Seismic amplitude variation with offset holds information on density and two elastic parameters: compressional and shear velocities (or impedances). We simultaneously invert multiple offset stacks to transform P-wave offset seismic reflection data to these parameters. Prior to the inversion, wavelets are estimated separately for each offset stack. This enables the inversion to compensate for offset-dependent phase, bandwidth and tuning and nmo stretch effects. The impedance volumes can be interpreted separately or combined to estimate other geophysical parameters which might optimally discriminate between facies. In this regard, we have found the Lame' parameters, Lambda and Mu particularly useful. From well log analysis we expect that reservoir sands have lower Lambda (incompressibility) and higher Mu (rigidity).

Study of key parameters of reservoir viz, porosity, water saturation, permeability and pore size distribution from well logging data is more complicated in carbonate reservoir due to geological heterogeneities than Clastic reservoir.The Magnolia field is located in GOM blocks GB 783 and 784 and produces from Plio-Pleistocene turbiditic sands that form a complex channel/levee sequence penetrated by 16 boreholes. The primary pays consist of two sands, each about 200 feet thick, separated by a 15 foot shale layer. The pays are divided into an eastern gas prone province and a western oil prone province. A reservoir flow simulation model is planned to optimize production from existing wells and to facilitate future field development. Construction of an accurate model is complicated by MDT pressure measurements which indicated compartmentalization below the resolution of conventional seismic analysis, and by overlap of the seismic attributes derived from producing reservoirs, wet sands, and shales.To mitigate these factors, geostatistical inversion was chosen to produce the rock property inputs for the flow simulation models. This approach allowed development of a rock properties model consistent with core data, log data, and geologic constraints as well as seismic information. It also allowed assessment of uncertainty through the generation of a statistically significant number of internally consistent alternate solutions (realizations). A Markov Chain Monte Carlo method was employed to integrate borehole and geologic information to produce acoustic impedance and lithology volumes which were then used to co-simulate porosity, permeability, p-wave velocity, and water saturation volumes. Multiple realizations of these products were reviewed, uncertainty was assessed, and a rock properties model was selected for conversion to a flow simulation modeling format. The entire process can be rerun relatively quickly to accommodate additional wells and improved seismic data or to match production history.

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A Bright spot prospect was identified using 2D and 3D seismic. Due to the isolation of this prospect from existing infrastructure it is critical to be able to predict the type of hydrocarbon likely to be present. The approach taken to quantify hydrocarbon type included the following steps:reprocess a 2D seismic line which connects the well control to the prospect,use the well to model the response of different pore fluids in the reservoir quality sands,perform incident angle dependant inversions of the 2D seismic,statistically quantify and compare the results from the prospect with the model results,use the fluid probability at the prospect in the project risk assessment. We know that normal incidence seismic data is the response to the acoustic impedance, AI, of the geologic layers. However, at non-zero incident angles, the seismic data is the response to the elastic impedance1, EI, of the geologic layers. Because of the inherent pitfalls of using amplitude variation with offset2, AVO, a new approach was taken to quantify the fluid at the prospect. First, a pre-stack time migration of a 2D seismic line was created followed by near and far angle stacks. AI and EI volumes are then generated for the respective angle stacks. This process is called angle dependent inversion, ADI. AI and EI values are extracted for each CDP within the prospect. Log-based fluid substitution3,4,5 models are created to establish a range of AI and EI values for reservoir quality sands. Each model is displayed as a probability distribution function, PDF and compared to the extracted ADI values. The results indicate that it is unlikely that the prospect sand is brine filled. The most probable hydrocarbon in the prospect reservoir is gas. Introduction A deepwater Gulf of Mexico prospect was generated in which high amplitude seismic events were used to define the areal extent of a potential reservoir. The location of the prospect is over 30 miles from the nearest host platform. It was assumed that a single subsea completion would be used to develop a discovery. However, it was quickly realized that given the estimated volume of the reservoir and the tie back distance a gas discovery would be very commercial while an oil discovery would be equivalent to a dry hole. Trend analysis of the surrounding fields was not conclusive in predicting the most likely hydrocarbon type at the prospect. Therefore, a study was conducted in which modeled acoustic and elastic impedance for varying reservoir fluids were compared to extracted values from near and far angle seismic inversions in an attempt to predict the fluid type in the prospect. Although the prospect was mapped using a 3D data set it was concluded that a single 2D seismic line through the prospect would be sufficient to make the fluid prediction. A recent-vintage 2D line was selected that went through the center of the prospect and within 500 feet of our primary well control (Well 1).

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