Estimation of Rock Dynamic Elastic Property Profiles through a Combination of Soft Computing, Acoustic Velocity Modeling, and Laboratory Dynamic Test on Core Samples

摘要

This paper presents a new approach for estimating rock elasticproperties in wells, especially for wells with limited log suites.The approach is basically a combination of efforts that are putis a series of steps. Firstly, is to model a synthetic S-wavevelocity profile for a key well, with support from laboratoryacoustic measurement on core samples, enabling theestablishment of profiles of elastic properties through thetheory of elasticity. Secondly, the modeling and validating ofrelationship between P-wave velocity, Poisson ratio (as anexample in this paper), porosity, water saturation, shalecontents, and matrix density based on the log data availablefor the key well. Thirdly, prediction of all missing log suites(if any) for other wells using soft computing (artificialintelligenceeural network) that has been ‘trained’ using datafrom the key well and other wells that have more or lesscomplete log data. Fourthly, estimation of rock elasticproperties for all wells (except the key well) using softcomputing. Finally, evaluation of results using comparisonwith the model validated in the key well. The method hasbeen applied on 14 wells of an active oil reservoir in Java,Indonesia. Comparisons of porosity and water saturationvalues between results from standard log interpretation andresults from the validated model serve as indicators for thesuccess of the method. The reasonably good comparisonsachieved have proved that the new approach is applicable, andthe use of the model relationship avoids ‘blind’ estimationoften practiced in reservoir characterization.