Identification of Well-Test Models by Use of Higher-Order Neural Networks
- A.O. Kumoluyi (Imperial College) | T.S. Daltaban (Imperial College) | J.S. Archer (Imperial College)
- Document ID
- Society of Petroleum Engineers
- SPE Computer Applications
- Publication Date
- December 1995
- Document Type
- Journal Paper
- 146 - 150
- 1995. Society of Petroleum Engineers
- 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 7.6.6 Artificial Intelligence, 4.3.4 Scale, 6.1.5 Human Resources, Competence and Training, 5.3.2 Multiphase Flow, 5.6.4 Drillstem/Well Testing
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In a well test, the central objective is to "prove" the formation in terms of a sustainable economic flow rate. This objective is related to the characterization of the reservoir and the estimation of its parameters, like permeability and skin, by knowledge of only the input and output signals, such as pressure and flow rate, and, in certain cases, other reservoir characteristics, such as the drainage area and the distance to faults.
Interpretation efforts, therefore, can be performed in two phases: (1) the identification of "reasonable" theoretical reservoir models representing the field data, and (2) the estimation of pertinent parameters of the selected theoretical reservoir model. The second phase is well established in the literature, whereas many problems are associated with model selection, such as uncertain data, incomplete data, and the ambiguity of the models (i.e., more than one model might be representing the field data).
One can identify the theoretical models based on similarities of the curvature of both the type curves (from both simple analytical models and complex numerical models) and the field data. The whole process can be conceived as a pattern-recognition task. In this paper, we present an approach by which it is possible to identify an appropriate well-test model in a consistent and objective manner, thereby reducing some of the problems associated with the task. The approach is based on the application of higher-order neural networks (HONN's).
Conventional neural networks are used in general for both optimization and classification tasks. For classification purposes, these networks will find appropriate mappings to any set of patterns with dependence on both spatial and temporal positions. That is, such networks find it extremely difficult and often impossible to deal with patterns invariant to some transformation groups, such as scaling, translation, and rotation. Well-test models are inherently translation invariant with respect to the field data and are also scale invariant. Thus, we propose the use of HONN's, by which we can encode the invariant properties of patterns into the architecture of the network, thereby constraining some of its weights (parameters). Constructing such networks enables us to identify well-test models in an automated and robust manner. Moreover, these networks are both pattern and training-rule independent. Additionally, the training time for such networks is extremely fast compared to conventional neural networks.
|File Size||1 MB||Number of Pages||5|