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The need to understand field-scale reservoir heterogeneity using seismic data requires implementing advanced solutions such as stochastic seismic inversion to go beyond the resolution of seismic data. Conventional seismic inversion techniques provide relatively low-resolution reservoir properties but do not provide quantitative estimates of the subsurface uncertainties. The objective of this study was to carry out a facies dependent geostatistical seismic inversion to generate multi-realization reservoir properties to improve the geological understanding of the two adjacent offshore fields in Abu Dhabi. An integrated approach of rock physics modelling and geostatistical inversion followed by porosity co-simulation was undertaken to characterize the spatially varying lithofacies and porosity of the complex carbonate reservoirs. Necessary checks to ensure highest quality data input included: 1) Rock physics modelling and shear sonic prediction 2) Invasion correction and production effect correction of elastic logs 3) Seismic feasibility analysis to define seismic facies and 4) Six angle stacks optimally defined to preserve AVO/AVA signature followed by AVO/AVA compliant post-stack processing. Subsequently, the joint facies driven geostatistical inversion was conducted to invert for multiple realizations high-resolution lithofacies and elastic rock properties. Finally, porosity was co-simulated and later ranked to map important geological variations. Based on the rock physics analysis, a 4 facies classification scheme (Porous Calcite, Porous Dolomite, Tight Calcite-Dolomite and Anhydrite) was adopted and used as input in the joint facies-elastic inversion. Before the geostatistical inversion, a deterministic inversion was performed that helped in refining the horizon interpretation of the surfaces used as a framework for the inversion. In geostatistical inversion, results are guided by variograms, facies, prior probability density functions, wells, inversion grid and seismic data quality. At start of the joint inversion, the parameters for inversion are defined in an unconstrained fashion aiming to obtain unbiased parameters which are blind to well control. Finally, using elastic properties constrained at the well locations, the joint geostatistical inversion was run to obtain multiple realizations of P-impedance, S-impedance, density and lithofacies. The cross-correlation between seismic and inverted synthetics was high across the whole area for all the partial angle stacks, with the lowest cross-correlation observed in the far angle stack. Lithofacies and elastic properties were used to co-simulate for porosity. The porosity results were then ranked to provide the P10, P50 and P90 models to be used for reservoir property model building. This study is an example of stochastically generating geologically consistent reservoir properties through high-resolution seismically constrained inversion results at 1ms vertical sampling. Lithofacies and elastic properties were jointly inverted, and co-simulated porosity results provided insights into high-resolution reservoir heterogeneity analysis through the ranking of equiprobable multiple realizations.
The need to understand field-scale reservoir heterogeneity using seismic data requires implementing advanced solutions such as stochastic seismic inversion to go beyond the resolution of seismic data. Conventional seismic inversion techniques provide relatively low-resolution reservoir properties but do not provide quantitative estimates of the subsurface uncertainties. The objective of this study was to carry out a facies dependent geostatistical seismic inversion to generate multi-realization reservoir properties to improve the geological understanding of the two adjacent offshore fields in Abu Dhabi. An integrated approach of rock physics modelling and geostatistical inversion followed by porosity co-simulation was undertaken to characterize the spatially varying lithofacies and porosity of the complex carbonate reservoirs. Necessary checks to ensure highest quality data input included: 1) Rock physics modelling and shear sonic prediction 2) Invasion correction and production effect correction of elastic logs 3) Seismic feasibility analysis to define seismic facies and 4) Six angle stacks optimally defined to preserve AVO/AVA signature followed by AVO/AVA compliant post-stack processing. Subsequently, the joint facies driven geostatistical inversion was conducted to invert for multiple realizations high-resolution lithofacies and elastic rock properties. Finally, porosity was co-simulated and later ranked to map important geological variations. Based on the rock physics analysis, a 4 facies classification scheme (Porous Calcite, Porous Dolomite, Tight Calcite-Dolomite and Anhydrite) was adopted and used as input in the joint facies-elastic inversion. Before the geostatistical inversion, a deterministic inversion was performed that helped in refining the horizon interpretation of the surfaces used as a framework for the inversion. In geostatistical inversion, results are guided by variograms, facies, prior probability density functions, wells, inversion grid and seismic data quality. At start of the joint inversion, the parameters for inversion are defined in an unconstrained fashion aiming to obtain unbiased parameters which are blind to well control. Finally, using elastic properties constrained at the well locations, the joint geostatistical inversion was run to obtain multiple realizations of P-impedance, S-impedance, density and lithofacies. The cross-correlation between seismic and inverted synthetics was high across the whole area for all the partial angle stacks, with the lowest cross-correlation observed in the far angle stack. Lithofacies and elastic properties were used to co-simulate for porosity. The porosity results were then ranked to provide the P10, P50 and P90 models to be used for reservoir property model building. This study is an example of stochastically generating geologically consistent reservoir properties through high-resolution seismically constrained inversion results at 1ms vertical sampling. Lithofacies and elastic properties were jointly inverted, and co-simulated porosity results provided insights into high-resolution reservoir heterogeneity analysis through the ranking of equiprobable multiple realizations.
The study area comprises an oil play with numerous opportunities, identifying sandstone sequences with proven potential. The main sequence was deposited in the Upper Miocene within a transitional environment (external neritic), resulting in the formation of bars in deltaic facies and channels, which represents an excellent quality and lateral extension of the storage rock, but also complexity due to internal variability. The trap is structural with closure against faults, formed in an extensive tectonic regime giving rise to normal faulting and increasing the degree of complexity for the characterization of the reservoirs. Seismic data have accurate information about these characteristics, however, it is insufficient to solve the variability in the vertical scale, so incorporating all the information obtained in the wells through seismic inversion is essential when characterizing highly heterogeneous reservoirs with thin thickness. Furthermore, the geostatistical inversion combines Bayesian inference with a sampling algorithm called Markov Chain Monte Carlo (MCMC) that allows incorporating all the information from well logs, geological information, geostatistical parameters, and seismic data, generating models that honor the input data (Hameed et al., 2011). Additionally, the method provides a solution to the problem of non-uniqueness of the results, based on a statistical distribution of the multiple realizations derived from the initial model. This work proposes a flow that integrates quantitative analysis, establishing a direct link between seismic measurements and well logs, which additionally, when combined with non-linear techniques such as geostatistical seismic inversion, can minimize the differences in scales, obtaining better models, more predictive and with quantification of uncertainty. The static workflow used consists of 6 main components: Pre-stacked gather conditioning, curve modeling by rock physics (Vp, Vs and Rho), geostatistical seismic inversion (impedance P, Vp/Vs ratio, density), determination of facies cubes (oil-sand, brine-sand and shale) and petrophysical properties (Vcl, Phie, permeability) using a robust algorithm combining Bayesian inference and Markov Chain Monte Carlo (MCMC), quantification of uncertainty and volumetric estimation by ranking multiple realizations (P10, P50, P90) and transfer to a geological mesh (upscaling) ready for numerical simulation without the use of typical extrapolation algorithms such as kriging or Sequential Gaussian Simulation (SGS), managing to minimize the scale differences, obtaining better models, more predictive and capable of estimating uncertainty. With the results obtained, redefined geo-bodies were extracted, already discretizing the sandstones with good rock quality from the sandstones with good rock quality and bearing hydrocarbons to have greater precision in the development of these fields. Subsequently, the dynamic information was coupled to analyze the existing Pressure Transient Analyses (PTA) that have identified pseudo-steady state and the Rate Transient Analyses (RTA) to numerically model the response, checking the volumes obtained previously. Additionally, a benchmarking was considered with more than 590 oil producing fields in siliciclastics worldwide, considering the main properties of the fluid, porosity, facies and depositional environments and drive mechanisms, thus identifying new development opportunities with less uncertainty.
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