APPLICATION OF ARTIFICIAL INTELLIGENCE FOR FLUID TYPING IN AN EXPLORATION ENVIRONMENT USING CALIBRATED COMPOSITIONAL DATA

摘要

Identifying the type of fluid that will be produced at surface is a significant reservoir characterization challenge that is prone to error and uncertainty in an exploration environment.It usually requires rigorous solution of equation of state coupled with phase envelopes,which are usually available after drilling the well.These errors have significant impact on development plans and accurate reserves assessment. With the introduction of advanced mud gas logging systems(AMG),quantitative assessment of gas data comparable to PVT analysis could be achieved while drilling.To get the most representative fluid typing results,a framework has to be established where local production data is compared to compositional data from PVT through model building techniques. The successful application of this technique has many advantages;it allows accurate fluid typing in real-time for reservoir characterization.This information can impact a spectrum of decisions,starting from rig operations to simulation efforts. In this study,decision trees,one form of artificial intelligence,are used to build a model that maps compositional data to production using local data sets. The resulting model is then used as a predictive tool to identify fluid types using AMG data while drilling before any other formation evaluation data,such as wireline logs,become available.