Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods
This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/13/7/1293 |
| _version_ | 1797525999810772992 |
|---|---|
| author | Shamil Islamov Alexey Grigoriev Ilia Beloglazov Sergey Savchenkov Ove Tobias Gudmestad |
| author_facet | Shamil Islamov Alexey Grigoriev Ilia Beloglazov Sergey Savchenkov Ove Tobias Gudmestad |
| author_sort | Shamil Islamov |
| collection | DOAJ |
| description | This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models. |
| first_indexed | 2024-03-10T09:21:01Z |
| format | Article |
| id | doaj.art-afb9d4807ddd463a92f07b4b42eacab1 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T09:21:01Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-afb9d4807ddd463a92f07b4b42eacab12023-11-22T05:10:10ZengMDPI AGSymmetry2073-89942021-07-01137129310.3390/sym13071293Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning MethodsShamil Islamov0Alexey Grigoriev1Ilia Beloglazov2Sergey Savchenkov3Ove Tobias Gudmestad4Department of Development and Operation of Oil and Gas Fields, Saint Petersburg Mining University, 199106 Saint Petersburg, RussiaWell Placement Department, SevKomNeftegaz LLC, 629830 Gubkinsky, RussiaThe Automation of Technological Processes and Production Department, Saint Petersburg Mining University, 199106 Saint Petersburg, RussiaPatent and Licensing Department, Saint Petersburg Mining University, 199106 Saint Petersburg, RussiaFaculty of Science and Technology, University of Stavanger, N-4036 Stavanger, NorwayThis article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.https://www.mdpi.com/2073-8994/13/7/1293machine learningdrilling problemsartificial intelligencerisk factor evaluationgradient boosting |
| spellingShingle | Shamil Islamov Alexey Grigoriev Ilia Beloglazov Sergey Savchenkov Ove Tobias Gudmestad Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods Symmetry machine learning drilling problems artificial intelligence risk factor evaluation gradient boosting |
| title | Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods |
| title_full | Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods |
| title_fullStr | Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods |
| title_full_unstemmed | Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods |
| title_short | Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods |
| title_sort | research risk factors in monitoring well drilling a case study using machine learning methods |
| topic | machine learning drilling problems artificial intelligence risk factor evaluation gradient boosting |
| url | https://www.mdpi.com/2073-8994/13/7/1293 |
| work_keys_str_mv | AT shamilislamov researchriskfactorsinmonitoringwelldrillingacasestudyusingmachinelearningmethods AT alexeygrigoriev researchriskfactorsinmonitoringwelldrillingacasestudyusingmachinelearningmethods AT iliabeloglazov researchriskfactorsinmonitoringwelldrillingacasestudyusingmachinelearningmethods AT sergeysavchenkov researchriskfactorsinmonitoringwelldrillingacasestudyusingmachinelearningmethods AT ovetobiasgudmestad researchriskfactorsinmonitoringwelldrillingacasestudyusingmachinelearningmethods |