Fifth EAGE Workshop on Rock Physics

Unconventional play exploration in carbonate-rich source rocks impose a technical challenge to unfold rock properties from seismic in very tight, fast-velocity, high-pressure source rock intervals such as the Shilaif Formation and the Diyab group supporting hydrocarbon generation across the Abu Dhabi emirate in the UAE. A set of unconventional rock physics models using regional wells have proven to be successful highlighting sweet spots from seismic based on significant changes in rock and pore compressibility as a function of total organic carbon (i.e., kerogen, bitumen, and hydrocarbons) in these carbonate-rich source rocks.

The co-injecting and commingled production mode is common for offshore oil field development in China. It caused the lack of separate layer production testing data. The lack of the basic seismic data for the origin reservoir and the log data for different development phased increased difficulties of the time-lapsed seismic reservoir monitoring and the prediction of the by-passed oil distribution. Three topics were discussed in this paper including the water flooding model for different production mode, the time-lapsed seismic logging curve dynamic reconfiguration method (Darcy Law) and the time-lapse seismic quantitative interpretation technology. The logging dynamic reconfiguration for the whole life of oil fields development and the quantitative forecasting for the by-passed oil distribution of the offshore oil field should be under the economic scale. The application of these technologies have achieved good effect in S oil field.

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Extraction of hydrocarbon from shale gas reservoirs always presents challenges due to their extremely low permeability and hence production depending totally on natural and induced fractures. Geomechanics is of vital importance for shale gas prospectivity evaluation in terms of brittleness and anisotropy. The research objective is to evaluate the prospectivity of Qusaiba Shale by delineating brittle zones/layers. This study also quantifies the anisotropy leading to accurate determination of hydraulic fractures orientation. The log data, including sonic (P and S-waves), density, and mineralogy logs for about 950 feet thick shale interval, were used to calculate the brittleness index. Brittleness index was determined based on two approaches: elastic parameters and mineralogy. Thomsen parameters and Schoenberg model were used to investigate the anisotropy. Both approaches for brittleness estimation revealed that Qusaiba Shale is composed of alternating brittle and ductile layers. The comparison of results revealed that the quartz rich layers exhibit high Young’s Modulus and low Poisson’s ratio in terms of high brittleness index. Anisotropy analysis revealed high degree of P and S wave anisotropy. The study emphasizes the importance of the role of brittleness and anisotropy analyses in prospectivity evaluation of Qusaiba Shale and assist in optimum designing of completion stages.

Seismic characterization of siliciclastic reservoirs is very sensitive to their clay type and content as well as their distribution type. Clay type and content of a reservoir and how they are distributed can affect reservoir quality and petrophysical properties such as porosity and permeability and can even change the elastic properties such as velocities. This means that reservoir properties can be overlooked by using seismic if their shale content (clay types and their distribution type) is not understood and interpreted properly. This study proposes an integrated approach based on integration between petrophysics, rock physics and seismic in order to characterize various shale distributions in an offshore oilfield. We used Thomas and Strieber model (1975) as the rock physics template to differentiate between various shale distributions at the given well locations. Then, the results are mapped onto the seismic pre-stack inversion results to extend the recognized shale distributions at well locations into the entire reservoir. Our results explain the reason for different observed reservoir quality between two zones (within the reservoir) with the same clay content.

This rock-physics investigation aims to understand the elastic response of shales and poorly-consolidated sands with special attention to the variations in textural expression including mineralogy, preferred clay alignment, and porosity; as well as non-textural parameters: stress state and fluid saturation. This research explores such elastic response to produce a deeper understanding in the frequency-related behavior through a modeling approach. Within this framework, two specific aspects are investigated: (i) the elastic modulus as a function of pressure variation, as well as silt- and fluid- inclusion in an anisotropic clay medium, that will consequently produce different clay preferred alignment and distinct anisotropic behaviour at different frequency regime; (ii) the elastic modulus as function of porosity and water-saturation in isotropic granular media, that also addresses frequency-velocity dispersion by acknowledging the microscopic component.

Understanding how the elastic wave velocities change with pressure is essential for better interpretation and modelling of time lapse seismic, as well as for geomechanics related applications. Several studies have attempted to develop empirical relations to predict velocity as function of pressure based on the measured velocities at low pressure or ambient conditions. Nevertheless, the developed relations are based on empirically determined parameters where the link to petrophysical properties and microstructure of the rock is not clear. In this study, we present an artificial neural network (ANN) model to predict compressional velocity as function of pressure in carbonate rocks. The data set consists of petrophysical properties and compressional velocities measured as function of confining pressure for 145 reservoir carbonate samples. We investigated the significance of various rock properties to select the appropriate input parameters (i.e., features) that impact the velocity-pressure relationship. In this work, we considered five different parameters: porosity, permeability, pore stiffness, dominant pore type (inter versus intra particle porosity), and pressure. Based on the results, the prediction of the ANN model outperforms that of the traditional regression approach. The ANN approach could predict the velocities with a correlation coefficient of 0.99 and average RMSE of 70 m/s.

This paper describes a methodological case study in which a data-driven rock physics basin scale analysis served as input for carrying out a probabilistic litho-fluid facies classification of AVO attributes, from which a hydrocarbon probability volume was obtained. This output along with other ancillary information aided the successful placement of both explorative and appraisal wells in a deep water field. The workflow was composed of two main steps, the first one to be carried out at well data scale, the second at seismic data scale. In the first one, a training set of AVO Intercept (I) and Gradient (G) attributes were computed from data-driven elastic property analysis. In the second, the well-based model was applied to seismic data. The final deliverable was a hydrocarbon probability volume useful for a constrained interpretation of conventional seismic information into expected litho-fluid related seismic responses. It has been proven on a real case scenario, where excellent match between hydrocarbon occurrence and well results were obtained at both at prospect de-risking and appraisal stage.

Numerically simulated moduli for micro-CT images of rock microstructure are usually considerably higher than those inferred from laboratory measured ultrasonic velocities. Here, we analyse a set of images of Bentheimer sandstone samples accompanied by stress-depended ultrasonic measurements. First, we propose a new segmentation workflow that could better segment dispersed clay out of sandstone matrix based on both pixel intensity and morphology of clay particles. Then, with the moduli computed for scans with different resolution we fit a linear relationship to extrapolate our estimates to the infinite resolution. Our calculation clearly shows that imperfect image segmentation algorithms and image resolution may be considered as the main reasons for the systematic bias between computed and measured moduli. Through the above adjustment, our digital rock physics workflow not only provides a robust calibration for bulk modulus-porosity relationship, but also the bulk modulus-clay relationship for sandstones with dispersed clay.

Predicting the properties of organic-rich mudrocks is essential for the efficient exploitation of these unconventional resources. Quantitative seismic interpretation can be used to estimate physical properties of rocks and their associated uncertainties if well-calibrated rock physics model is constructed. In this study, rock physics models are combined with basin and petroleum system and geochemical models to construct an organic-rich rock physics template that incorporates the thermal maturation of kerogen. The properties of kerogen are modeled as function of thermal maturation incorporating creation of organic porosity and densification of the remaining solid kerogen. The effective elastic moduli, with pores in both the kerogen and the inorganic matrix are modeled using differential effective medium model. Gassmann’s fluid substitution equation is used to fill pores with hydrocarbons. The model is applied on the Shublik Formation in North Slope, Alaska. Results show a good correlation with compressional and shear impedances measured from well logs. The Shublik Formation rock physics template is used to predict the physical properties of the rock using P-impedance, S-impedance, and density cubes inverted from seismic angle gathers. The use of the Approximate Bayesian Calculation allows for sampling multiple realizations of these properties.