Geostatistical Inversion of Spectrally Broadened Seismic Data for Re-Evaluation of Oil Reservoir Continuity in Inas Field, Offshore Malay Basin

Abstract

The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

“…After applying fluid substitution to the Utsira Formation and generating synthetic seismic traces for each CO 2 saturation state, a clear fluid effect can be seen. The inversion was then carried out after effectively tweaking the impedance parameters with the help of the cross plots from the well information [50,51]. The time slices taken at 2400 ms, with a window centered on the target and a fixed size of 10 ms, highlight the simultaneous and sequential prediction results.…”

CO2 Injection Monitoring: Enhancing Time-Lapse Seismic Inversion for Injected Volume Estimation in the Utsira Formation, Sleipner Field, North Sea

“…While it is essential to acknowledge the non-uniqueness inherent in the inversion result [34], we pursued a systematic approach to estimate the volumetric changes evident in the 4D anomaly map, reflecting seismic variations attributed to the injection process. To enhance the precision of our time-lapse interpretation, we calculated the reservoir volume affected by the injection process and then cross referenced it with the recorded injection volumes for the field.…”