A substantial proportion of proven oil and gas reserves of the world is contained in the carbonate reservoir. It is estimated that about 60% of the world’s oil and 40% of gas reserves are confined in carbonate reservoirs. Exploration and development of hydrocarbons in carbonate reservoirs are much more challenging due to poor seismic imaging and reservoir heterogeneity caused by diagenetic changes. Evaluation of carbonate reservoirs has been a high priority for researchers and geoscientists working in the petroleum industry mainly due to the challenges presented by these highly heterogeneous reservoir rocks. It is essential for geoscientists, petrophysicists, and engineers to work together from initial phases of exploration and delineation of the pool through mature stages of production, to extract as much information as possible to produce maximum hydrocarbons from the field for the commercial viability of the project. In the absence of the well-log data, the properties are inferred from the inversion of seismic data alone. In oil and gas exploration and production industries, seismic inversion is proven as a tool for tracing the subsurface reservoir facies and their fluid contents. In this paper, seismic inversion demonstrates the understanding of lithology and includes the full band of frequency in our initial model to incorporate the detailed study about the basin for prospect evaluation. 3D seismic data along with the geological & petrophysical information and electrologs acquired from drilled wells are used for interpretation and inversion of seismic data to understand the reservoir geometry and facies variation including the distribution of intervening tight layers within the Miocene carbonate reservoir in the study area of Central Luconia. The out-come of the seismic post-stack inversion technique shows a better subsurface lithofacies and fluid distribution for delineation and detailed study of the reservoir.
Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.
3D-seismic data have increasingly shifted seismic interpretation work from a horizons-based to a volume-based focus over the past decade. The size of the identification and mapping work has therefore become difficult and requires faster and better tools. Faults, for instance, are one of the most significant features of subsurface geology interpreted from seismic data. Detailed fault interpretation is very important in reservoir characterization and modeling. The conventional manual fault picking is a time-consuming and inefficient process. It becomes more challenging and error-prone when dealing with poor quality seismic data under gas chimneys. Several seismic attributes are available for faults and discontinuity detection and are applied with varying degrees of success. We present a hybrid workflow that combines a semblance-based fault likelihood attribute with a conventional ant-tracking attribute. This innovative workflow generates optimized discontinuity volumes for fault detection and automatic extraction. The data optimization and conditioning processes are applied to suppress random and coherent noise first, and then a combination of seismic attributes is generated and co-rendered to enhance the discontinuities. The result is the volume with razor sharp discontinuities which are tracked and extracted automatically. Contrary to several available fault tracking techniques that use local seismic continuity like coherency attributes, our hybrid method is based on directed semblance, which incorporates aspects of Dave Hale’s superior fault-oriented semblance algorithm. The methodology is applied on a complex faulted reservoir interval under gas chimneys in a Malaysian basin, yet the results were promising. Despite the poor data quality, the methodology led to detailed discontinuity information with several major and minor faults extracted automatically. This hybrid approach not only improved the fault tracking accuracy but also significantly reduced the fault interpretation time and associated uncertainty. It is equally helpful in detecting any seismic objects like fracture, chimneys, and stratigraphic features.
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