Generalization with Reverse-Calibration of Well and Seismic Data Using
Machine Learning Methods for Complex Reservoirs Predicting During Early-Stage
Geological Exploration Oil Field
- URL: http://arxiv.org/abs/2304.03048v2
- Date: Fri, 2 Jun 2023 09:56:03 GMT
- Title: Generalization with Reverse-Calibration of Well and Seismic Data Using
Machine Learning Methods for Complex Reservoirs Predicting During Early-Stage
Geological Exploration Oil Field
- Authors: Dmitry Ivlev
- Abstract summary: The aim of this study is to develop and apply an autonomous approach for predicting the probability of hydrocarbon reservoirs spreading in the studied area.
The methodology uses machine learning algorithms in the problem of binary classification.
Attributes of seismic wavefield are used as predictors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The aim of this study is to develop and apply an autonomous approach for
predicting the probability of hydrocarbon reservoirs spreading in the studied
area. The methodology uses machine learning algorithms in the problem of binary
classification, which restore the probability function of the space element
belonging to the classes identified by the results of interpretation of well
logging. Attributes of seismic wavefield are used as predictors. The study
includes the following sequence of actions: creation of data sets for training,
selection of features, reverse-calibration of data, creation of a population of
classification models, evaluation of classification quality, evaluation of the
contribution of features in the prediction, ensembling the population of models
by stacking method. As a result, a prediction was made - a three-dimensional
cube of calibrated probabilities of belonging of the studied space to the class
of reservoir and its derivative in the form of the map of reservoir thicknesses
of the Achimov complex of deposits was obtained. Assessment of changes in the
quality of the forecast depending on the use of different data sets was carried
out. Conclusion. The reverse-calibration method proposed in this work uses the
uncertainty of geophysical data as a hyperparameter of the global tuning of the
technological stack, within the given limits of the a priori error of these
data. It is shown that the method improves the quality of the forecast. The
technological stack of machine learning algorithms used in this work allows
expert-independent generalization of geological and geophysical data, and use
this generalization to test hypotheses and create geological models based on a
probabilistic view of the reservoir.
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