ALBERT

Description: Commercial gaseous hydrocarbon has been established from multilayered reservoirs within the Bhuvanagiri Formation in the Ariyalur-Pondicherry subbasin, but sustained production is obtained from only a few wells of the Bhuvanagiri Field. This has necessitated developing an integrated depositional model dovetailing distribution of favorable reservoir areas of the Bhuvanagiri Formation within the subbasin. Root-mean-square amplitude attributes and spectral decomposition attributes, along with RGB blending of spectral slices at different frequencies, have revealed a conspicuously northeast-southwest-trending channel within the Bhuvanagiri Formation. From well, sedimentological, and biostratigraphic data analysis, a deepwater turbidity channel model for the Bhuvanagiri Formation has been postulated. Deciphering the facies distribution pattern vertically and laterally within the turbidity channel is often complex and challenging. Integrated analysis of available laboratory data, petrographic, and scanning electron microscopy studies indicate poor porosity and permeability because of clay coating on grains, occurrence of authigenic clay as pore fill, cementation, and other diagenetic changes that have made reservoir characterization increasingly challenging. Four major lithofacies assemblages have been identified: basal lags, slumps and debris flows, arenaceous coarse-grained stacked channels, and fine-grained channel levee with characteristic log and seismic responses. To characterize the lithofacies, various crossplots have been generated by using processed logs to derive interrelationships between reservoir facies and log impedance. A model-based inversion has been attempted, which resulted in fairly satisfactory output with likely discrimination of reservoir and nonreservoir in an unexplored area within the field. The outcome would facilitate further exploration and delineation activities within the Bhuvanagiri Formation in the Ariyalur-Pondicherry subbasin.

Description: Digital rocks are 3D image-based representations of pore-scale geometries that reside in virtual laboratories. High-resolution 3D images that capture microstructural details of the real rock are used to build a digital rock. The digital rock, which is a data-driven model, is used to simulate physical processes such as fluid flow, heat flow, electricity, and elastic deformation through basic laws of physics and numerical simulations. Unconventional reservoirs are chemically heterogeneous where the rock matrix is composed of inorganic minerals, and hydrocarbons are held in the pores of thermally matured organic matter, all of which vary spatially at the nanoscale. This nanoscale heterogeneity poses challenges in measuring the petrophysical properties of source rocks and interpreting the data with reference to the changing rock structure. Focused ion beam scanning electron microscopy is a powerful 3D imaging technique used to study source rock structure where significant micro- and nanoscale heterogeneity exists. Compared to conventional rocks, the imaging resolution required to image source rocks is much higher due to the nanoscale pores, while the field of view becomes smaller. Moreover, pore connectivity and resulting permeability are extremely low, making flow property computations much more challenging than in conventional rocks. Elastic properties of source rocks are significantly more anisotropic than those of conventional reservoirs. However, one advantage of unconventional rocks is that the soft organic matter can be captured at the same imaging resolution as the stiff inorganic matrix, making digital elasticity computations feasible. Physical measurement of kerogen elastic properties is difficult because of the tiny sample size. Digital rock physics provides a unique and powerful tool in the elastic characterization of kerogen.

Description: In simple terms, rock physics provides the much-needed link between measurable elastic properties of rocks and their intrinsic properties. This enables us to connect seismic data, well logs, and laboratory measurements to minerology, porosity, permeability, fluid saturations, and stress. Rock-physics relationships/models are used to understand seismic signatures in terms of reservoir properties that help in exploration risk mitigation. Traditionally, rock physics has played an irreplaceable role in amplitude variation with offset (AVO) modeling and inversion, 3D/4D close-the-loop studies, and seismic time-lapse analysis and interpretation. Today, rock-physics research and application have influenced a much wider space that spans digital rock physics, microseismic, and distributed acoustic sensing (DAS) data analysis. In this special section, we have included papers that cover much of these advanced methods, providing us with a better understanding of subsurface elastic and transport properties, thereby reducing bias and uncertainties in quantitative interpretation.

Description: We have developed a new approach to perform Bayesian linearized amplitude-variation-with-offset (AVO) inversion in the depth domain using nonstationary wavelets. Bayesian linearized AVO inversion, a hybrid approach combining physics-based models with statistical learning, has gained immense popularity because of its superior computational speed and ability to estimate uncertainties in inverted model parameters. Bayesian linearized AVO inversion is performed on time-domain seismic data; therefore, depth-imaged seismic cannot be inverted directly using this method and would require depth-to-time conversion before AVO inversion can be done. Subsequently, time-to-depth conversion of the inverted volumes would be required for reservoir modeling and well placement. Domain conversions introduce additional uncertainty in geophysical workflows. In conventional AVO inversion, the seismic wavelet is assumed to be stationary, and this assumption leads to a restriction in the length of the time window over which the inversion can be performed. Therefore, AVO inversion is usually restricted to a narrow time window around the target of interest, and if multiple targets are present at different depths, multiple inversions must be run on the same volume. Depth-domain amplitude inversion is a recent development and has been previously presented in an iterative formulation. Implementing linearized Bayesian inversion directly in the depth domain using nonstationary wavelets is a convenient new approach that takes advantage of superior computational speed and uncertainty quantification without compromising the accurate spatial location that depth imaging provides. Combining these two ideas creates a novel, unique, and powerful seismic inversion technique that can be useful for quantitative interpretation and reservoir characterization.