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Unconfined compressive strength (UCS) is an important rock parameter required in the engineering design of structures built on top or within the interior of rock formations. In a site investigation project, UCS is typically obtained discretely (through point-to-point measurement) and interpolated. This method is less than optimal to resolve meter-scale UCS variations of heterogenous rock such as carbonate formations in which property changes occur within data spacing. We investigate the geotechnical application of multiattribute analysis based on near-surface reflection seismic data to probe rock formations for their strength attributes at meter-scale variability. Two Late Jurassic outcrops located in central Saudi Arabia serve as testing sites: the Hanifa Formation in Wadi Birk and the Jubaila Formation in Wadi Laban. The study uses core and 2D seismic profiles acquired in both sites, from which we constrain UCS, acoustic velocity, density, and gamma-ray values. A positive linear correlation between UCS and acoustic impedance along the core indicates that seismic attributes can be utilized as a method to laterally extrapolate the UCS away from the core location. Seismic colored inversion serves as input for neural network multiattribute analysis and is validated with a blind test. Results from data at both outcrop sites indicate a high degree of consistency with an absolute UCS error of approximately 5%. We also demonstrate the applicability of predicted UCS profiles to interpret mechanical stratigraphy and map lateral UCS heterogeneities. These findings provide a less expensive alternative to constrain UCS from limited core data on a field-scale site engineering project.

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High porosity-high permeability stromatoporoid/coral facies are important components of the Late Jurassic carbonate reservoirs in the Middle East. This facies exhibits sub-seismic depositional heterogeneities that subsurface models often overlook due to the limited interwell resolution of subsurface data. Understanding the effect of this facies on the 3D distribution of static reservoir properties and uncertainty in volumetric calculations of hydrocarbons in-place will improve estimates of the ultimate recovery and hence reservoir development decisions. A 3D high-fidelity outcrop-based geocellular depositional model that honors the spatial and petrophysical heterogeneity of the stromatoporoid/coral facies was constructed based on the Hanifa reservoir outcrop analog in central Saudi Arabia. The model was constructed from a 1.2 km × 1 km drone photogrammetry survey, measured sections (total length 150m) and spectral gamma-ray data, >200 thin sections, a 50 m-long core, a 19 km-long network of 2D and 3D Ground Penetrating Radar, and 600 m-long 2D seismic profiles. The facies model was populated with porosity and permeability equivalent to subsurface reservoir facies and utilized as the baseline petrophysical model for the comparison study. A set of pseudo wells at ~1 km spacing were simulated from the model capturing the model's 1D facies stacking and properties around the wellbore. The pseudo wells were utilized to stochastically build facies and static reservoir models scenarios to replicate the baseline model from limited well data. The volumetric calculation of each realization is compared with the baseline to investigate the range of volumetric uncertainty that would be introduced by the lateral distribution of stromatoporoid/coral facies. Early results show that depending upon the modeling methodology, the volumetric discrepancy between stochastic simulations and the deterministic outcrop baseline model is ~10-15%. Using a high-fidelity outcrop-based reservoir model, we have demonstrated the strong influence of 3D depositional heterogeneity of the stromatoporoid/coral facies on the uncertainty associated with hydrocarbon in-place volumes. We conclude that a static reservoir model can be significantly improved by using data-driven geological models that reflect the 3D heterogeneity of depositional facies.