Four recent case studies document how an advanced seismic workflow, which integrates geoscience data, enables operators to enhance their reservoir characterization interpretation.
The North West Shelf of Australia is a prolific hydrocarbon province hosting significant volumes of hydrocarbons, primarily derived from Jurassic and Cretaceous targets. A new regional, integrated geoscience study has been undertaken by CGG to develop new insights into the paleogeography and petroleum systems of Late Permian to Triassic successions which have historically been underexplored in favour of Jurassic to Cretaceous targets. This comprehensive analysis from a paleogeographic and petroleum system perspective provides a basin evaluation tool for Triassic prospectivity.
The Campos Basin, offshore Brazil, features complex shallow geology in the forms of pronounced seabed canyons and paleo-canyons. The rapid variations in the velocity field due to these complex shallow geologic features can be difficult for ray-based tomography techniques to resolve, resulting in distorted images in deeper section. Full waveform inversion (FWI) is able to utilize the recorded diving–wave energy to resolve the high-resolution velocity model in these geologically complex areas. Additionally, dip-constrained non-linear slope tomography introduces dip constraints to ray-based residual move-out tomography and is able to capture small-scale velocity anomalies associated with these shallow heterogeneities. A combined workflow of FWI and dip-constrained tomography enabled Chevron to build accurate and detailed velocity models in the Campos Basin, resulting in fewer seismic image distortions. We demonstrate the method using a Campos Basin, Brazil narrow-azimuth streamer dataset.
To help the industry identify new and previously overlooked prospects in Australia’s Gippsland Basin, CGG acquired a new basin-scale 3D seismic multi-client survey in 2020.
A new, proprietary technique that improves production forecasts has been demonstrated on an Asian gas field. Multi-scale ensemble-based history matching (MS-EnOpt) improves production forecasting and reserve estimation, and provides engineers with an understanding of key reservoir uncertainties.
Seismic inversion transforms seismic reflection data into quantitative rock-property descriptions of a reservoir. Seismic data bandwidth is limited by signal-to-noise ratio, absorption, source wavelet, and shot and receiver ghosts. A typical deterministic seismic inversion workflow fills the low frequencies by extrapolating or interpolating existing well logs along stratigraphic layers. The interpolation result is often biased by the well locations and quality of the well logs and can be affected by the interpolation method. We propose a 3-stage method to minimize the dependency of seismic inversion on a well-log based initial model and improving confidence in the final result.
Screen license round opportunities using trusted insight into the location and characteristics of natural seepage, indicative of active petroleum systems, with CGG’s focused Malaysian data packages, available for all 13 of the 2021 bid round acreages.
Least-squares migration (LSM) can potentially provide better amplitude fidelity, higher image resolution, and fewer migration artifacts than standard migration. Conventional LSM is often solved iteratively through linearized inversion, and therefore is often referred to as iterative LSM. In recent years, various single-iteration LSM approaches have been proposed as a cost-effective approximation of iterative LSM and have produced promising results. To exploit the full potential of LSM, we propose to employ the curvelet-domain Hessian filter (CHF), useful in single-iteration LSM, as a preconditioner for conventional iterative LSM. We call this approach CHF-preconditioned LSM (CPLSM). We first validate our CPLSM approach using SEAM I synthetic data and show that it produces better amplitude fidelity over the single-iteration CHF approach and converges faster than conventional iterative LSM. Furthermore, we demonstrate with an application to field data that CPLSM produces fewer migration artifacts and less noise than conventional iterative LSM. This addresses a known problem of iterative LSM that is caused by the use of inaccurate modeling algorithms followed by overfitting the modeled synthetic data to the recorded data.
A machine learning workflow can quickly, and accurately, predict mineralogy, porosity and saturation in multiple wells to better understand productive layers in unconventional oil reservoirs.