| dc.description.abstract | Specific computational tools help the geologist to identify lithologies and stratigraphic
stacking in well drilling, reducing operational costs and managing the professionals
practical work time, directing them to efficient data interpretations or even in the
improvement of scientific research in the region. in geologically distinct regions. In this
study, the application of machine learning algorithms for the supervised classification
of lithologies was evaluated. Data from records of multivariate parameters in offshore
wells were used, related to the International Ocean Discovery Program (IODP) with
supervised and unsupervised data (images), with the creation of a context of hybrid
application of algorithms, divided into two manuscripts. In manuscript I, through the
analysis of the lithologies proposed in 7 IODP-Expeditions and the use of the use of
the algorithms, it was possible to group and divide the lithological sets into four groups
of lithologies and templates. The geophysical properties used in the present study
included GRA, PWL, MS, RSC and SRM. The templates were submitted to training
and testing by the Multi-Layer Perceptron (MLP), DecisionTree, RandomForest and
Support Vector Machine (SVM) methods, using the classification metrics as the result
evaluation. As a result, it was observed that Template1 obtained better results in the
MLP algorithm, Template2 and Template3 obtained better results in the RandomForest
algorithm above 80.00% accuracy. For Cross-validation, the RandomForest algorithm
achieved excellent performance in all scenarios. In the Practical Template, the G2
lithology group obtained the best result with the MLP algorithm with an average
accuracy greater than 85.00%. For manuscript II, the division of the data included the
formatting of three datasets: dataset0, dataset1 and dataset2, specifically with the data
obtained during the IODP-Expedition 362. The petrophysical data used included PWL,
GRA, RSC, NGR, MAD, MS, RGB and high-definition images. Dataset0 included
temporary training data to validate the best interpolator. The dataset1 has the
interpolated data of the petrophysical properties, making a total of 295,945 records for
U1480 and U1481 with 17 features. The dataset2 covers the texture and color data
extracted from the segmentation of the images, making a total of 85,058 records for
the U1480 and U1481, with 90 features. Each data set is replicated in two groups of
lithology: Group 1 and Group 2. For dataset2, new combinations are added between
the features forming 102 practical arrangements with specific results in each
combination. The values were interpolated by Linear, Spline, Slinear, Quadratic, Cubic,
Akima, Pchip and Piecewise. The machine learning method used for all datasets is
RamdomForest. The results show that the best interpolator evaluated in dataset0 is
Akima with an accuracy of 98.22%. For dataset1, U1480, the accuracy value is 96.96%
in the combination of 70% training and 30% testing in Group 1 and 97.71% in the
combination of 70% training and 30% testing in Group 2. For dataset1, U1481, the
accuracy value is 99.68% in the combination of 80% training and 20% testing in Group
1 and 99.74% in the combination of 80% training and 20% testing in Group 2. For
dataset2, the best evaluated practical arrangements are 51 (Group 1) and 102 (Group
2) for U1480 and practical arrangements 32, 33, 44, 45 and 51 (Group 1) and 102
(Group 2) for U1481. Regarding the new Area Superpixel (Apx) method, the best
results are in Group 1 with the greatest combination of training and the least
combination of tests. The evaluated datasets were grouped in an organization between
the expedition sites, in which a context of real practical application in the daily activity
of the geologist was generated with excellent results of lithological classification. The
interpolation of petrophysical data is valid and necessary when there is little data for
training respecting the characteristics of each property and interpolator. Properties
extracted from the images are relevant and grouped together with the petrophysical
properties create a context of extreme importance in the discovery and presentation of
information to the geologist. | en |
