Reservoir characterization to derive properties

Reservoir Characterization

PGS has developed unique reservoir characterization and quantitative interpretation workflows to derive fluid distribution and lithology to the highest accuracy in 3D and 4D.

Our reservoir characterization workflows are applicable in a number of settings from frontier basin reconnaissance, through exploration, appraisal and field development, and further to production optimization. They can be tailored to our clients' needs.

Technology

Seismic Data Conditioning

Seismic data conditioning, using a Reservoir Oriented Processing (ResOP) workflow, is a key improvement for any quantitative seismic interpretation or reservoir characterization project using pre-stack seismic inversion technology. A tailored processing sequence is applied to remove noise and enhance amplitude, bandwidth and alignment and to condition the data specifically for quantitative interpretation.

AVO/AVA Analysis

PGS uses a very efficient AVO/AVA workflow that allows for the rapid evaluation of prospectivity over large datasets. The elastic attributes calculated are routinely used as a first step in derisking prospectivity using prestack seismic data.

Rock Physics Analysis

PGS’ specialist approach to rock physics and AVO studies is based on a robust statistical rock physics workflow and is the foundation of more extensive quantitative interpretation studies enabling us to handle rock physics models in the depth domain.

Seismic Inversion

Our seismic inversion workflow is capable of handling 2D or 3D input data independently or in combination. For example, wells on matched 2D lines are tied to a 3D survey for both inversion calibration and model-building constraints. In the absence of well data within a survey (or tie-lines), wavelets can be derived statistically and models can be built entirely using regional rock physics trend information.

Geostatistical Depth Conversion

The depth conversion workflow is an integral part of our reservoir characterization workflow. It is especially relevant for lithology/fluid prediction. The primary objective is to produce a velocity model with a stable and geologically plausible background trend which will enable accurate depth conversion over the study area.

Lithology/Fluid Prediction

The PGS lithology/fluid prediction workflow utilizes a Bayesian supervised classification scheme to make quantitative predictions based on inverted seismic data and depth-dependent, statistical rock physics models over a target zone of interest. The process generates lithology and fluid probability volumes.

Bayesian Seismic Inversion

Determining the uncertainty inherent in reservoir property estimation is a key requirement for the risk assessment of an existing asset. PGS uses a state-of-the-art Bayesian inversion which directly inverts reservoir properties in the depth-domain. This, coupled with depth dependent rock physics, provides a unique workflow for asset de-risking.

Pore Pressure Prediction

PGS has developed a state of the art methodology to generate seismic pore pressure cubes by incorporating high-density velocity analysis into an integrated workflow.

Examples

Reservoir Rock Physics Trends in the Barents Sea

Our detailed Barents Sea rock physics study focuses on the most prolific Jurassic and Triassic reservoirs. This project establishes key depth dependent rock physics trends in the Norwegian Barents Sea region.

Reservoir characterization to derive properties | PGS