Net pay determination by artificial neural network: Case study on Iranian offshore oil fields

• An algorithm based on artificial neural network (ANN) is presented to identify productive zones in oil wells.
• Proposed ANN-based algorithm showed precision of 86% in carbonate reservoir of Mishrif, and 91% in sandy Burgan reservoir.
• Proposed method works precisely both in clastic and carbonate reservoirs.
• ANN-based method is more compatible with well tests in comparison to cut-off method.

Determining productive zones has always been a challenge for petrophysicists. On the other hand, Artificial Neural Networks are powerful tools in solving identification problems. In this paper, pay zone determination is defined as an identification problem, and is tried to solve it by trained Neural Networks. Proposed methodology is applied on two datasets: one belongs to carbonate reservoir of Mishrif, the other belongs to sandy Burgan reservoir. The results showed high precision in classifying productive zones in predefined classes with Classification Correctness Rate of more than 85% in both geological conditions. Applicability of proposed pay zone determination procedure in carbonate environment is a great advantage of developed methodology. Fuzzified output, being independent of core tests and verification with well tests results are of other advantages of the Neural Network-based method of pay zone detection.

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