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The objective of this study is to characterize sand reservoirs by using seismic inversion technique, the results were used to support CO2 storage potential identification and reservoir modeling works (storage volume calculation). The key storage targets are the saline aquifers and depleted reservoirs. These main targets were interpreted as a deposition of distributary channels occurring in the Paleo Chao Praya delta plain during Miocene. The results of this project contribute to a more accurate volume calculation for CO2 storage capacity. A rock physics feasibility analysis was carried out to understand a link between the observed seismic responses and the rock properties. Based on conclusions made in the rock physics analysis, P-Impedance could be used to delineate sand reservoir from shale, thus, a post-stack deterministic seismic inversion was selected for this reservoir characterization. Bayesian litho-classification method justifies lithology types by Probability Density Function (PDF) of P-Impedance, the resulting PDF was then applied to the inverted relative P-Impedance to create sand probability and lithology (most probable) volumes. Then, posterior validation of the lithology classification results was performed by investigating the match between the actual upscaled lithology log and pseudo lithology log from the Bayesian classification. Furthermore, the sand probability maps of the target reservoirs show an acceptable sand distribution response to the distributary channels in lower coastal plain environment that is consistent with the well results. The results of this work demonstrate how quantitative interpretation (QI) can successfully improve confidence in sand reservoirs mapping, in an area of complex faulted reservoir interval. The results presented here are beneficial for storage potential identification and reservoir modeling part, which can provide a more precise estimation of CO2 storage volume. The final results of the QI study provide good quality seismic inversion products and lithology cube, which enabled sand delineation at the target CO2 storage level. The key contributors have been ensuring optimal seismic input data, being in this case achieved through using a PSDM seismic processing technology, careful parameterization of seismic inversion process, and utilization of Bayesian classification method for lithology classification.

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Combination and stratigraphic traps may contribute with significant gas reserves in Bongkot Field, Gulf of Thailand. However, such stratigraphic play cannot be easily defined as conventional seismic interpretation provides mainly structural information. To identify stratigraphic prospects in this area, a seismic pre-stack inversion and reservoir characterization study was carried out. The input dataset consisted of 365 km2 of 3D seismic, six wells, and interpreted time horizons. Following seismic pre-conditioning and rock physics analysis, wavelet extraction and well-ties were performed for each individual well, considering every input angle stack. Constrained by input time horizons, low frequency models were built based on well log and seismic stacking velocity. Inversion parameters were tested; subsequently, final inversion results were subjected to Bayesian classification to obtain a litho-facies volume. In addition, multi-linear regression was used to derive elastic-petrophysical relationships, to generate petrophysical property volumes. The final results included inverted elastic properties, classified litho-facies, computed effective porosity and Vshale volumes. By analyzing these results, several channel and deltaic/sand lobe features could be observed throughout the study area. Connected sand-filled channels with high porosity were mainly observed in the shallow section, as sand distribution appeared sparser and more isolated with increasing depth. Also, the predicted reservoirs in the deeper section were mostly filled with gas, while shallower sand bodies were mostly filled with brine. This observation implied that the high net-to-gross reservoir distribution in the shallow section can be a key factor that hinders effective trapping of hydrocarbons at this level. Since reservoir distribution plays a key role in hydrocarbon trapping mechanism, upside stratigraphic potential was identified from isolated gas-filled channels, mapped from seismic inversion products, to implement a more successful field development strategy in a mature field.

Seismic-well tie is a crucial process to correlate subsurface information from well logs and acquired seismic data. Traditionally, a manual seismic-well tie is conducted based on the interpreter's visual pattern recognition, which is subjective, time-consuming, and may lead to unrealistic velocity distortion. This paper presents a new method to automatically tie seismic to well using Dynamic Time Warping (DTW) and Optimal Interpolation (OI), to save man-hour and to obtain a more reliable time-depth relationship. To produce a better tie, we use DTW to seek the appropriate amounts of time stretching and squeezing to match the synthetic and actual seismic. Then, we balance the rigid pattern matching of DTW by using OI to smooth DTW results and constrain changed rock velocity to be within physical bound. The invented technique has been used to tie seismic to six exploration wells in the Gulf of Thailand. The results from the automated method are then compared with the manual method. For all wells, resulting synthetic-seismic correlations from the automated well tie are higher than the manual method by 1.6%-14.9%. Applied time shifts from the automated and manual methods are then compared. Notably, time adjustment correlations between the automated and manual well tie are considerably high, around 72%-85%, suggesting that both methods yield similar outcomes, yet the automated well tie gives a slightly higher match between tied synthetic and observed seismic traces.