A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms
| Parent link: | Earth Science Informatics.— .— Berlin: Springer Nature Vol. 16.— 2023.— P. 3387-3416 |
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| Other Authors: | , , , , , |
| Summary: | Title screen Mechanical brittleness index (BImech) of the rock is a necessary parameter for selecting appropriate drilling bits and proper depth intervals for hydraulic fracturing. The BImech measurement is possible through continuous coring followed by laboratory tests, which are extremely cost- and time-intensive. Although petrophysical logs provide us with some continuous pieces of information, the complex nonlinear relationship between the logs and the BImech calls for implementing intelligent approaches before one can predict BImech from the petrophysical logs. Therefore, the present research is an attempt to develop BImech prediction models using least-squares support-vector machine (LSSVM) and multilayer perceptron (MLP) neural network (NN) as well as their hybrid forms with cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA) on data from wells penetrating Maroon Oilfield. For this purpose, we began by approximating the BImech from the Poisson’s ratio and static Young’s modulus – with the later obtained from laboratory test results-calibrated petrophysical logs. Next, the entire set of available data was split into two subsets, namely modeling and validation subsets. Application of the second version of the nondominated sorting genetic algorithm (NSGA-II) combined with the MLP-NN for feature selection showed that, among the eight features considered, five made the best set for developing estimator models, including P- and S-wave velocities, depth, density (RHOB), and gamma-ray (GR) readings. Accordingly, intelligent algorithms were developed by means of these five features on the basis of the modeling data. Results of the training and testing phases showed that the hybrid algorithms were more accurate than the simple forms of either MLP or LSSVM. Among the hybrid algorithms, the highest levels of accuracy and generalizability were achieved with the LSSVM-COA. Application of the developed models on the validation data and a well in the Azadegan oil field further confirmed the high accuracy and generalizability of this model for predicting the BImech. Therefore, at a high level of confidence, the proposed model can be recommended for predicting BImech at wells penetrating similar formations Текстовый файл AM_Agreement |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12145-023-01098-1 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680215 |