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.

Exploring hydrocarbon in structural-stratigraphical traps is challenging due to the high lateral variation of lithofluid facies. In addition, reservoir characterization is getting more obscure if the reservoir layers are thin and below the seismic vertical resolution. Our objectives are to reduce the uncertainty of reserve estimation and to predict hydrocarbon distribution more accurately in such reservoir layers by proposing a new workflow that works better than the conventional one. The approach was performed by integrating petroelastic modeling, stochastic elastic seismic inversion, and Bayesian probability classification in the upper reservoir layer of Group E in the Northern Malay Basin. A robust petroelastic model was initially built to obtain more obvious separation of different lithofluid classes in elastic properties crossplot, that is acoustic impedance versus [Formula: see text] ratio. To achieve reliable distribution of elastic properties per identified lithofluid class, a Monte Carlo simulation was then run and the posterior probability of all classes was computed using Bayesian classification, followed by confusion matrix assessment. Stochastic elastic seismic inversion was carried out on conditioned seismic data to predict elastic properties away from the wells. Using all elastic properties realizations, ranking was calculated and uncertainty was quantified at the blind well location. The most probable scenario is the realization that has a much closer probability to the measured criterion value at the blind well. The computed posterior probability of hydrocarbon-bearing sand was applied on the selected stochastic realization (acoustic impedance and [Formula: see text] volumes) according to the ranking result. Finally, the hydrocarbon distribution probability map was generated and validated with lithofluid facies information of four distributed wells. Such a comparison authenticated the hydrocarbon prediction particularly at the blind well location.

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Porosity and facies are two main properties of rock which control the reservoir quality and have significant role in petroleum exploration and production. Well and seismic data are the most prevalent information for reservoir characterization. Well information such as logs prepare adequate vertical resolution but leave a large distance between the wells. In comparison, three-dimensional seismic data can prepare more detailed reservoir characterization in the inter-well space. Generally, seismic data are an efficient tool for identification of reservoir structure; however, such data usable in reservoir characterization. Therefore, these two types of information were incorporated in order to obtain reservoir properties including porosity and facies in the study area. Using Multimin algorithm, petrophysical analysis was carried out for estimation of reservoir porosity. Then, an accurate post-stack inversion was accomplished to obtain the acoustic impedance volume. The results showed that the Ghar sandstone is characterized by a lower acoustic impedance compared to the high acoustic impedance Asmari Formation. Because of a relationship between acoustic impedance and reservoir properties (i.e., porosity), porosity cube calculation was performed by artificial neural network method which is a popular approach for parameter estimation in petroleum exploration. The consequences showed a good agreement between log based and seismic inversion-derived porosity. The inversion results and well logs cross-plots analyses illustrated that the Ghar member considered as a high quality zone with porosity 22 to 32 percent and the Asmari dolomite shows a low quality interval characters with porosity 1 to 6 percent. The findings of this study can help for better understanding of reservoir quality (especially porous Ghar member delineation) by lithology discrimination in the analysis of identification reservoirs and finding productive well location in Hendijan field.

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