Application of Evolutionary Neural Networks for Well-logging Recognition in Petroleum Reservoir.
DOI: 10.1109/CIS.2011.87 Conference: Seventh International Conference on Computational Intelligence and Security, CIS 2011, Sanya, Hainan, China, December 3-4, 2011
A critical task of well-logging interpretation is to differentiate oil-gas-water layers. Other approaches based on data exploration and low recognition rate are difficult to generalize oil-gas-water layers identification because of the high moisture content in the later period of development. In this research we utilize evolutionary neural networks to build the interpreting model of oil-gas-water layers and extracting well-logging parameters. By using an evolutionary neural network method to recognize reservoir stratum, it can efficiently distinguish oil-gas-water layers.
Conference Paper: Qualitative interpretation of process trends by using neural networks[Show abstract] [Hide abstract]
ABSTRACT: Qualitative interpretation is a process to convert numerical output of sensors into symbolic representation. This process is one of the most critical path to connect intelligent systems with real world. In this paper, qualitative interpretation is realized as pattern-based classification of time-series signal by using ART2 neural networks. As an example, automatic classification of flow patterns in a pneumatic conveyor is successfully demonstratedKnowledge-Based Intelligent Electronic Systems, 1998. Proceedings KES '98. 1998 Second International Conference on; 05/1998
- [Show abstract] [Hide abstract]
ABSTRACT: The issue of the modeling for the distribution management based on demand forecasting. ANN model is applied to the field of demand forecasting. The modeling of market demand forecasting is built using BP algorithm. The model of forecasting is established by Simulation model of MATLAB. Based on them, a simple numerical example is given to test and forecast with three-layer BP network model.Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on; 09/2010
- [Show abstract] [Hide abstract]
ABSTRACT: The author discusses an automatic method for the direct mapping of the constraints and the objectives related to 0-1 programming of formulated combinatorial optimization problems onto neural networks. The model is a massively interconnected network (a Hopfield network or a Boltzmann machine), and the right connection pattern and the associated appropriate connection strengths are generated. The author explains why constraints and objectives can be efficiently mapped onto such a network through weights. A proof that generated weights imply constraint satisfaction is presented. The author gives a set of general forms of met constraints and translates them into weights. It is shown that it is difficult to generate a network with weights that simultaneously satisfy all the constraints. A way to build such a network by using Π-E units is proposed
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.