Selecting and Modifying Multiphase Correlations for Gas-Lift Wells Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Roman Gorbachev ◽  
Andrey Gubaev ◽  
Alexander Lubnin ◽  
Alexey Chorny ◽  
Vasif Kurbanov
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Injection Rate ◽  
Machine Learning Algorithms ◽  
Operating Modes ◽  
Bottom Hole ◽  
Urgent Task ◽  
The Republic ◽  
Gas Lift ◽  
Bottomhole Pressure

Abstract In the conditions of the development of the oil fields of the Cuu Long Basin in the continental shelf of the Republic of Vietnam, in the absence of downhole gauge systems, the urgent task is improving the accuracy of the calculation of bottomhole pressure in the producing wells based on the operation modes and construction. The aim of the paper is to create tools for selecting and modifying the correlation of multiphase flow most suitable for the development of a particular group of fields, as well as to develop a tool to implement effective management of the modes of operation of gas-lift wells by choosing the optimal gaslift injection rate. Based on data from 814 instrumental measurements in wells with different construction, liquid flow rate, watercut, GOR and gaslift injection, the calculation of bottom hole pressures was made. The calculated and actual bottomhole pressures were compared with five correlations of multiphase flow, the most suitable correlations were determined and modified, including using machine learning methods, which helped to significantly improve the convergence of calculated and actual bottomhole pressures. On the basis of the newly modified correlation, a calculation of bottom hole pressure (BHP) in each production well was made, the calculation of the change in bottomhole pressure when changing the operating modes of wells has been implemented. For the field group of the Cuu Long Basin, it was revealed that with the increase in watercut in the producing wells significantly reduces the efficiency of the gas-lift method of operation. This effect is not reflected in the widespread correlations of multiphase flow, which does not allow to use the results of calculations without making additional edits. A way to adapt the calculation values to instrumental measurements has been implemented, one of the known correlations has been modified and used in the forecast of changes in bottomhole pressure after changes in operating modes of wells throughout the well stock.

scholarly journals Machine Learning for Landslides Prevention: A Survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

scholarly journals Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3136
Author(s):  
Tommaso Barbariol ◽  
Enrico Feltresi ◽  
Gian Antonio Susto
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Multiphase Flow ◽  
Time Series Data ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Gas Content ◽  
Series Data ◽  
Oil Well ◽  
Flow Meters

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.

scholarly journals Machine Learning for Landslides Prevention: A Survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

scholarly journals Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

2021 ◽  
Vol 11 (21) ◽  
pp. 10442
Author(s):  
Karlo Babić ◽  
Milan Petrović ◽  
Slobodan Beliga ◽  
Sanda Martinčić-Ipšić ◽  
Mihaela Matešić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Crisis Communication ◽  
Learning Algorithms ◽  
Language Model ◽  
Machine Learning Algorithms ◽  
Large Dataset ◽  
Communication Problems ◽  
The Republic ◽  
Over Time

This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206,196 tweets related to COVID-19 and constructed a dataset of 10,000 tweets which we manually annotated with a sentiment label. We trained the Cro-CoV-cseBERT language model for the representation and clustering of tweets. Additionally, we compared the performance of four machine learning algorithms on the task of sentiment classification. After identifying the best performing setup of NLP methods, we applied the proposed framework in the task of characterisation of COVID-19 tweets in Croatia. More precisely, we performed sentiment analysis and tracked the sentiment over time. Furthermore, we detected how tweets are grouped into clusters with similar themes across three pandemic waves. Additionally, we characterised the tweets by analysing the distribution of sentiment polarity (in each thematic cluster and over time) and the number of retweets (in each thematic cluster and sentiment class). These results could be useful for additional research and interpretation in the domains of sociology, psychology or other sciences, as well as for the authorities, who could use them to address crisis communication problems.

scholarly journals Machine learning for landslides prevention: a survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei ◽  
Francesco Piccialli
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

AbstractLandslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

Application of Machine Learning Algorithms and Integrated Production Modelling to Improve Accuracy of Liquid Production Rate Measurements Using Multiphase Flow Meters

2021 ◽  
Author(s):  
Maksim Yuryevich Nazarenko ◽  
Anatoly Borisovich Zolotukhin
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Production Rate ◽  
Flow Rate ◽  
Learning Algorithms ◽  
Oil Production ◽  
Machine Learning Algorithms ◽  
Flow Meter ◽  
Integrated Production ◽  
Production Modelling

Abstract Objectives/Scope: During the period of two years the difference between sum of daily oil flow rate measurements of each oil production well using multiphase flow meter (MPFM) and cumulative daily oil production rate measured by custody transfer meter increased overall by 5%. For some wells inaccuracy of MPFM liquid rate measurement could reach 30-50%. The main goal of this research was to improve the accuracy of multiphase flow meter production rate measurements. Methods, Procedures, Process: More than 80 oil production wells were involved in the research, more than 100 flow rate tests were carried out. Machine learning methods such as supervised learning algorithms (linear and nonlinear regressions, method of gradient descent, finite differences algorithm, etc.) have been applied coupled with Integrated production modelling tools such as PROSPER and OpenServer in order to develop a function representing correlation between MPFM parameters and flow rate error. Results, Observations, Conclusions: The difference between cumulative daily oil production rate measured by custody transfer meter and multiphase flow meters decreased to 0.5%. The solution has been officially applied at the oil field and saved USD 500K to the Company. The reliability of the function was then proved by the vendor of MPFMs. Novel/Additive Information: For the first time machine learning algorithms coupled with Integrated Production modelling tools have been used to improve the accuracy of multiphase flow meter production rate measurements.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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