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The studied Fields cover about 1900 sq km covering fourteen wells in Abu Dhabi, UAE. It was discovered in 1962 and developed by Abu Dhabi Petroleum Company. The objective of the current study is to illustrate the comparison between prestack stochastic & prestack deterministic seismic inversion results to better understand vertical and lateral heterogeneities of upper Jurassic reservoirs and their impact on property distribution during static modelling and further into dynamic model construction.

Broadband seismic data has several benefits for quantitative seismic reservoir characterization. It is characterized by a significant increase of seismic frequency bandwidth on both the low and high sides of the frequency spectra. This work presents a novel seismic inversion approach to exploit the full value out of broadband seismic data. The average wavelet from broadband seismic data is limited in high and low frequencies due to the short duration of the well log and the misalignment of the seismic data with the well-log synthetic at high frequencies. Limitation of the extracted wavelet and optimization can generate band-limited inversion results that do not capture the full range of frequencies. An alternate approach of dividing the data into three frequency bands resulted in extracted wavelets that capture the spectrum of each band, and in turn produced a reliable broadband inversion result honoring the full range of frequencies present in the data. Inversion results gave a superior match of the estimated synthetic with the data spectra (Figure 1), and the reservoir was better calibrated at all the well locations. Successful recovery of the ultra-low frequencies enabled us to maximize the value of the broadband data. The workflow also pushed the frequency of the inverted properties to 80 Hz which helped in turn to characterize some of the relatively thinner layers, which were otherwise getting averaged out. Building a low frequency model for AVO seismic inversion using ultra-low frequency information leads to a significant improvement of predictability away from wells. As a prior model, a geologically constrained (4 Hz) low frequency filter was applied. Review of the broadband AVO seismic inversion results clearly indicate a better match between the inverted traces and well log properties at the studied wells. Also, the blind well test results at four wells indicate an excellent match to the blind well logs, which adds a high degree of confidence on the inverted elastic properties. Also, the synthetic spectra of the ultra-low and ultra-high frequencies is captured and maintained in the inverted broadband seismic data. The novelty of the new workflow is in the ability to effectively invert the broad frequency band of seismic data. Successful recovery of the ultra-low and ultra-high frequencies enabled us to maximize the value of the broadband data. Subsequently, the high frequency elastic properties helped in successful characterization of thinner reservoirs and will help in better optimization of the future field development initiatives.

The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

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