Well test model identification using self organizing feature map
Well test data has been traditionally used for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, reservoir mechanism, etc. A number of techniques, both conventional methods such as type curve matching and numerical simulation, and artificial intelligence 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) are recent development in computer vision and image analysis. These are specialized computer software that generate a strategy to produce non-linear mapping functions for complex problems. ANN 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 methods are mostly model-specific (developed for specific reservoir models) and hence are not general enough. This paper presents a new method based on ANN 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 previously feasible.
- OSTI ID:
- 207356
- Report Number(s):
- CONF-950648-; TRN: 96:002120-0030
- Resource Relation:
- Conference: Petroleum computer conference: leveraging technology, Houston, TX (United States), 11-14 Jun 1995; Other Information: PBD: 1995; Related Information: Is Part Of Petroleum computer conference; PB: 334 p.
- Country of Publication:
- United States
- Language:
- English
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