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Application Of Artificial Intelligence In Well Test Data Interpretation

Salim Mehr, Mahdi | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39409 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Masihi, Mohsen; Shadizadeh, Reza
  7. Abstract:
  8. Well-Testing is a one of the usual methods in petroleum engineering for evaluating reservoir and well parameters. This method is based on measuring downhole pressure verses time in different production conditions and then drawing these data in different pressure-time graphs and evaluating reservoir characteristics and calculating reservoir parameters. This thesis describes the development of techniques for the automation of the model identification and parameter estimation of a well test interpretation, using Artificial Intelligence. The computer programs which are written in MATLAB software use Neutral Network toolbox to detect a model that is based on the pressure derivative curve, and simulates the visual diagnosis performed by a human expert. Most of the reasoning involved in such a diagnosis uses a symbolic representation of the pressure derivative curve, which in the case of a human expert is built almost unconsciously. Techniques were developed to replicate this perception step. A major difficulty in the analysis of real data, particularly using the pressure derivative, is the separation of the true reservoir response from signal or differentiation noise. Again, this is relatively simple for a human observer, but difficult to implement in a computer program. This thesis describes an algorithm developed to overcome this problem. The algorithm was able to distinguish response from noise correctly, therefore permitting correct model identification. In a manner analogous to a human expert, the technique constructs an interpretation model by combining the features of the different flow periods of the pressure response, for the entire duration of a test. The adequacy of a model is determined by qualitative as well as quantitative information. Once a model is chosen, its parameters are estimated using correlations. The software developed in this work can perform model identification on analytical as well as real data
  9. Keywords:
  10. Artificial Intelligence ; Well Testing ; Neural Network ; Reservoir Model

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