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.
Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples, has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes.
Data from all sources, at all scales, has helped to assess petroleum potential offshore Mozambique beyond the relatively small play fairway proven to date. New insights into proven and potential reservoirs, plus improved predictions of the presence, type and maturity of source rocks, have enabled the development of new play concepts.
Demonstrate the value of least-squares migration on various complex geology around the world. It includes real data examples published by various SI teams such as least-squares Kirchhoff for tow-streamer/OBS 4D, LS-RTM for subsalt imaging, least-square Q-PSDM on Northsea & Austrialia.
Although good for time-lapse monitoring, ocean-bottom surveys often miss the first time-step of initial fluid production. To capture this it is necessary to co-process ocean-bottom data with the exploration dataset, usually towed streamer. Our North Sea study has significant 4D noise produced by un-cancelled migration operator from the very different, irregular, survey geometries after 4D binning. We migrate to common-offset, scattering-angle, and dip-angle output domains to explore similarity-filtering strategies with the data. Both the scattering- and dip-angle gathers respond well to similarity filtering, but results show greatest signal continuity and coherence when dip-angle gathers are used.