Majid Al-Hasani graduated from Leeds University, UK in 2002 with BSc in Physics. He joined Petroleum Development of Oman in 2002 as a petrophysicist. He started in the New Oil team handling PP rig activities including well picks, logging programs, logging follow-up, Qc…. Four years later, he joined the Well Reservoir Management team to handle the hoist activities and to plan & execute the surveillance strategies for three fields. In 2008, he joined the study centre team and embarked on SCAL programs and field development plans. During his time with Petroleum Development of Oman, Majid did his MSc in Reservoir Engineering & Natural Gas at Sultan Qaboos University in Sultanate of Oman is 2007.

Abstract
Carbonate Reservoir Rock Typing: A Case Study in North Oman

Modeling permeability in carbonate reservoirs is a common challenge in the petroleum industry. One well known method is the Lucia approach which has been successful in many fields. In the Lucia approach, the relationship between permeability and porosity is classified per rock fabric number. Each rock fabric number class contains geological information of particle size and sorting. The calculated permeability is usually compared to core permeability for calibration.

This approach is adapted in a carbonate reservoir in the north of the Sultanate of Oman. One key challenge in this reservoir is to recognise facies from standard open hole logs. Absolute log values are not consistent for a single identified facies, even within same wells. The reservoir rock typing technique is used to account for the variable pore structure and its influence on permeability. For example, the best reservoir rock, usually grainstones and grain dominated packstones with RRT = 1-2, has a low porosity with high permeability, while mud dominated packstones and wackestones with RRT in the range 2-3.5 usually have a high porosity with low permeability. The first step in the approach is to obtain RRT from porosity & fluid saturation logs. Then, RRT instead of RFC (rock fabric classification) is used to compute permeability from the Lucia poro-perm transform. Note that RRT is a continuous/fractional number, while the RFC number is discrete.

This study also uses an interactive static-dynamic modelling feedback loop to optimize permeability and RRT coefficients. The permeability obtained from the initial RRT coefficients by matching the calculated permeability to core permeability, does not result in a good history match. The process of iteration, to change permeability by changing RRT coefficients was repeated until a satisfactory history match was obtained.

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