
TY  - JOUR
A1  - Shadlow, James 
A1  - Craig, Adam 
A1  - Christiansen, David 
A1  - Mitchell, Robert 
T1  - Quantitative Interpretation: Use of Seismic Inversion Data to Directly Estimate Hydrocarbon Reserves and Resources
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2018
VL  - 2018
IS  - 1
SP  - 1
EP  - 9
KW  - 
UR  - https://doi.org/10.1071/ASEG2018ABW8_3A 
N2  - A quantitative interpretation workflow utilising AVO inversion based lithology prediction data was developed to directly assess reserves and resources for an LNG development project in the Carnarvon Basin. The study area is covered by modern MAZ PSDM 3D seismic data using broadband acquisition and processing techniques, calibrated by numerous well intersections of the Triassic Mungaroo Formation reservoirs. Interpretation of the fluvio-deltaic reservoir bodies can be somewhat interpretive using &#x2018;traditional&#x2019; workflows. By interpreting chronostratigraphic events tied to well-based biostratigraphy and then using the lithology prediction volumes, the interpretation of reservoir bodies becomes more objective. Seismic inversion data are typically used to qualitatively guide resource assessments, through amplitude mapping or use in static and dynamic modelling. In this case study, the inversion based prediction volumes are used to extract P90, P50 and P10 sand geobodies which are directly input into probabilistic reserve and resource assessments. The workflow is applied to discovered, developed, undeveloped and prospective reservoirs. Geobody extraction required the PSDM depth data to be accurately calibrated to wells. A calibrated velocity model was built by perturbing the imaging velocities in a 3D model to tie the chronostratigraphic events associated with all the reservoir intervals. Fluid contacts derived from wells were used to provide a depth cut-off to the geobody extractions. The resulting reserve and resource assessments from this workflow show an excellent match with previous assessments including static and dynamic modelling methods. The geobodies also identified previously unrecognised channel sands not easily interpreted on full and angle stack data. 
ER  -

