scholarly journals Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6307
Author(s):  
Cong Wang ◽  
Lisha Zhao ◽  
Shuhong Wu ◽  
Xinmin Song
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Extraction ◽  
Multivariate Time Series ◽  
Oil Production ◽  
Carbonate Reservoir ◽  
Surveillance Data ◽  
Production Data ◽  
Low Permeability ◽  
Boosting Method

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

scholarly journals Machine learning based decline curve analysis for short-term oil production forecast

2021 ◽  
pp. 014459872110117
Author(s):  
Amine Tadjer ◽  
Aojie Hong ◽  
Reidar B Bratvold
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Science ◽  
Oil Production ◽  
Production Data ◽  
Short Term ◽  
Production Forecast ◽  
Physical Relationship ◽  
Production Forecasting ◽  
Decline Curve

Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Empirical analysis of time series using feature selection algorithms

2021 ◽  
Author(s):  
Mikhail Kanevski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Selection ◽  
Dimensional Space ◽  
Multivariate Time Series ◽  
Feature Space ◽  
Real Data ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Linear And Nonlinear

<p>Nowadays a wide range of methods and tools to study and forecast time series is available. An important problem in forecasting concerns embedding of time series, i.e. construction of a high dimensional space where forecasting problem is considered as a regression task. There are several basic linear and nonlinear approaches of constructing such space by defining an optimal delay vector using different theoretical concepts. Another way is to consider this space as an input feature space – IFS, and to apply machine learning feature selection (FS) algorithms to optimize IFS according to the problem under study (analysis, modelling or forecasting). Such approach is an empirical one: it is based on data and depends on the FS algorithms applied. In machine learning features are generally classified as relevant, redundant and irrelevant. It gives a reach possibility to perform advanced multivariate time series exploration and development of interpretable predictive models.</p><p>Therefore, in the present research different FS algorithms are used to analyze fundamental properties of time series from empirical point of view. Linear and nonlinear simulated time series are studied in detail to understand the advantages and drawbacks of the proposed approach. Real data case studies deal with air pollution and wind speed times series. Preliminary results are quite promising and more research is in progress.</p>

scholarly journals Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

2021 ◽  
Author(s):  
Zekai Lu ◽  
Nian Liu ◽  
Ying Xie ◽  
Junhui Xu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Models ◽  
Energy Production ◽  
Production Data ◽  
Learning Models ◽  
Energy Research ◽  
Public Health Emergencies ◽  
Production Plans ◽  
Machine Learning Models

Abstract COVID-19 is a huge catastrophe of global proportions, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and COVID-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the LightGBM, NNAT and LSTM machine learning models worked better in unstable energy series, while the ARIMA statistical model still had an advantage in stable energy time series. Overall, the machine learning models outperformed the traditional models during COVID-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during COVID-19.

scholarly journals Heterogeneity in vaccination coverage explains the size and occurrence of measles epidemics in German surveillance data

2010 ◽  
Vol 139 (4) ◽  
pp. 505-515 ◽  
Author(s):  
S. A. HERZOG ◽  
M. PAUL ◽  
L. HELD
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Vaccination Coverage ◽  
School System ◽  
Multivariate Time Series ◽  
Surveillance Data ◽  
Federal State ◽  
Robert Koch Institute ◽  
Incidence Pattern ◽  
Age Of Entry

SUMMARYThe objective of this study was to characterize empirically the association between vaccination coverage and the size and occurrence of measles epidemics in Germany. In order to achieve this we analysed data routinely collected by the Robert Koch Institute, which comprise the weekly number of reported measles cases at all ages as well as estimates of vaccination coverage at the average age of entry into the school system. Coverage levels within each federal state of Germany are incorporated into a multivariate time-series model for infectious disease counts, which captures occasional outbreaks by means of an autoregressive component. The observed incidence pattern of measles for all ages is best described by using the log proportion of unvaccinated school starters in the autoregressive component of the model.

Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers

2021 ◽  
Author(s):  
Bruno Guilherme Carvalho ◽  
Ricardo Emanuel Vaz Vargas ◽  
Ricardo Menezes Salgado ◽  
Celso Jose Munaro ◽  
Flavio Miguel Varejao
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Flow Instability ◽  
Multivariate Time Series ◽  
Oil Wells ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Offshore Oil
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