52nd U.S. Rock Mechanics/Geomechanics Symposium,
2018. American Rock Mechanics Association
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89 since 2007
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ABSTRACT: Asmari and Sarvak limestones are two main oil producer formations in Iran and the Middle East. The production and optimal utilization of these reservoirs will have a significant impact on the economy of the petroleum industry. Geomechanical modelling of oil reservoirs are widely used in optimum drilling, production and reservoir compaction. Hence, the static Young’s modulus (Es) is one of the most essential parameters for any reservoir geomechanical modelling. However, information on the values of Es along the well depth is often discontinuous and limited to the core locations. Therefore, dynamic Young’s modulus (Ed) determined from open-hole log data such as density and compressional and shear wave velocities could result in continuous estimation of elastic properties of the formations versus depth. Nevertheless, static parameters are more reliable than the dynamic parameters and they are widely accepted by geomechanics around the world. The relationship between the static and dynamic elastic modulus in rock materials has been frequently addressed in scientific literature. Overall, when it comes to the study of materials with a wide range of elastic moduli, the functions that best represent this relationship are non-linear and do not depend on a single parameter. Therefore, finding a valid correlation between static and dynamic parameters could result in a continuous and more reliable knowledge on elastic parameters. In this study, published data of the tests which were carried out on 45 Asmari and Sarvak limestone core specimens are used. Then, as an artificial intelligence method, artificial neural networks were developed to correlate Es and Ed data. After comparing the results of the suggested method with correlations which were established between dynamic and static measurements, a good agreement was observed. The accuracy of the obtained results have shown that artificial neural networks are appropriate tools to predict the values of Es based on Ed data of limestone formations.
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