Habeeba Ali Al-Housani

Habeeba holds a BSc degree in Physics from Al-Ain University U.A.E.; and worked as a teacher in Physics after graduating. Habeeba joined ADCO in 2003 and worked as a Petrophysicist for the Studies Team of the Bab field for 7 seven years. Her areas of expertise include, log interpretation and petrophysical support for field management, SCAL and BHI analysis, Mini Frac and geomechanics, Reservoir Rock Typing, and Neural Net prediction of petrophysical properties.

Abstract

Title: Bab Field Artificial Neural net Prediction of Permeability Thamama Zone “B” , Petrophysical Grouping and Reservoir Rock Typing

Predicting carbonate reservoirs’ permeability in non cored wells remains as one of the most difficult challenges in static and dynamic models.

In this study, we introduce a methodology to quantify geological information and integrate this into permeability prediction studies. The input data we have used consists of a combination of open hole log data and probability distribution of reservoir rock types (RRT) from static geological model. Integrating geological information in probability domain has added important geological control to the process and leveraged the prediction levels to more than 90%. After permeability prediction we move into Petrophysical grouping(PG) assignment using the previous result as critical input data for PGs which start with MICP data then move into routine core analysis(RCA) domain ended by log domain. After PG assignment for non cored wells the PG’s results were used as critical input plus the predicted permeability and log porosity for RRT prediction for non cored wells.

Presentation link