Application of Bayesian in determining productive zones by well log data in oil wells

Pedram Masoudi (1) , Behzad Tokhmechi (1) , Majid Ansari (1) , Mohammad Jafari (2) , Shahram Zamanzadeh (3)

1 School of Mining, Petroleum and Geophysics Engineering
2 Faculty of Geography
3 NIOC - National Iranian Oil Company

Exploration specialists conventionally utilize a cut-off-based method to find productive zones inside the oil wells. Using conventional method, pay zones are separated crisply from non-pay zones by applying cut-off values on some petrophysical features. In this paper, a Bayesian technique is developed to find productive zones (net pays), and Bayesian Network is used to select the most appropriate input features for this newly developed method. So, two Bayesian methods were developed: the first one with conventional pay determination inputs (shale percent, porosity and water saturation), the other with two inputs, selected by Bayesian Network (porosity and water saturation). Two developed Bayesian methods are applied on well log dataset of two wells: one well is dedicated for training and testing Bayesian methods, the other for checking generalization ability of the proposed methods. Outputs of two presented methods were compared with the results of conventional cut-offbased method and production test results (i.e. a direct procedure to check validation of proposed methods). Results show that the most prominent advantage of developed Bayesian method is determination of net pays fuzzily with no need to identify cut-offs, in addition to higher precision of classification: nearly 30% improvement in precision of determining net pays of first well (training well), and about 50% improvement in precision of determining productive zones through the generalizing well.

Mots clés: net pay; productive zone; Bayesian Network; petrophysics; well test

Domaines: Géologie appliquée

Pedram Masoudi, Behzad Tokhmechi, Majid Ansari, Mohammad Jafari, Shahram Zamanzadeh. Application of Bayesian in determining productive zones by well log data in oil wells. Journal of Petroleum Science and Engineering, 2012, 94-95, pp.47-54. ⟨10.1016/j.petrol.2012.06.028⟩. ⟨insu-01382398⟩