Adi Sutanto Simanjuntak


Research has been performed with the characterization of hydrocarbon reservoir in “TAB” field using acoustic impedance inversion modeling. Acoustic impedance (AI) is a rock's ability to parse seismic waves that is the product results from rock density and velocity. Acoustic impedance also influenced by the type of litology, pressure, temperature, porosity and fluid content. This research used AI inversion method because a result of this inversion can give a imaging of the actual subsurface conditions, so that it can mapping the distribution of porosity reservoir target. The purpose of this research is determine value of acoustic impedance inversion results in the reservoir, estimate value of porosity a rocks in the reservoir and mapping the pattern of spread reservoir through analysis of acoustic impedance and porosity values. with comparing the results of inversion from some inversion modeling such as Bandlimited, Model Based and Linear Programming Sparse-Spike, so used is linear programming sparse spike model. The result of linear programming sparse spike model showing good correlation is 0.927 and the small error is 0.440 and does not depend on initial model, so it is good to used for targets that have a high reflectivity value. The results of inversion showing acoustic impedance located in low impedance zone between 2000 m/s*gr/cc - 3458 m/s*g/cc with depth around 1500 to 1700 ms. From this results has been slicing of the data. This slicing data is done with a window on 10 ms under the horizon, 20 ms under the horizon, and 30 ms under the horizon. Distribution of porosity inversion results is done by using 7 attribute. The results of porosity distribution obtained an average of 30%. Slicing porosity that shows the acoustic impedance values located in low anomaly which have a high porosity.

Keyword: Acoustic Impedance (AI), Linear Programming Sparse Spike (LPSS), porosity

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DOI: http://dx.doi.org/10.23960/jge.v2i01.214


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