This paper presents a new approach for estimating rock elastic properties in wells, especially for wells with limited log suites. The approach is basically a combination of efforts that are put is a series of steps. Firstly, is to model a synthetic S-wave velocity profile for a key well, with support from laboratory acoustic measurement on core samples, enabling the establishment of profiles of elastic properties through the theory of elasticity. Secondly, the modeling and validating of relationship between P-wave velocity, Poisson ratio (as an example in this paper), porosity, water saturation, shale contents, and matrix density based on the log data available for the key well. Thirdly, prediction of all missing log suites (if any) for other wells using soft computing (artificial intelligence/neural network) that has been ‘trained’ using data from the key well and other wells that have more or less complete log data. Fourthly, estimation of rock elastic properties for all wells (except the key well) using soft computing. Finally, evaluation of results using comparison with the model validated in the key well. The method has been applied on 14 wells of an active oil reservoir in Java, Indonesia. Comparisons of porosity and water saturation values between results from standard log interpretation and results from the validated model serve as indicators for the success of the method. The reasonably good comparisons achieved have proved that the new approach is applicable, and the use of the model relationship avoids ‘blind’ estimation often practiced in reservoir characterization.

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