English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The aim of this study is to develop and apply an autonomous approach forpredicting the probability of hydrocarbon reservoirs spreading in the studiedarea. The methodology uses machine learning algorithms in the problem of binaryclassification, which restore the probability function of the space elementbelonging to the classes identified by the results of interpretation of welllogging. Attributes of seismic wavefield are used as predictors. The studyincludes the following sequence of actions: creation of data sets for training,selection of features, reverse-calibration of data, creation of a population ofclassification models, evaluation of classification quality, evaluation of thecontribution of features in the prediction, ensembling the population of modelsby stacking method. As a result, a prediction was made - a three-dimensionalcube of calibrated probabilities of belonging of the studied space to the classof reservoir and its derivative in the form of the map of reservoir thicknessesof the Achimov complex of deposits was obtained. Assessment of changes in thequality of the forecast depending on the use of different data sets was carriedout. Conclusion. The reverse-calibration method proposed in this work uses theuncertainty of geophysical data as a hyperparameter of the global tuning of thetechnological stack, within the given limits of the a priori error of thesedata. It is shown that the method improves the quality of the forecast. Thetechnological stack of machine learning algorithms used in this work allowsexpert-independent generalization of geological and geophysical data, and usethis generalization to test hypotheses and create geological models based on aprobabilistic view of the reservoir.

Generalization with Reverse-Calibration of Well and Seismic Data Using  Machine Learning Methods for Complex Reservoirs Predicting During Early-Stage  Geological Exploration Oil Field