EAGE Workshop on Applications and Challenges of Rock Physics for Quantitative Geophysical Interpretation

The Unayzah sandstone in Central Saudi Arabia is a mature consolidated formation. The effective medium model of Raymer-Hunt-Gardner accurately predicts the effects of porosity, lithology and pore-fluid on the seismic response. Empirical results are calibrated to measured data at well log and seismic scale to confirm the validity of the model. This model-driven approach will be used to constrain the amplitude-versus-angle (AVA) inversion results for P-impedance and Vp/Vs ratio, which is then used to predict porous oil-saturated zones within the formation.

Quantitative seismic interpretation techniques, such as impedance inversion, use rock physics to infer some parameters from seismic about the subsurface such as porosity and pore fluids. The reliability of results obtained from these techniques depends greatly on the accuracy and consistency of the seismic wavelet used in the analysis. Accuracy of a seismic wavelet is its ability in reproducing, from a well-based reflectivity series, a synthetic seismic trace that matches a real trace at the well location. Consistency of a wavelet is a measure of its spatial invariability from well to well. Here, we test three different wavelet-extraction methods, namely, statistical (Bayesian), deterministic incorporating single-trace and well calibration, and hybrid that employs multi-well multi-trace calibration. We demonstrate that relying solely on the statistics of seismic data to extract wavelets with no regard to well information produces inferior results than when well data is used. Due to severe spatial inconsistency, statistically derived wavelets are deemed inappropriate for use in quantitative interpretation. Though both are suitable for quantitative interpretation, the hybrid method produces more robust wavelets than using only deterministic techniques since the former takes advantage of additional information not only from multiple wells but also from multiple traces around each well.

The application of a quantitative interpretation workflow is illustrated using examples from two projects in different shallow formations in the Canadian oil sands. In an area with complex reservoirs, complex fluid distributions and unconventional rock property behaviour, high drilling density provides the data necessary for robust custom rock-physics templates. High quality shallow seismic data completes the conditions required for unprecedented detail in reservoir characterization, enabling accurate predictions of both lithology and fluids. The incorporation of multi-component data shows how an under-utilized dimension of seismic data can contribute to the accuracy of the solution. Finally, comparison of actual vs predicted drilling results from a significant number of wells supports the legitimacy of the quantitative information derived from the integrated process.

The aim of this study is to highlight the application of petrophysical analysis followed by rock physics modelling on the well data of the Burgan field. In reservoir characterization, integration of well information such as petrophysical analysis, rock physics modelling with seismic data is very important for a good reservoir description. In this paper, we proposed the used of a consistent petrophysical analysis of the well data which produces water saturation, porosity, volume of clay and volume of calcite applied to an inclusion-based rock physics model to construct a synthesis of density, P and S velocity. An important advantage is that a consistent model of elastic properties have been built can be applied to quality control of the petrophysical analysis interpretation or to synthesize elastic log if needed for this field. The result of this work is very useful for further seismic reservoir characterization where P and S velocity are able to differentiate sand and shale in Burgan field.

To accurately study the factors that affect seismic velocity in carbonate rocks one should look at the texture, grain types, mineralogy, pore types and pore size of the rock as all these elements influence the seismic velocity of the rock. Compressional and shear wave velocities (at 1 MHz) of thirty-seven Arab-D rock samples were measured dry, and as functions of saturating fluid and confining pressure. The lithology, mineralogy, porosity, and pore type and size distribution of each sample were obtained using a combination of thin-section and scanning electron microscopy, helium porosimetry, and mercury intrusion porosimetry. The microporous nature of the skeletal and oolite grains appears to produce a softer material, which in turn increases the rock compressibility and decreases the expected seismic velocities of the samples.

Traditionally,the values of reservoir rock properties have been acquired from log data or direct measurement in a physical laboratory. Recent advances in imaging and image processing, together with improved availability of high performance computing, gave rise to digital techniques for investigating the properties of rock samples. These techniques are based on high-resolution imaging of the rock's pore space, segmentation of the images into pores and various minerals and simulation of the physical processes controlled by the desired rock properties. These techniques form the novel discipline of digital rock physics (DRP). The goal of the current work is to validate the results of DRP measurements of geophysical parameters by comparing them with the results obtained in traditional physical laboratories.

Porosity and fluid prediction of Minagish Oolite, Minagish Field of south west of Kuwait was carried out using geostatistical inversion approach. This field has several reservoirs and the oil is accumulated primarily in the Minagish Oolite reservoir of Lower Cretaceous age. This reservoir consists of thick porous carbonate containing oil in unsaturated condition. Geostatistical inversion using Bayesian inference combined with Markov Chain Monte Carlo (MCMC) sampling algorithm integrates the information of well logs, geological constraints, geostatistical parameters and seismic data. Multiple realizations of geostatistical inversion were generated to output highly detailed P-impedance and lithology of the reservoir, which were then used to co-simulate porosity and water saturation. The results of porosity and water saturation of this study are validated through recently drilled wells.

We performed rock physics modeling and simultaneous inversion of multiple seismic angle stacks with an objective to map the spatial extent of heavy oil bearing Jodhpur sands in Baghewala structure of Bikaner-Nagaur basin. We built a rock physics model from measured acoustic and shear information from offset wells and used the model to predict shear sonic data in four wells inside the area of study. We further used the inverted acoustic and shear impedances to predict seismic litho-facies and associated probabilities under a Bayesian framework. The reservoir property maps derived from inverted seismic data conform to the geological setting of the area.