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

Pedram Masoudi (1, 2) , Bita Arbab (2) , Hossein Mohammad Rezaei (3)

1 School of Mining, Petroleum and Geophysics Engineering
2 Department of Research and Technology
3 Department of Petrophysics

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 Neural Network-based method of pay zone detection.

Mots clés: net pay; production; Artificial Neural Network; carbonate; well test; cut-off

Domaines: Géologie appliquée

Pedram Masoudi, Bita Arbab, Hossein Mohammad Rezaei. Net pay determination by artificial neural network: Case study on Iranian offshore oil fields. Journal of Petroleum Science and Engineering, 2014, Neural network applications to reservoirs: Physics-based models and data models, 123, pp.72 - 77. ⟨10.1016/j.petrol.2014.07.007⟩. ⟨insu-01382434⟩