Correlating geologic and seismic data with unconventional resource production curves using machine learning

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. O39-O47 ◽  
Author(s):  
Ryan Smith ◽  
Tapan Mukerji ◽  
Tony Lupo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Oil And Gas ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Full Time ◽  
Data Types ◽  
Production Time ◽  
Well Production ◽  
Unconventional Resource

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data. We are then able to predict the well-production time series, with 65%–76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.

scholarly journals Machine Learning for Automatic Classification of Tomato Ripening Stages Using Electrophysiological Recordings

2021 ◽  
Vol 5 ◽  
Author(s):  
Gabriela Niemeyer Reissig ◽  
Thiago Francisco de Carvalho Oliveira ◽  
Ádrya Vanessa Lira Costa ◽  
André Geremia Parise ◽  
Danillo Roberto Pereira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wavelet Transform ◽  
Electrical Activity ◽  
Fruit Ripening ◽  
Tomato Fruit ◽  
Descriptive Analysis ◽  
Principal Component ◽  
Ripening Stages

The physiological processes underlying fruit ripening can lead to different electrical signatures at each ripening stage, making it possible to classify tomato fruit through the analysis of electrical signals. Here, the electrical activity of tomato fruit (Solanum lycopersicum var. cerasiforme) during ripening was investigated as tissue voltage variations, and Machine Learning (ML) techniques were used for the classification of different ripening stages. Tomato fruit was harvested at the mature green stage and placed in a Faraday's cage under laboratory-controlled conditions. Two electrodes per fruit were inserted 1 cm apart from each other. The measures were carried out continuously until the entire fruits reached the light red stage. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. Descriptive analysis from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis). Finally, ApEn, PCA1, PCA2, and PCA3 were obtained. These features were used in ML analyses for looking for classifiable patterns of the three different ripening stages: mature green, breaker, and light red. The results showed that it is possible to classify the ripening stages using the fruit's electrical activity. It was also observed, using precision, sensitivity, and F1-score techniques, that the breaker stage was the most classifiable among all stages. It was found a more accurate distinction between mature green × breaker than between breaker × light red. The ML techniques used seem to be a novel tool for classifying ripening stages. The features obtained from electrophysiological time series have the potential to be used for supervised training, being able to help in more accurate classification of fruit ripening stages.

Incorporation of Physics into Machine Learning for Production Prediction from Unconventional Reservoirs: A Brief Review of the Gray-Box Approach

2021 ◽  
pp. 1-12
Author(s):  
Hui-Hai Liu ◽  
Jilin Zhang ◽  
Feng Liang ◽  
Cenk Temizel ◽  
Mustafa A. Basri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Complex Problem ◽  
Unconventional Reservoirs ◽  
Future Research ◽  
Gas Industry ◽  
Well Production ◽  
Determination Of Parameters ◽  
Production Prediction

Summary Prediction of well production from unconventional reservoirs is often a complex problem with an incomplete understanding of physics and a considerable amount of data. The most effective way for dealing with it is to use the gray-box approach that combines the strengths of physics-based models and machine learning (ML) used for dealing with certain components of the prediction where physical understanding is poor or difficult. However, the development of methodologies for the incorporation of physics into ML is still in its infancy, not only in the oil and gas industry, but also in other scientific and engineering communities, including the physics community. To set the stage for further advancing the use of combining physics-based models with ML for predicting well production, in this paper we present a brief review of the current developments in this area in the industry, including ML representation of numerical simulation results, determination of parameters for decline curve analysis (DCA) models with ML, physics-informed ML (PIML) that provides an efficient and gridless method for solving differential equations and for discovering governing equations from observations, and physics-constrained ML (PCML) that directly embeds a physics-based model into a neural network. The advantages and potential limitations of the methods are discussed. The future research directions in this area include, but are not limited to, further developing and refining methodologies, including algorithm development, to directly embed physics-based models into ML; exploring the usefulness of PIML for reservoir simulations; and adapting the new developments of how the physics and ML are incorporated in other communities to the well-production prediction. Finally, the methodologies we discuss in the paper can be generally applied to conventional reservoirs as well, although the focus here is on unconventional reservoirs.

Forecasting Stock Index Futures Intraday Returns: Functional Time Series Model

2020 ◽  
Vol 24 (3) ◽  
pp. 265-271
Author(s):  
Yizheng Fu ◽  
◽  
Zhifang Su ◽  
Boyu Xu ◽  
Yu Zhou
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Futures Contract ◽  
Functional Time Series ◽  
Functional Principal Component ◽  
Dynamic Forecasting

It is of great significance to forecast the intraday returns of stock index futures. As the data sampling frequency increases, the functional characteristics of data become more obvious. Based on the functional principal component analysis, the functional principal component score was predicted by BM, OLS, RR, PLS, and other methods, and the dynamic forecasting curve was reconstructed by the predicted value. The traditional forecasting methods mainly focus on “point” prediction, while the functional time series forecasting method can avoid the point forecasting limitation, and realize “line” prediction and dynamic forecasting, which is superior to the traditional analysis method. In this paper, the empirical analysis uses the 5-minute closing price data of the stock index futures contract (IF1812). The results show that the BM prediction method performed the best. In this paper, data are considered as a functional time series analysis object, and the interference caused by overnight information is removed so that it can better explore the intraday volatility law, which is conducive to further understanding of market microstructure.

The Oxford Handbook of Functional Data Analysis

2018 ◽  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Principal Component ◽  
Regression Modeling ◽  
Functional Principal Component Analysis ◽  
Functional Regression ◽  
Vector Integration ◽  
Wide Range

This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as linear/nonparametric functional regression, varying coefficient models, and linear/nonparametric functional processes (i.e. functional time series). Part II considers related benchmark methods/tools for functional data analysis, including curve registration methods for preprocessing functional data, functional principal component analysis, and resampling/bootstrap methods. Finally, Part III examines some of the fundamental mathematical aspects of the infinite-dimensional setting, with a focus on the stochastic background and operatorial statistics: vector-valued function integration, spectral and random measures linked to stationary processes, operator geometry, vector integration and stochastic integration in Banach spaces, and operatorial statistics linked to quantum statistics.

scholarly journals Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series

2020 ◽  
Vol 12 (7) ◽  
pp. 1132 ◽  
Author(s):  
Simone Pesaresi ◽  
Adriano Mancini ◽  
Giacomo Quattrini ◽  
Simona Casavecchia
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Mediterranean Forest ◽  
Plant Associations ◽  
Functional Principal Component ◽  
Ndvi Time Series

The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.

scholarly journals Functional principal component based time-series genome-wide association in sorghum

2020 ◽  
Author(s):  
Chenyong Miao ◽  
Yuhang Xu ◽  
Sanzhen Liu ◽  
Patrick S. Schnable ◽  
James C. Schnable
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Genome Wide Association ◽  
Phenotypic Change ◽  
Genome Wide Association Studies ◽  
Functional Principal Component ◽  
Genome Wide ◽  
Wide Range ◽  
Over Time

ABSTRACTThe phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track phenotypic change over time. Identifying genetic loci regulating differences in the pattern of phenotypic change remains challenging. In this study we used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum diversity panel using a number of technologies including RGB and hyperspectral imaging. Imaging lasted for thirty-seven days centered on reproductive transition. A new higher density SNP set was generated for the same population. Several genes known to controlling trait variation in sorghum have been cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotype. Partitioning was consistent with the known molecular function of the individual cloned genes. FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Time-series analysis can increase the accuracy and power of quantitative genetic analyses.

Trajectories of machine learning-predicted mortality risk among patients with cancer and associated end-of-life utilization.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1553-1553
Author(s):  
Manqing Liu ◽  
Jinbo Chen ◽  
Eric Li ◽  
Runze Li ◽  
Ravi Bharat Parikh
Keyword(s):  
Machine Learning ◽  
End Of Life ◽  
Mortality Risk ◽  
End Of Life Care ◽  
Performance Status ◽  
Principal Component ◽  
Care Quality ◽  
Functional Principal Component Analysis ◽  
Life Care ◽  
Comorbidity Burden

1553 Background: Machine learning (ML) algorithms outperform traditional tools used for prognostication and may facilitate earlier discussions between oncologists and patients (pts) about hospice enrollment and treatment modification. Identifying longitudinal trajectories of mortality risk may help clinicians and health systems understand which populations such algorithms are likely to benefit. Methods: We identified trajectories of mortality risk and their association with existing metrics of end-of-life care quality, using electronic health and registry data from a prospective cohort of 3,280 pts with cancer who were seen in 18 tertiary or community medical oncology practices within a large academic health system between January 2018 and May 2020 and died prior to November 2020. A validated ML algorithm (c-statistic 0.89; Parikh et al, JAMA Oncol, 2020) prospectively generated mortality risk predictions prior to all encounters. Functional principal component analysis (FPCA) identified modes of variation for all patient-level mortality risk predictions associated with encounters prior to death. Adjusted logistic regression analyses tested associations between mortality risk trajectory and metrics of high-quality end-of-life care. Results: FPCA revealed 2 trajectories that represented 36% and 64% of all pts in the cohort. The first cluster (“unpredictable”) consisted of pts whose ML-predicted mortality risk rose sharply within 30 days of death. The second cluster (“predictable”) consisted of pts whose ML-predicted mortality risk was higher at baseline and rose gradually until death. Individuals with predictable mortality risk trajectories were more likely to have worse performance status, high comorbidity burden, and gastrointestinal (GI) malignancies (Table). Predictable trajectories were associated with higher hospice enrollment (adjusted odds ratio [aOR] 1.87, 95% CI 1.48-2.37), less inpatient death (aOR 0.72, 95% CI 0.56-0.92), less end-of-life intensive care unit admissions in the last 30 days of life (aOR 0.74, 95% CI 0.57-0.95), and less chemotherapy in the last 14 days of life (aOR 0.77, 95% CI 0.55-1.08). Conclusions: Over one-third of deaths among pts with cancer follow an unpredictable trajectory. For pts with unpredictable trajectories, overreliance on ML predictions could perpetuate aggressive end-of-life care.[Table: see text]

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