Integration of 3D Seismic Data in Reservoir Modeling and Assessing Uncertainty in Lithology Distributions in The Nuayyim Field of Central Saudi Arabia
Authors: Al-Otaibi Ahmed M. 
Published: December 2007

This paper presents a newly developed technique to integrate the seismic attributes that represent interval average reservoir property as a ‘soft constraint’ with well log data into a full 3D reservoir property model. This geostatistical technique is called ‘3D Gaussian Sequential Simulation with block kriging (3D GSS with Block Kriging)’. The technique is applied to the Nuayyim Field in central Saudi Arabia where the main reservoir in the Unayzah zone-A sand.

The average interval seismic impedance map is extracted from the inverted 3-D seismic data and is related to lithology and reservoir porosity. Using the correlation coefficient between seismic impedance and Vshale and porosity, two ‘soft constraint’ rws for the Unayzah zone-A reservoir are produced to build the lithology and porosity 3D reservoir models from the impedance maps.

Average interval seismic impedance is found to correlate very well with average porosity and Vshale of the reservoir. Given the sparse well control and dense seismic data of the Nuayyim Field, it is critical to incorporate the ‘soft’ porosity and lithology estimates from seismic data with the hard well data into a consistent reservoir model.

Geostatistical simulation of the reservoir yields estimates of the probability of encountering reservoir quality sand to build reservoir quality model by generating ten equiprobable realizations of the lithology model. Using the probability cube as a multiplier, a probabilistic net sand map is produced. The uncertainty assessment of the multiple 3D lithology realizations was evaluated by predicting the reservoir quality of some recently drilled wells. The results indicate that the method is robust for this field.

Introduction

The main reservoir in the Nuayyim Field of central Saudi Arabia is the Unayzah zone-A sand. The lithofacies and porosity distribution in this reservoir are heterogeneous with thick siltstone interbedded within the reservoir zone in the south. The siltstone pinches out towards the north. The depositional environment of the reservoir sand was interpreted as eolian sand (W. King et al 1994). This new field has sparse well control, but is covered by a 3D seismic survey. 3D seismic data of about 200 square kilometers coverage and 625 square meters bin size was incorporated to guide the interpolation of well and petrophysical data to produce probabilistic lithology and reservoir porosity models. The average thickness of the Unayzah zone-A reservoir is about 100 feet with an average net pay of 40 feet.

Inversion of the seismic volume involves data preparation, wavelet processing to zero phase data and enhancement of the seismic signal, well data calibration, and interpretation. The seismic data was optimally enhanced through dephasing and applying FX-Y decon using ten drilled wells prior to being inverted to produce acoustic impedance and velocity cubes.

Interval average seismic impedance of the Unayzah zone. A reservoir was incorporated with interval average reservoir porosity and lithology. Using the correlation coefficients sand/silt realization and reservoir porosity distribution models were generated. These simulation models are more realistically imaging the heterogeneity of the Unayzah zone-A reservoir than using the well control alone. Fig. 1 shows ten scattered wells used in this study.

Methodology

This study involves four main stages:Seismic wavelet processing,seismic inversion,seismically-derived structural framework andgeostatistical modeling.

The flow chart in Fig. 2 shows the procedure and methodology of integrating seismic and well log data to generate the model cubes and maps.

The seismic volume of the Nuayyim Field was processed in order to zero phase the data for stratigraphic analysis.

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