USING ARTIFICIAL INTELLIGENCE METHODS TO PREVENT COMPLICATIONS IN WELL CONSTRUCTION

2021 ◽  
Vol 4 (1) ◽  
pp. 132-144
Author(s):  
A.N. Dmitrievsky ◽  
◽  
N.A. Eremin ◽  
A.D. Chernikov ◽  
L.I. Zinatullina ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
Automated Systems ◽  
Emergency Situations ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods ◽  
Well Field ◽  
Oil Water ◽  
Model Oil ◽  
Productive Time

The article discusses the use of automated systems for preventing emergency situa-tions in the process of well construction using artificial intelligence methods to increase the productive time of well construction by reducing the loss of working time to eliminate compli-cations. Key words: problems and complications during drilling, emissions, gas and oil water showings, stuck, artificial neural networks, digitalization, drilling, well, field, oil and gas blockchain, artificial intelligence, machine learning methods, geological and technological research, neural network model, oil and gas construction wells, identification and forecasting of complications, prevention of emergency situations.

scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

ARTIFICIAL INTELLIGENCE METHODS IN GAS TRANSMISSION SYSTEM MANAGEMENT

2020 ◽  
pp. 18-22
Author(s):  
Leonid Berner ◽  
Vladislav Nikanorov ◽  
Sergey Marchenko ◽  
Yury Zeldin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Expert Systems ◽  
Transportation Systems ◽  
Transmission System ◽  
System Management ◽  
Emergency Situations ◽  
Artificial Intelligence Methods ◽  
Gas Supply

This article discusses methods of artificial intelligence used in the operational dispatch management of gas transportation systems (GTS) in regular and emergency situations: expert systems and neural networks, as well as tools that make it possible to effectively implement these methods: GTS modeling, gas supply forecasting.

scholarly journals Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions

Georesursy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 87-96
Author(s):  
Alexander D. Chernikov ◽  
Nikolay A. Eremin ◽  
Vladimir E. Stolyarov ◽  
Alexander G. Sboev ◽  
Olga K. Semenova-Chashchina ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
Multidimensional Data ◽  
Technological Parameters ◽  
Gas Wells ◽  
Well Drilling ◽  
Artificial Intelligence Methods ◽  
Oil And Gas Wells ◽  
Problems And Solutions ◽  
And Training

This paper poses and solves the problem of using artificial intelligence methods for processing large volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Digital modernization of the life cycle of wells using artificial intelligence methods, in particular, helps to improve the efficiency of drilling oil and gas wells. In the course of creating and training artificial neural networks, regularities were modeled with a given accuracy, hidden relationships between geological and geophysical, technical and technological parameters were revealed. The clustering of multidimensional data volumes from various types of sensors used to measure parameters during well drilling has been carried out. Artificial intelligence classification models have been developed to predict the operational results of the well construction. The analysis of these issues is carried out, and the main directions for their solution are determined.

Drilling Problems Forecast Based on Neural Network

2021 ◽  
Author(s):  
Sergey Olegovich Borozdin ◽  
Anatoly Nikolaevich Dmitrievsky ◽  
Nikolai Alexandrovich Eremin ◽  
Alexey Igorevich Arkhipov ◽  
Alexander Georgievich Sboev ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
Oil And Gas ◽  
Forecast Accuracy ◽  
Distributed Data ◽  
Technological Parameters ◽  
Well Drilling ◽  
Artificial Intelligence Methods ◽  
Data Objects ◽  
Mud Loss

Abstract This paper poses and solves the problem of using artificial intelligence methods for processing big volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Big volumes of geodata from the stations of geological and technological measurements during drilling varied from units to tens of terabytes. Digital modernization of the life cycle of well construction using machine learning methods contributes to improving the efficiency of drilling oil and gas wells. The clustering of big volumes of geodata from various sources and types of sensors used to measure parameters during drilling has been carried out. In the process of creating, training and applying software components with artificial neural networks, the specified accuracy of calculations was achieved, hidden and non-obvious patterns were revealed in big volumes of geological, geophysical, technical and technological parameters. To predict the operational results of drilling wells, classification models were developed using artificial intelligence methods. The use of a high-performance computing cluster significantly reduced the time spent on assessing the probability of complications and predicting these probabilities for 7-10 minutes ahead. A hierarchical distributed data warehouse has been formed, containing real-time drilling data in WITSML format using the SQL server (Microsoft). The module for preprocessing and uploading geodata to the WITSML repository uses the Energistics Standards DevKit API and Energistic data objects to work with geodata in the WITSML format. Drilling problems forecast accuracy which has been reached with developed system may significantly reduce non-productive time spent on eliminating of stuck pipe, mud loss and oil and gas influx events.

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

2021 ◽  
Vol 193 (7) ◽  
Author(s):  
Yong Jie Wong ◽  
Yoshihisa Shimizu ◽  
Akinori Kamiya ◽  
Luksanaree Maneechot ◽  
Khagendra Pralhad Bharambe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
River Basin ◽  
River Classification ◽  
Artificial Intelligence Methods
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