Pseudo Density Log Generation Using Artificial Neural Network
- Wennan Long (University of Southern California) | Di Chai (University of Kansas) | Fred Aminzadeh (University of Southern California)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 May, Anchorage, Alaska, USA
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 7.6.7 Neural Networks, 7 Management and Information, 5.6 Formation Evaluation & Management, 5 Reservoir Desciption & Dynamics, 7.6 Information Management and Systems, 5.6.1 Open hole/cased hole log analysis, 7.6.6 Artificial Intelligence, 5.1 Reservoir Characterisation, 7.6.4 Data Mining
- Model Based Clustering, Artificial Neural Network, Pseudo Well Log, Discriminant Analysis, Principal Component Analysis
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- 313 since 2007
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Reservoir characterization is often a demanding and complicated task due to the nonlinear and heterogeneous physical properties of the subsurface environment. Those issues can be overcome accurately and efficiently by the use of computer-based intelligence methods such as Neural Network, Fuzzy Logic and Genetic Algorithm. This paper will describe how one integrates a comprehensive methodology of data mining techniques and artificial neural network (ANN) in reservoir petrophysics properties prediction and regeneration. Density log, which acts as a powerful tool in petrophysical properties indication, is often run over just a small portion of the well due to economic considerations, the borehole environment or operation difficulties. Furthermore, missing log data is common for old wells, and wells drilled by other companies. Working towards a resolution to these challenges, we will demonstrate successfully constructed automatic system which includes well logging data preprocessing, data mining technologies and ANN prediction. Based on one field case study, this methodology was proficient and stable in pseudo-density log generation.
|File Size||3 MB||Number of Pages||21|
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