4D simultaneous PP-PS prestack inversion: the Edvard Grieg field, Norwegian North Sea

Abstract

The Edvard Grieg oil field was discovered in 2007 in the Norwegian North Sea and is operated by Lundin Energy Norway. The field is in the production stage. Production began in November 2015, and water injection began in July 2016. The oil bearing reservoir lies in a half graben in Haugaland High, composed of multi-source sediment accumulation bounded by unconformities as most of deposition occurred subareally. The early Cretaceous to late Triassic reservoir is composed of aeolian sands, fluvial sands, alluvial conglomerates, and shallow marine sands, all capped by a regionally extensive unit of chalk. Reservoir characterization challenges arise from the depositional complexity of the field and detailed analysis must be done to plan for future production. In the oil and gas industry, detailed analysis and inversion is typically done using PP seismic data. In this project, I work to evaluate the benefits of PS data to better characterize the reservoir heterogeneity and understand the effects of production and injection by performing simultaneous PP-PS prestack time-lapse inversion. My analysis begins with theoretical expectations of the PS dataset from a rock physics approach and analysis of the raw seismic and well data. The input PS data has significant signal loss from sand injectites directly above the reservoir, where PP data showed no signal loss, resulting in the PS reservoir interval to contain a 9Hz peak frequency when registered to PP time. Given this information, the expectation of the PS data was to only marginally improve model estimates. Synthetic work was done to assess inversion performance and controlling parameters. Findings show if only PP waves were used for inversion, large offsets would be needed for a partially successful S-impedance inversion, which is not available in the Edvard Grieg survey, due to a maximum 34 degrees incidence angle. This idea is reflected in the prestack PP inversion results for the field data. The prestack PP inversion produces the best estimate for P-impedance, a large improvement from post-stack inversion, however, the resulting S-impedance estimate simply follows the background relationship with the P-impedance term. By performing joint PP-PS inversion, we greatly improve the S-impedance estimates to further characterize the reservoir heterogeneity using Vp/Vs. The seismic data is shot in two vintages, 2016 and 2018, with time-lapse purposes in mind, leading to excellent repeatability (11% NRMS for PP data, 24% NRMS for PS data). Theoretically, the P-impedance estimate is influenced by fluid and pressure changes, while the S-impedance estimate is chiefly influenced by pressure. This discrepancy can be used to separate these two effects in locations where overlap and interference occurs. The inversion results showed that with limited offsets, the PP prestack inversion derived S-impedance change estimate provides no time-lapse interpretation benefits and simply mimics the changes in P-impedance. With PP-PS 4D inversion the S-impedance was able to capture geomechanical changes in the field and aid in the separation of the effects of saturation and pressure. The optimal P-impedance estimate is derived from PP prestack inversion while the optimal S-impedance estimate is derived from PP-PS prestack inversion. This S-impedance is noisier due to the PS data, but is far more accurate and allowed for better identification of reservoir quality heterogeneities from impedance extractions and the generation of facies volumes. Baffles and barriers were identified in the large sand bodies and alluvial section that correlate to the 4D response. S-impedance change is used in conjunction with P-impedance change to create saturation and pressure change maps in the reservoir. The maps are used to determine reservoir compartmentalization, monitor injected fluids, understand water drive, and identify bypass zones. The work in this thesis demonstrates the benefits of 4D joint PP-PS prestack inversion on maximizing the understanding of reservoir quality, heterogeneity, and fluid flow pathways. This information proves invaluable to industry asset teams in making drilling and reservoir management decisions.