The Application of Committee Machine With Intelligent Systems to the Prediction of Permeability From Petrographic Image Analysis and Well Logs Data: A Case Study From the South Pars Gas Field, South Iran
| Autor: | Ali Kadkhodaie-Ilkhchi, Javad Ghiasi-Freez, Mansur Ziaii |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Artificial neural network
General Chemical Engineering Well logging Intelligent decision support system Energy Engineering and Power Technology Mineralogy General Chemistry Geotechnical Engineering and Engineering Geology Fuzzy logic Petrography Permeability (earth sciences) Fuel Technology Committee machine Porosity Geology |
| Zdroj: | Petroleum Science and Technology. 30:2122-2136 |
| ISSN: | 1532-2459 1091-6466 |
| Popis: | Permeability is the ability of porous rock to transmit fluids. An accurate knowledge of reservoir permeability is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs, and intelligent systems to predict permeability. Petrographic image analysis was employed to measure the optical porosity, pore types, pore morphologies, mineralogy, amount of cement, and type of texture. Available conventional log measurements include bulk density, neutron porosity, and natural gamma ray. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL), and a neuro-fuzzy (NF) model. Afterwards, two types of committee machine with intelligent systems (CMIS) were used to combine the permeability values calculated from the individual intelligent systems: simple averaging and weighted averaging. In the weighted averaging, a genetic algorithm model was em... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|