PNN also gave better results when compared to other methods like band-limited, colored, sparse-spike inversion techniques. Slicing porosity that shows the acoustic impedance values located in low anomaly which have a high porosity. We used PNN to invert the seismic data to exploit the thin bedded coalbed methane reservoir. The results of porosity distribution obtained an average of 30%. Distribution of porosity inversion results is done by using 7 attribute. 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. From this results has been slicing of the data.

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. 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. 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. LithoSi is designed to predict facies type and fluid content from seismic inversion attributes, while quantifying the uncertainty in the prediction. 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. HOUSTON Hampson-Russell Software & Services has released LithoSi, an addition to its recently revamped HRS-9 suite for reservoir characterization. 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 most basic and commonly used one-dimensional model for the seismic trace is referred to as the convolutional model, which states that the seismic trace is simply the convolution of the earths reflectivity with a seismic source function with the addition of a noise component. Acoustic impedance also influenced by the type of litology, pressure, temperature, porosity and fluid content. Acoustic impedance (AI) is a rock's ability to parse seismic waves that is the product results from rock density and velocity.

Research has been performed with the characterization of hydrocarbon reservoir in “TAB” field using acoustic impedance inversion modeling.

Colored inversion hampson russell