892812B2 (en) * | 2002-05-21 | 2005-05-17 | Noble Drilling Services Inc. | Automated method and system for determining the state of well operations and performing process evaluation |
-
2003
- 2003-10-01 GB GB0322966A patent/GB2396697A/en not_active Withdrawn
- 2003-12-22 GB GB0511693A patent/GB2412678B/en not_active Expired - Fee Related
- 2003-12-22 CA CA2511203A patent/CA2511203C/en not_active Expired - Lifetime
- 2003-12-22 AU AU2003290305A patent/AU2003290305A1/en not_active Abandoned
- 2003-12-22 WO PCT/GB2003/005596 patent/WO2004059123A1/en not_active Application Discontinuation
-
2005
- 2005-06-23 NO NO20053079A patent/NO337843B1/en not_active IP Right Cessation
Also Published As
Publication number | Publication date |
---|---|
AU2003290305A1 (en) | 2004-07-22 |
GB0322966D0 (en) | 2003-11-05 |
NO20053079L (en) | 2005-09-16 |
GB2412678A (en) | 2005-10-05 |
CA2511203C (en) | 2011-12-06 |
GB2396697A (en) | 2004-06-30 |
GB2412678B (en) | 2006-05-10 |
GB0511693D0 (en) | 2005-07-13 |
NO20053079D0 (en) | 2005-06-23 |
AU2003290305A8 (en) | 2004-07-22 |
NO337843B1 (en) | 2016-06-27 |
WO2004059123A1 (en) | 2004-07-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CA2925268C (en) | Real-time risk prediction during drilling operations | |
AU2013403353B2 (en) | Real-time risk prediction during drilling operations | |
US6868920B2 (en) | Methods and systems for averting or mitigating undesirable drilling events | |
EP2773848B1 (en) | Method and system for predicting a drill string stuck pipe event | |
CA2511203A1 (en) | System and method for rig state detection | |
EP2893378B1 (en) | Model-driven surveillance and diagnostics | |
Ahmed et al. | Stuck Pipe Early Warning System Utilizing Moving Window Machine Learning Approach | |
NO20210175A1 (en) | Early warning and automated detection for lost circulation in wellbore drilling | |
CN105102760A (en) | System, method and computer program product for wellbore event modeling using rimlier data | |
Li et al. | Incipient fault detection for geological drilling processes using multivariate generalized Gaussian distributions and Kullback–Leibler divergence | |
US11959360B2 (en) | Holistic approach to hole cleaning for use in subsurface formation exploration | |
CN114352255B (en) | Method and device for monitoring state of oil and gas drilling shaft | |
Liang | Application of quantitative risk analysis to pore pressure and fracture gradient prediction | |
CN107609330B (en) | Access log mining-based internal threat abnormal behavior analysis method | |
US20180112522A1 (en) | Method for estimating a transit time of an element circulating in a borehole | |
Lafond et al. | Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach | |
Ramos et al. | A Real-Time Probabilistic Slide Drilling Dysfunction Advisory to Assist Remote Directional Drilling Operations | |
CN111594197B (en) | Tunnel broken section advanced intelligent sensing method based on TBM rock machine information | |
CN117854232A (en) | Improved landslide hazard early warning method and system based on landslide tangent angle algorithm | |
US20240151132A1 (en) | Event detection using hydraulic simulations | |
Zomarah et al. | Early Warning Method to Detect Mud Motor Stallings Incidents in Drilling Operations Using Machine Learning | |
Solano | Evaluation of new methods for processing drilling data to determine the cause of changes in bit performance |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
EEER | Examination request | ||
MKEX | Expiry |
Effective date: 20231222 |