Geomechanical Measurements and Machine Learning Help Predict Trouble Stages

2021 ◽  
Vol 73 (04) ◽  
pp. 42-43
Author(s):  
Chris Carpenter
Keyword(s):  
Machine Learning ◽  
Debris Flows ◽  
High Pressures ◽  
Phase 1 ◽  
Horizontal Wells ◽  
Internal Diameter ◽  
Data Set ◽  
Geomechanical Properties ◽  
Midland Basin ◽  
Well Completion

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201699, “Predicting Trouble Stages With Geomechanical Measurements and Machine Learning: A Case Study of Southern Midland Basin Horizontal Completions,” by Eric Romberg, SPE, Keban Engineering; Aaron Fisher, Tracker Resources; and Joel Mazza, SPE, Fracture ID, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Unexpected problems during completion create costs that can cause a well to be outside its planned authorization for expenditure, even uneconomic. These problems range from experiencing abnormally high pressures during treatment to casing failures. The authors of the complete paper use machine-learning methods combined with geomechanical, wellbore-trajectory, and completion data sets to develop models that predict which stages will experience difficulties during completion. Field Modeling and Well Planning The operator’s acreage is in the southeastern portion of the Midland Basin. In this area of the basin, the Wolfcamp B and C intervals often contain a significant amount of slope sediments and carbonate debris flows because of the proximity of the eastern shelf. These intervals cause significant drilling and completion issues. During the past 5 years, the operator acquired and licensed approximately 130 sq mile of 3D seismic data. In addition, the operator cored three wells, drilled six pilot wells with complete log suites, licensed 40 wells with a triple/quad combination, acquired data and surveys on 112 existing horizontal wells, and has 347 vertical wells with formation tops for depth control. This rich data set yielded a robust 3D reservoir model that was used to map a sequence of stacked, high-quality landing targets. Model-Aided Well-Completion Strategy. The operator often encountered difficult stages in the form of high breakdown pressures, high pumping pressures, and the inability to place proppant. On a few occasions, drilling out all plugs was not possible because of casing obstructions possibly related to fault activation during the stimulation. The operator began analyzing curvature and similarity volumes for potential fault/fracture identification near the difficult completion stages and compromised casing intervals. Drillbit geomechanics data collection was planned for all lateral wells. The geomechanical properties recorded were used to reduce risks during completions further by informing the plug and perforation stage design. Stages were planned to reduce variation in minimum horizontal stress (Shmin) within each stage. The geomechanical data also identified carbonate debris flows within the well path, allowing completion engineers to bypass rock considered unproductive. Completion Issues and Other Factors Contributing to Casing Deformation. From February 2017 through November 2019, the operator drilled and completed 28 Wolfcamp horizontal wells. The plug-and-perforation completion technique was used on all 28 wells. While drilling out composite fracturing plugs, casing obstruction was encountered in six of 28 wells. These obstructions limited the working internal diameter of the production casing and either prevented or inhibited access beyond the obstruction. In two of the Phase 1 wells, conventional drillout assemblies were not able to pass the obstructions.

A NEW METHOD OF DECLINE CURVE FORECASTING FOR PROJECT WELLS ON THE BASE OF MACHINE LEARNING ALGORITHMS

2020 ◽  
pp. 69-74
Author(s):  
S.I. Gabitova ◽  
L.A. Davletbakova ◽  
V.Yu. Klimov ◽  
D.V. Shuvaev ◽  
I.Ya. Edelman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Horizontal Wells ◽  
Machine Learning Algorithms ◽  
New Method ◽  
Well Completion ◽  
Decline Curve ◽  
Forecasting Method ◽  
Decline Curves

The article describes new decline curves (DC) forecasting method for project wells. The method is based on the integration of manual grouping of DC and machine learning (ML) algorithms appliance. ML allows finding hidden connections between features and the output. Article includes the decline curves analysis of two well completion types: horizontal and slanted wells, which illustrates that horizontal wells are more effective than slanted.

Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study

2020 ◽  
Vol 8 (3) ◽  
pp. T589-T597 ◽  
Author(s):  
Mark Mlella ◽  
Ming Ma ◽  
Rui Zhang ◽  
Mehdi Mokhtari
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Rock Physics ◽  
Physical Models ◽  
Data Sets ◽  
Reservoir Properties ◽  
Data Set ◽  
Geomechanical Properties ◽  
Marine Shale ◽  
Exploration And Production

Brittleness is one of the most important reservoir properties for unconventional reservoir exploration and production. Better knowledge about the brittleness distribution can help to optimize the hydraulic fracturing operation and lower costs. However, there are very few reliable and effective physical models to predict the spatial distribution of brittleness. We have developed a machine learning-based method to predict subsurface brittleness by using multidiscipline data sets, such as seismic attributes, rock physics, and petrophysics information, which allows us to implement the prediction without using a physical model. The method is applied on a data set from Tuscaloosa Marine Shale, and the predicted rock physics template is close to the calculated value from conventional inverted elastic parameters. Therefore, the proposed method helps determine areas of the reservoir that have optimal geomechanical properties for successful hydraulic fracturing.

On the identification of Electron Diffusion Regions at the magnetopause with an AI approach

2020 ◽  
Author(s):  
Quentin Lenouvel ◽  
Vincent Génot ◽  
Philippe Garnier ◽  
Sergio Toledo-Redondo ◽  
Benoît Lavraud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Phase 1 ◽  
Diffusion Region ◽  
Data Set ◽  
Electron Diffusion ◽  
Training Techniques ◽  
Research Fields ◽  
Dayside Magnetopause

<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p><strong></strong></p> <p>MMS has already been producing a very large dataset with invaluable information about how the solar wind and the Earth's magnetosphere interact. However, it remains challenging to process all these new data and convert it into scientific knowledge, the ultimate goal of the mission. Data science and machine learning are nowadays a very powerful and successful technology that is employed to many applied and research fields. During this presentation, I shall discuss the tentative use of machine learning for the automatic detection and classification of plasma regions, relevant to the study of magnetic reconnection in the MMS data set, with a focus on the critical but poorly understood electron diffusion region (EDR) at the Earth's dayside magnetopause. We make use of the EDR database and the plasma regions nearby that has been identified by the MMS community and compiled by Webster et al. (2018) as well as the Magnetopause crossings database compiled by the ISSI team, to train a neural network using supervised training techniques. I shall present a list of new EDR candidates found during the phase 1 of MMS and do a case study of some of the strong candidates.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

Exchange Spin Coupling from Gaussian Process Regression

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.

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

2019 ◽  
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. <br>

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

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
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.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

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.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

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.

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

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
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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