--> Challenges Related to the 3D Geo-Cellular Modeling of Vulcaniclastic Mature Oil Field in Central Sumatra, Indonesia

AAPG/SEG International Conference & Exhibition

Datapages, Inc.Print this page

Challenges Related to the 3D Geo-Cellular Modeling of Vulcaniclastic Mature Oil Field in Central Sumatra, Indonesia

Abstract

Abstract

The benefits of building realistic 3D geo-cellular models for oil and gas fields are substantial. Beside the quick update of the oil and gas reserves, such models can be used for the planning of different schemes for enhanced oil recovery (EOR). In addition those models can provide the fundament for intelligent reservoir management necessary for maximizing the economical extraction of the hydrocarbons.

We are sharing our experience with the building of the 3D model for Meruap field – a complex multilayer faulted field in Central Sumatra, Indonesia, where the reservoirs are represented by Miocene deltaic/ fluvial sequences with abundant vulcaniclastic. The field has been discovered by British Petroleum in 1974 and has been on production since the middle of the 1980th. As of the end of 2014 the field has 65 wells and the cumulative production reached 15 million BBL of oil. Sumudra Energy is the current operator of the field, and currently is in advance planning stage for implementing EOR.

We are focusing on the challenges encountered during the building of Meruap 3D model related to:

  • - the depositional mode - based on well logs and cores. The available 3D seismic survey was able to identify the major faults but did not provide recognition of the depositional patterns and trends due to low vertical resolution and high attenuation of the seismic signals from the gas cups and the interbedded shales and volcanic tuff. Therefore Image training and object modeling were not attempted and the priority was given to the stochastic modeling of the facies with variograms.
  • - the saturation model - based on capillary pressure and Leveret J function using Special Core Analyses data and determination of the Free Water Level for each zone.
  • - the identification of pay reservoirs for vulcanoclastic zones - difficult task due to presence of high resistivity pyroclastics minerals. In some of the target zones the formation water is very fresh and can be mistaken for oil due to high resistivity readings. Therefore a new resistivity property (R_deep) has been introduced in the model which has been stochastically distributed by co-kriging with the water saturation (Sw) from the saturation model.
  • - the pay flag for each reservoir cell - has been calculated with specific cut-offs for porosity, Sw and R_deep in such fashion that all well testing results are honored.
  • null

    The future tasks are to create dynamic model with well history matching for the reservoirs planned to undergo EOR.