A Machine-Learning Based Workflow for Predicting Overpressure in a Stiff Dolomitic Formation

Abstract Overpressure refers to the abnormally high subsurface pressure that may exceed hydrostatic pressure at a given depth. Its characterization is an important part of subsurface characterization as it allows to complete drilling operations in a safe and optimal way. In dolomitic formations, however, the prediction of such overpressure is especially challenging because of (1) the high degree of lateral variability of the formations, (2) the limited effect of overpressure on tight rocks elastic parameters, and (3) the complexity of physical processes involved to form overpressure. In addition to these factors, existing experimental models generally used to relate elastic parameters to pressure are often not well calibrated to carbonate rocks. The alternative to existing purely physical approaches is a data-driven model that leverages data from offset wells. We show that due to the complexity of the characterization question to be solved, an end-to-end machine learning based approach is deemed to fail. Instead of a fully automated approach, we show a semi-supervised workflow that integrates seismic, geological data, and overpressure observations from previously drilled wells to map overpressure regions. Attribute maps are first extracted from a 3D seismic data set in an overpressured geological formation of interest. An auto-encoder is then used to learn a more compact representation of data, resulting in a reduced number of latent attributes. Then, a hand-tailored semi-supervised approach is applied, which is a combination of clustering method (here based on DBSCAN algorithm) and Bayesian classification to determine overpressure risk degree (no risk, mild, or high risk). The approach described in this study is compared to direct end-to-end models and significantly outperforms them with an error on a blind well prediction of around 25%. The overpressure probability maps resulting from the models can be used later for the optimization of drilling processes and to reduce drilling hazards.

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Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession

Data Science in Finance and Economics ◽

10.3934/dsfe.2021010 ◽

2021 ◽

Vol 1 (2) ◽

pp. 180-195

Author(s):

Aditya Narvekar ◽

◽

Debashis Guha ◽

Keyword(s):

Machine Learning ◽

Ensemble Methods ◽

Bankruptcy Prediction ◽

Financial Ratios ◽

Machine Learning Techniques ◽

Set Covering ◽

Economic Losses ◽

Data Set ◽

Out Of Sample ◽

High Degree

<abstract> Bankruptcy prediction is an important problem in finance, since successful predictions would allow stakeholders to take early actions to limit their economic losses. In recent years many studies have explored the application of machine learning models to bankruptcy prediction with financial ratios as predictors. This study extends this research by applying machine learning techniques to a quarterly data set covering financial ratios for a large sample of public U.S. firms from 1970–2019. We find that tree-based ensemble methods, especially XGBoost, can achieve a high degree of accuracy in out-of-sample bankruptcy prediction. We next apply our best model, using XGBoost, to the problem of predicting the overall bankruptcy rate in USA in the second half of 2020, after the COVID-19 pandemic had necessitated a lockdown, leading to a deep recession. Our model supports the prediction, made by leading economists, that the rate of bankruptcies will rise substantially in 2020, but it also suggests that this elevated level will not be much higher than 2010. </abstract>

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Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

Wellcome Open Research ◽

10.12688/wellcomeopenres.17148.1 ◽

2021 ◽

Vol 6 ◽

pp. 248

Author(s):

Paul Mwaniki ◽

Timothy Kamanu ◽

Samuel Akech ◽

Dustin Dunsmuir ◽

J. Mark Ansermino ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Pulse Oximeter ◽

Regression Models ◽

Data Sets ◽

Learning Models ◽

Data Set ◽

Logistic Regression Models ◽

End To End

Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained machine learning models to predict paediatric hospitalization given raw photoplethysmography (PPG) signals obtained from a pulse oximeter. We trained self-supervised learning (SSL) for automatic feature extraction from PPG signals and assessed the utility of SSL in initializing end-to-end deep learning models trained on a small labelled data set with the aim of predicting paediatric hospitalization.Methods: We compared logistic regression models fitted using features extracted using SSL with end-to-end deep learning models initialized either randomly or using weights from the SSL model. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: The SSL model trained on both labelled and unlabelled PPG signals produced features that were more predictive of hospitalization compared to the SSL model trained on labelled PPG only (AUC of logistic regression model: 0.78 vs 0.74). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can improve the classification of PPG signals by either extracting features required by logistic regression models or initializing end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.

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Practical Machine-Learning Applications in Well-Drilling Operations

SPE Drilling & Completion ◽

10.2118/205480-pa ◽

2021 ◽

pp. 1-19

Author(s):

T. A. Olukoga ◽

Y. Feng

Keyword(s):

Machine Learning ◽

Case Studies ◽

Oil And Gas ◽

Oil And Gas Industry ◽

Support Vector ◽

Drilling Fluids ◽

Optimal Size ◽

Data Set ◽

Representative Case ◽

Drilling Operations

Summary There is a great deal of interest in the oil and gas industry (OGI) in seeking ways to implement machine learning (ML) to provide valuable insights for increased profitability. With buzzwords such as data analytics, ML, artificial intelligence (AI), and so forth, the curiosity of typical drilling practitioners and researchers is piqued. While a few review papers summarize the application of ML in the OGI, such as Noshi and Schubert (2018), they only provide simple summaries of ML applications without detailed and practical steps that benefit OGI practitioners interested in incorporating ML into their workflow. This paper addresses this gap by systematically reviewing a variety of recent publications to identify the problems posed by oil and gas practitioners and researchers in drilling operations. Analyses are also performed to determine which algorithms are most widely used and in which area of oilwell-drilling operations these algorithms are being used. Deep dives are performed into representative case studies that use ML techniques to address the challenges of oilwell drilling. This study summarizes what ML techniques are used to resolve the challenges faced, and what input parameters are needed for these ML algorithms. The optimal size of the data set necessary is included, and in some cases where to obtain the data set for efficient implementation is also included. Thus, we break down the ML workflow into the three phases commonly used in the input/process/output model. Simplifying the ML applications into this model is expected to help define the appropriate tools to be used for different problems. In this work, data on the required input, appropriate ML method, and the desired output are extracted from representative case studies in the literature of the last decade. The results show that artificial neural networks (ANNs), support vector machines (SVMs), and regression are the most used ML algorithms in drilling, accounting for 18, 17, and 13%, respectively, of all the cases analyzed in this paper. Of the representative case studies, 60% implemented these and other ML techniques to predict the rate of penetration (ROP), differential pipe sticking (DPS), drillstring vibration, or other drilling events. Prediction of rheological properties of drilling fluids and estimation of the formation properties was performed in 22% of the publications reviewed. Some other aspects of drilling in which ML was applied were well planning (5%), pressure management (3%), and well placement (3%). From the results, the top ML algorithms used in the drilling industry are versatile algorithms that are easily applicable in almost any situation. The presentation of the ML workflow in different aspects of drilling is expected to help both drilling practitioners and researchers. Several step-by-step guidelines available in the publications reviewed here will guide the implementation of these algorithms in the resolution of drilling challenges.

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Exchange Spin Coupling from Gaussian Process Regression

10.26434/chemrxiv.12589541.v3 ◽

2020 ◽

Author(s):

Marc Philipp Bahlke ◽

Natnael Mogos ◽

Jonny Proppe ◽

Carmen Herrmann

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Molecular Magnets ◽

Molecular Structures ◽

Spin Coupling ◽

Structure Property ◽

Data Set ◽

Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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Random Forest Refinement of Pairwise Potentials for Protein-ligand Decoy Detection

10.26434/chemrxiv.8047820.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jun Pei ◽

Zheng Zheng ◽

Hyunji Kim ◽

Lin Song ◽

Sarah Walworth ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Probability Function ◽

Pair Potential ◽

Scoring Function ◽

Stable Structure ◽

Scoring Functions ◽

Atom Pair ◽

Data Set ◽

Atom Pairs

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function’s ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF6) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a machine learning (ML) model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the ‘comparison’ concept and, the resultant RF models were tested on CASF-2013.5 In a comparison of the performance of our RF models against 29 scoring functions, we found our models outperformed the other scoring functions in predicting the native pose. In addition, we used two artificial designed potential models to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which share the same peak positions with GARF but have fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not.

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220130232 ◽

2019 ◽

Vol 21 (9) ◽

pp. 662-669 ◽

Cited By ~ 1

Author(s):

Junnan Zhao ◽

Lu Zhu ◽

Weineng Zhou ◽

Lingfeng Yin ◽

Yuchen Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Regression Tree ◽

Large Data ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Applied Sciences ◽

10.3390/app9061128 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1128 ◽

Cited By ~ 12

Author(s):

Yundong Li ◽

Wei Hu ◽

Han Dong ◽

Xueyan Zhang

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Hurricane Sandy ◽

Training Data ◽

Aerial Images ◽

Detection Methods ◽

Single Shot ◽

Data Set ◽

Augmentation Strategies ◽

Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Digital Biomarkers ◽

10.1159/000516619 ◽

2021 ◽

pp. 158-166

Author(s):

Noah Balestra ◽

Gaurav Sharma ◽

Linda M. Riek ◽

Ania Busza

Keyword(s):

Machine Learning ◽

Upper Extremity ◽

Sensor Data ◽

Inpatient Setting ◽

Accelerometer Data ◽

Data Set ◽

Machine Learning Classification ◽

Exercise Type ◽

Exercise Dose ◽

Rehabilitation Exercises

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

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