Well-test model identification with self-organizing feature map
- SHL Systemhouse, Dallas, TX (United States)
- Arco E and P Technology, Plano, TX (United States)
Well-test data have been used traditionally for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, and reservoir mechanism. A number of techniques, both conventional methods, such as type-curve matching and numerical simulation, and artificial intelligence (AI) methods, have been used for identifying well-test models. These methods are laborious and time-consuming and at times give incorrect results. Artificial neural networks (ANN`s) are recent developments in computer vision and image analysis. These networks are specialized computer software that generate a strategy to produce nonlinear mapping functions for complex problems. ANN`s are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well-test data have been reported. These applications are mostly model-specific (developed for specific reservoir models) and, hence, are not general enough. This paper presents a new method based on ANN`s that uses Kohonen`s self-organizing feature (SOF) mapping technique to identify well-test interpretation models. By grouping well-test data into distinct categories, the SOF algorithm produces a general mapping function. This method can help analyze well-test data from a large variety of reservoirs (including reservoirs with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was feasible previously.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 392237
- Journal Information:
- SPE Computer Applications, Journal Name: SPE Computer Applications Journal Issue: 4 Vol. 8; ISSN 1064-9778; ISSN SCAPEP
- Country of Publication:
- United States
- Language:
- English
Similar Records
Well test model identification using self organizing feature map
A survey of artificial neural network applications in flow analysis of energy networks
An implementation of a hybrid intelligent tool for distribution system fault diagnosis
Conference
·
Sat Dec 30 23:00:00 EST 1995
·
OSTI ID:207356
A survey of artificial neural network applications in flow analysis of energy networks
Technical Report
·
Mon Oct 31 23:00:00 EST 1994
·
OSTI ID:10193191
An implementation of a hybrid intelligent tool for distribution system fault diagnosis
Journal Article
·
Mon Mar 31 23:00:00 EST 1997
· IEEE Transactions on Power Delivery
·
OSTI ID:492223