Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.

A comprehensive study of a minimized component pencil shaped (PS) 9-level inverter constructed with just two DC supplies is presented in this research. Many of the suggested low component multilevel inverters (MLIs) use DC supplies that are not being used appropriately along with additional conducting switches. Since this proposed MLI has a reduced quantity of power electronic switches, it is more efficient. The architecture may be expanded to a modular higher voltage level inverter, which uses less DC supplies and uses them correctly without the need of an extra H-bridge circuit. To determine their optimum capabilities, the proposed inverter parameters' simplified formulas are constructed. Furthermore, the extended model of the proposed architecture is used to generate an optimum PSMLI design for lowering the total standing voltage (TSV) of the inverter. To demonstrate its advantages over recent MLIs of similar types, comparison studies are given to justify the proposed inverter. Through proper simulation and laboratory experiment, the MLI obtained a higher efficiency of 95.54%. On the other hand, the optimized 17level version of PSMLI obtained total harmonic distortions (THD) of only 5.15% which successfully attained IEEE 519 standard performance.INDEX TERMS multilevel inverter, power electronics, switched capacitor, voltage boost, nearest level control.

No abstract

Understanding geological differences in a proved reservoir requires precise facies classification. Predicting facies from seismic data is frequently seen as an inverse uncertainty quantification problem in seismic reservoir characterization. Typically, the uncertainty in the model parameters that regulate the geographic distributions is being ignored. The target facies and its uncertainty can be determined by calculating the posterior distribution of a Bayesian inverse problem conditioned by the seismic data, in which the model parameters are inferred from the observed seismic data using a Bayesian inference framework. It is believed that such facies classification model has a unique set of model parameters that best fits it. The proposed work is unique in that it quantifies the epistemic uncertainty of the predicted facies in blind well conditioned by Seismic Amplitude Versus Offset (AVO-Seismic) attributes in the Bayesian inference framework. Under this framework, parameter uncertainties of the neural net. weights and biases are calculated using their posterior distributions from the ensamble models generated by Marcov-Chains Monte-Carlo (MCMC) by assuming that the prior values of the weights and biases are uninformative. The proposed approach is also demonstrated on Synthetic Amplitude Versus Offset (AVO-Synthetic) dataset (derived from the well log information) and we have found high relevance in the predicted results. For comparision, a plain Deep Learning and Deep learning with Monte Carlo Dropout are employed and the results indicate that our model performs more efficiently comparing to the others indicating the possibility of the model to be used in real world solution to adequate facies classication.

Electrofacies were initially introduced for defining a set of recorded log responses in order to characterize a bed and permitted it to be distinguished from the other rock units as an improvement to the traditional use of well logs. Grouping a formation into electrofacies can be used in lithology prediction, reservoir characterization and discrimination. Usually Multivariate statistical analyses, such as principal component analysis ‘PCA’ and cluster analysis are used for this purpose. In this study Extra Tree Classifier (ETC) based feature selection method is used to select the important attributes and three distinctive electrofacies were extracted from the dendrogram plot using the selected attributes. Finally, we proposed a rough set theory (RST) based white box classification approach to extract the pattern of the electrofacies in the form of decision rules which will allow the geosciences researchers to correlate the electrofacieses with the lithofacies from the extracted rough set (RS) rules.