scholarly journals Prediction of Oil and Gas Occurrence in the Southern Part of Perm Krai Based on Regional 3D Modeling

2020 ◽  
Vol 20 (4) ◽  
pp. 317-330
Author(s):  
Aleksey L. Yuzhakov ◽  
◽  
Ivan S. Putilov ◽  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Oil And Gas ◽  
Structural Parameters ◽  
Linear Discriminant ◽  
Seismic Surveys ◽  
Geochemical Parameters ◽  
Individual Probability ◽  
Gas Occurrence ◽  
Perm Krai

The territory of the southern part of Perm Krai is well studied in terms of oil and gas prospecting. About 150 oil and gas fields have been discovered there, over 7000 deep wells have been drilled, and 3D seismic surveys have been completed on the area exceeding 5000 km2. The state of exploration of the territory allows us to have an immense array of geologic information, which can be used to search and predict oil and gas occurrence in structures that remain left out or that have not been studied yet. The research area was limited by the confines of Perm Krai in the south, west, and east and by a conventional line in the north along the boundary of the completed seismic surveys. To study the territory based on the reflecting horizon surface of Perm Krai, a 3D geological model has been built within IRAPRMS software system. The model calculates a regional, a zonal and local constituents of the reflecting horizon of Perm Krai. The local constituent allowed us to single out structures divided into three categories: structures of ascertained oil and gas occurrence, structures that do not contain oil and gas (empty), and structures for which a prediction is needed. In the model, structural parameters representing a trap potential for the accumulation and retention of hydrocarbons were calculated. Moreover, geochemical parameters showing a generation potential and a migration constituent, as well as hydrogeological parameters as indirect data to determine the saturation of structures with hydrocarbons, were downloaded into the model. The obtained data about the importance of each parameter for all structures allowed us to generate a single database and predict oil and gas occurrence by the machine learning method, i.e. through the step-bystep linear discriminant analysis. Based on the results of the linear discriminant analysis, 138 predicted structures were arranged in groups in accordance with degrees of their potential. By applying the built individual probability models, a map of the regional probability of structures’ saturation with hydrocarbons was obtained; this map served as a basis and amendment of oil and gas geological zoning boundaries of the southern part of Perm Krai.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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