Estimation of minimum horizontal stress, geomechanical modeling and hybrid neural network based on conventional well logging data – a case study
| Autor: | Mohsen Hadian, Mostafa Mansouri Zadeh, Majid Jamshidian, Sahand Nekoeian, Morteza Mansouri Zadeh |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Environmental Engineering Artificial neural network business.industry 020209 energy Computer Science::Neural and Evolutionary Computation Well logging Evolutionary algorithm Particle swarm optimization Imperialist competitive algorithm 02 engineering and technology Structural engineering Pollution Backpropagation Physics::Geophysics Hybrid neural network 020401 chemical engineering Multilayer perceptron 0202 electrical engineering electronic engineering information engineering Geotechnical engineering 0204 chemical engineering business Waste Management and Disposal |
| Zdroj: | Geosystem Engineering. 20:88-103 |
| ISSN: | 2166-3394 1226-9328 |
| DOI: | 10.1080/12269328.2016.1227728 |
| Popis: | The minimum horizontal stress (Shmin) is one of the three principal stresses and is required for evaluation of the hydraulic fracturing, sand production, and well stability. Shmin is obtained using direct methods such as the leak-off and mini-frac tests or using some equations like the poroelastic equation. These equations require some information including the elastic parameters, shear sonic logs, core data and the pore pressure. In this study, a geomechanical model is constructed to obtain the minimum horizontal stress; then, an artificial neural network (ANN) with multilayer perceptron and feedforward backpropagation algorithm based on the conventional well logging data is applied to predict the Shmin. Cuckoo optimization algorithm (COA), imperialist competitive algorithm, particle swarm optimization and genetic algorithm are also utilized to optimize the ANN. The proposed methodology is applied in two wells in the reservoir rock located at the southwest of Iran, one for training, and the other... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|