WO2023009027A1 - Procédé et système d'avertissement concernant des anomalies à venir lors d'un processus de forage - Google Patents
Procédé et système d'avertissement concernant des anomalies à venir lors d'un processus de forage Download PDFInfo
- Publication number
- WO2023009027A1 WO2023009027A1 PCT/RU2021/000623 RU2021000623W WO2023009027A1 WO 2023009027 A1 WO2023009027 A1 WO 2023009027A1 RU 2021000623 W RU2021000623 W RU 2021000623W WO 2023009027 A1 WO2023009027 A1 WO 2023009027A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- data
- drilling
- training
- real
- warning
- Prior art date
Links
- 238000005553 drilling Methods 0.000 title claims abstract description 144
- 238000000034 method Methods 0.000 title claims abstract description 103
- 230000008569 process Effects 0.000 title claims abstract description 45
- 238000012549 training Methods 0.000 claims abstract description 64
- 230000004044 response Effects 0.000 claims abstract description 20
- 238000010801 machine learning Methods 0.000 claims abstract description 9
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 description 20
- 238000012545 processing Methods 0.000 description 18
- 239000012634 fragment Substances 0.000 description 17
- 238000013528 artificial neural network Methods 0.000 description 15
- 238000010586 diagram Methods 0.000 description 15
- 238000012360 testing method Methods 0.000 description 11
- 230000002159 abnormal effect Effects 0.000 description 10
- 238000013459 approach Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 10
- 238000007781 pre-processing Methods 0.000 description 8
- 230000006399 behavior Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 7
- 230000036541 health Effects 0.000 description 7
- 239000012530 fluid Substances 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000010276 construction Methods 0.000 description 5
- 238000003860 storage Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- FFKXTXJQZGIKQZ-UHFFFAOYSA-N ROPA Natural products O1C2C3C=C(CO)CC(C(C(C)=C4)=O)(O)C4C3(O3)C(C)CC2(C(C)=C)OC31CC1=CC=CC=C1 FFKXTXJQZGIKQZ-UHFFFAOYSA-N 0.000 description 3
- 230000002547 anomalous effect Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 3
- 238000005755 formation reaction Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 230000007257 malfunction Effects 0.000 description 3
- 239000011435 rock Substances 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
- 230000010267 cellular communication Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 230000009918 complex formation Effects 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 150000002430 hydrocarbons Chemical class 0.000 description 2
- 230000002045 lasting effect Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002243 precursor Substances 0.000 description 2
- 230000003449 preventive effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000011664 signaling Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 101100009524 Anas platyrhynchos HSD17B12 gene Proteins 0.000 description 1
- 238000012935 Averaging Methods 0.000 description 1
- UEEJHVSXFDXPFK-UHFFFAOYSA-N N-dimethylaminoethanol Chemical compound CN(C)CCO UEEJHVSXFDXPFK-UHFFFAOYSA-N 0.000 description 1
- 101150018840 SPM1 gene Proteins 0.000 description 1
- 101100335888 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) GAL83 gene Proteins 0.000 description 1
- 101100256975 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) SIP2 gene Proteins 0.000 description 1
- 101100203850 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) SPT2 gene Proteins 0.000 description 1
- 101100291461 Schizosaccharomyces pombe (strain 972 / ATCC 24843) spm2 gene Proteins 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000003339 best practice Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 239000012972 dimethylethanolamine Substances 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005183 dynamical system Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000011990 functional testing Methods 0.000 description 1
- 231100001261 hazardous Toxicity 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000013349 risk mitigation Methods 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Definitions
- the described invention aims to detect abnormal parameters during the drilling process in order to predict potential accidents and provide operational personnel with sufficient time to make decisions and prevent real problems, thereby providing significant time and cost savings.
- the system proposed by the described invention is based on a computerized method of warning about a future anomaly during drilling. This method includes:
- the appropriate Regression Models (131) are selected from among the trained Regression Models.
- the appropriate Regression Models (131) can be selected during the use phase depending on the composition of the pre-processed well data coming in real time.
- Regression Models for the relevant drilling parameters eg, SPPA, TQA, and HKLA
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mining & Mineral Resources (AREA)
- Geology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Environmental & Geological Engineering (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Automation & Control Theory (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Fluid Mechanics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
L'invention concerne un procédé informatique d'avertissement sur des anomalies futures lors d'un processus de forage, lequel consiste à: générer un premier ensemble d'apprentissage de données pour un premier algorithme d'apprentissage machine (MLA) sur la base de données historiques de puits; instruire un premier algorithme MLA en utilisant le premier ensemble d'apprentissage de données afin de générer un premier modèle de réponse normale; déterminer une anomalie potentielle de forage sur la base du premier modèle de réponse normale et de données en temps réel du puits; générer un second ensemble d'apprentissage de données pour un second algorithme MLA sur la base de données historiques de puits et de données de modèle générées par le premier modèle de réponse normale; instruire le second algorithme MLA en utilisant le second ensemble d'apprentissage de données afin de générer un second modèle de réponse normale; prédire des paramètres principaux du processus de forage sur la base du second modèle de réponse normale et des données en temps réel de puits; déterminer une anomalie factuelle de forage sur la base des paramètres principaux prédits du processus de forage et des données en temps réel de puits; générer un avertissement sur la base d'une anomalie potentielle de forage et/ou d'une anomalie factuelle de forage.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| RU2021122723A RU2772851C1 (ru) | 2021-07-30 | Способ и система для предупреждения о предстоящих аномалиях в процессе бурения | |
| RU2021122723 | 2021-07-30 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2023009027A1 true WO2023009027A1 (fr) | 2023-02-02 |
Family
ID=85087131
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/RU2021/000623 WO2023009027A1 (fr) | 2021-07-30 | 2021-12-29 | Procédé et système d'avertissement concernant des anomalies à venir lors d'un processus de forage |
Country Status (1)
| Country | Link |
|---|---|
| WO (1) | WO2023009027A1 (fr) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116305588A (zh) * | 2023-05-17 | 2023-06-23 | 中国航空工业集团公司沈阳空气动力研究所 | 一种风洞试验数据异常检测方法、电子设备及存储介质 |
| CN117662106A (zh) * | 2024-01-30 | 2024-03-08 | 四川霍尼尔电气技术有限公司 | 一种钻机电控系统及电控方法 |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015123591A1 (fr) * | 2014-02-13 | 2015-08-20 | Intelligent Solutions, Inc. | Système et procédé fournissant une assistance en temps réel pour une opération de forage |
| US20170308802A1 (en) * | 2016-04-21 | 2017-10-26 | Arundo Analytics, Inc. | Systems and methods for failure prediction in industrial environments |
| WO2019216891A1 (fr) * | 2018-05-09 | 2019-11-14 | Landmark Graphics Corporation | Optimisation bayésienne basée sur l'apprentissage pour l'optimisation de paramètres de forage aptes à être commandés |
-
2021
- 2021-12-29 WO PCT/RU2021/000623 patent/WO2023009027A1/fr unknown
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015123591A1 (fr) * | 2014-02-13 | 2015-08-20 | Intelligent Solutions, Inc. | Système et procédé fournissant une assistance en temps réel pour une opération de forage |
| US20170308802A1 (en) * | 2016-04-21 | 2017-10-26 | Arundo Analytics, Inc. | Systems and methods for failure prediction in industrial environments |
| WO2019216891A1 (fr) * | 2018-05-09 | 2019-11-14 | Landmark Graphics Corporation | Optimisation bayésienne basée sur l'apprentissage pour l'optimisation de paramètres de forage aptes à être commandés |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116305588A (zh) * | 2023-05-17 | 2023-06-23 | 中国航空工业集团公司沈阳空气动力研究所 | 一种风洞试验数据异常检测方法、电子设备及存储介质 |
| CN116305588B (zh) * | 2023-05-17 | 2023-08-11 | 中国航空工业集团公司沈阳空气动力研究所 | 一种风洞试验数据异常检测方法、电子设备及存储介质 |
| CN117662106A (zh) * | 2024-01-30 | 2024-03-08 | 四川霍尼尔电气技术有限公司 | 一种钻机电控系统及电控方法 |
| CN117662106B (zh) * | 2024-01-30 | 2024-04-19 | 四川霍尼尔电气技术有限公司 | 一种钻机电控系统及电控方法 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9280517B2 (en) | System and method for failure detection for artificial lift systems | |
| AU2012370482B2 (en) | Determining optimal parameters for a downhole operation | |
| US8988237B2 (en) | System and method for failure prediction for artificial lift systems | |
| US8988236B2 (en) | System and method for failure prediction for rod pump artificial lift systems | |
| US20210406792A1 (en) | Hydraulic fracturing operation planning using data-driven multi-variate statistical machine learning modeling | |
| CN109508827B (zh) | 一种基于时间递归神经网络的钻井事故预警方法 | |
| EP2893378B1 (fr) | Surveillance et diagnostics pilotés par des modèles | |
| US20180357343A1 (en) | Optimization methods for physical models | |
| Gupta et al. | Applying big data analytics to detect, diagnose, and prevent impending failures in electric submersible pumps | |
| WO2023009027A1 (fr) | Procédé et système d'avertissement concernant des anomalies à venir lors d'un processus de forage | |
| Li et al. | Diagnosis of downhole incidents for geological drilling processes using multi-time scale feature extraction and probabilistic neural networks | |
| CN105900022A (zh) | 使用概率介质的基于人工智能模型的动态过程控制的方法和系统 | |
| WO2018029454A1 (fr) | Procédé et système d'analyse de données de forage | |
| Dmitrievsky et al. | On increasing the productive time of drilling oil and gas wells using machine learning methods | |
| US20220027538A1 (en) | Hydraulic fracturing job plan real-time revisions utilizing detected response feature data | |
| US20210348490A1 (en) | Oilfield system | |
| Leng et al. | A hybrid data mining method for tunnel engineering based on real-time monitoring data from tunnel boring machines | |
| Noshi | Application of data science and machine learning algorithms for ROP optimization in west texas: turning data into knowledge | |
| Li et al. | Abnormality detection for drilling processes based on Jensen–Shannon divergence and adaptive alarm limits | |
| RU2745136C1 (ru) | Автоматизированная система выявления и прогнозирования осложнений в процессе строительства нефтяных и газовых скважин | |
| Li et al. | Rescaling method for improved machine-learning decline curve analysis for unconventional reservoirs | |
| Chernikov et al. | Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions | |
| Li et al. | Detection of downhole incidents for complex geological drilling processes using amplitude change detection and dynamic time warping | |
| JP2023547849A (ja) | ラベルなしセンサデータを用いた産業システム内の稀な障害の自動化されたリアルタイムの検出、予測、及び予防に関する、方法または非一時的コンピュータ可読媒体 | |
| Aslam et al. | Anomaly detection using explainable random forest for the prediction of undesirable events in oil wells |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21952044 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |