Application of artificial neural networks for reservoir characterization with limited data

The production performance of a heterogeneous reservoir cannot be realistically predicted without accurate reservoir characterization. Lack of detailed permeability data often hampers the reservoir characterization efforts. In this study, artificial intelligence techniques were successfully utilized to predict the missing information. More specifically, the objective of this study was to develop an accurate reservoir description with the aid of artificial neural networks (ANN) utilizing available geophysical well log data and limited core data. The superior ability of ANN for pattern recognition makes it a prime candidate for permeability prediction from well log data. The most complete reservoir description is generally provided through identification and characterization of the flow units. The presence of flow units suggests that separate permeability-log data relationship exists for each unit. ow units in the wells with core data were first defined using self optimizing (Kohonen) neural network to cluster core-log data in a systematic manner. Utilizing the flow unit definition, a back-propagation artificial neural network to predict the flow units in the wells without core data was trained and tested. A second back-propagation artificial neural network was then trained and tested to predict the permeability profile within each flow unit. However, the use of limited permeability measurements to train and test the second network resulted in inconsistent predictions due to sensitivity to the arrangement of the data. An innovative approach for training and testing of the ANN was then developed to overcome the problem and to provide consistent and reliable predictions. o networks which predicted the flow units and their attributes were utilized to develop the description of the reservoir. Primary and secondary production data were utilized to evaluate the reservoir description. The reservoir description substantially improved the simulation of the secondary recovery performance. The simulation results confirmed the presence of heterogeneities which have had profound impact on the performance of the reservoir. The methodology presented in this paper can serve as a guideline for the characterization of reservoirs with limited data.