Publications

Singa field has proven gas from carbonates of the Lower Miocene Baturaja Formation. The field is over 9 km long and 2.5 km wide and is located in Lematang Block, the central part of the Lematang Deep between the southern trend of Pendopo anticlinorium and Muara Enim anticlinorium. One exploration well and one delineation well have already been drilled and the plan is to drill 2 horizontal development wells. The field has a maximum gas reservoir thickness of 282 feet and contains an average of 30% CO2 and 110 ppm H2S.Significant porosity (> 15%) has been observed in the upper part of the Baturaja Formation (BRF), 282 feet thick in the S1 well and 125 feet thick in the S2 well. Dominant porosity in thin sections is mouldic and micro vuggy, with some fractures. The lower BRF is carbonate mudstone to wackstone, with minor porosity (~8%) interpreted as a platform carbonate. The upper BRF is limestone with skeletal wackstone and packstone containing algalforam- mollusc and a coral-algal-foram-mollusc intraclast floatstone, interpreted as lagoonal and back reef facies.There are two concerns regarding this deep carbonate field: reservoir quality and porosity distribution. Because of the high CO2 and H2S content within the gas target, the reservoir has to be large enough to be developed economically. Seismic inversion and attributes techniques are applied to characterize the reservoir and to quantify reservoir distribution. The objective is to predict reservoir quality and porosity distribution in order to generate an updated geological model for reservoir modelling and to optimize new well locations.Cross-plotting acoustic impedance (AI) vs. porosity from well logs shows that the gas and brine values are populated in separate trends, with a good relationship (minimal scatter). This suggests that porosity can be estimated from AI by a transform equation. Integrated with the AI method, seismic amplitude-envelope (AE) attribute is used to aid facies interpretation and to identify the carbonate depositional setting. Integration of AI and AE is useful for predicting reservoir quality and porosity distribution in this field. As a result of using updated reservoir modelling based on the seismic inversion and seismic attribute results, the field development plan can be revised to include the drilling of two development wells.