Results of hydraulic fracturing design improvements and changes in execution strategies for unconventional tight gas targets in the Cooper Basin, Australia

Tight gas stimulations in the Cooper Basin have been challenged by strike–slip to reverse stress regimes, adversely affecting the hydraulic fracturing treatment. These stress conditions increase borehole breakout and affect log and cement quality, create more tortuous pathways and near-wellbore pressure loss, and reduce fracture containment. These factors result in stimulation of lower permeability, low modulus intervals (e.g. carbonaceous shales and interbedded coals) versus targeted tight gas sands. In the Windorah Trough of the Cooper Basin, several steps have been employed in an ongoing experiment to improve hydraulic fracturing results. First, the wellbore was deviated in the maximum horizontal stress direction and perforations shot 0 to 180° phased to better align the resulting hydraulic fractures. Next, existing drilling and logging-while-drilling data were used to train a machine learning model to improve reservoir characterisation in sections with missing or poor log data. Finally, diagnostic fracture injection tests in non-pay and pay sections were targeted to specifically inform the machine learning model and better constrain permeability and stress profiles. It is envisaged that the improved well and perforation alignment and better targeting of intervals for the fracturing treatment will result in lowered tortuosity, better fracture containment, and higher concentrations of localised proppant, thereby improving conductivity and targeting of desired intervals. The authors report the process and results of their experimentation, and the results relative to the offsetting vertical well where a typical five-stage treatment was employed.

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Design of Machine Learning Model for Urban Planning and Management Improvement

International Journal of Performability Engineering ◽

10.23940/ijpe.20.06.p14.958967 ◽

2020 ◽

Vol 16 (6) ◽

pp. 958 ◽

Cited By ~ 1

Author(s):

Zhou Jiafeng ◽

Liu Tian ◽

Zou Lin

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Learning Model ◽

Planning And Management ◽

Machine Learning Model ◽

Urban Planning And Management ◽

Management Improvement

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A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

10.2523/iptc-19230-ms ◽

2019 ◽

Author(s):

Mohammed Al-Ghazal ◽

Viranchi Vedpathak

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Model ◽

Machine Learning Model

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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10.26434/chemrxiv.7411172 ◽

2018 ◽

Author(s):

Steen Lysgaard ◽

Paul C. Jennings ◽

Jens Strabo Hummelshøj ◽

Thomas Bligaard ◽

Tejs Vegge

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Au Nanoparticles ◽

Learning Model ◽

Energy Calculations ◽

Atomic Distribution ◽

Machine Learning Model ◽

Fold Reduction ◽

Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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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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