Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

• Published in: Neural computing & applications 2013-11, Vol.23 (6), p.1763-1770
• Main Authors: Fegh, Asaad, Riahi, Mohammad Ali, Norouzi, Gholam Hosein
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Characteristics of producing layers. Reservoir geology. In situ fluids; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Crude oil, natural gas and petroleum products; Crude oil, natural gas, oil shales producing equipements and methods; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Energy; Exact sciences and technology; Fuels; Image Processing and Computer Vision; Memory organisation. Data processing; Original Article; Probability and Statistics in Computer Science; Prospecting and production of crude oil, natural gas, oil shales and tar sands; Software
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1142-8

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.