Seismic reservoir mapping from 3-D AVO in a North Sea turbidite system

We present a methodology for estimating uncertainties and mapping probabili ties of occurrence of different lithofacies and pore fluids from seismic am plitudes, and apply it to a North Sea turbidite system. The methodology com bines well log facies analysis, statistical rock physics, and prestack seis mic inversion. The probability maps can be used as input data in exploratio n risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic-scale sedimentary units which we refer to as seism ic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick-bedded clean sa nds with water, plane laminated thick-bedded sands with oil, and pure shale s have very similar acoustic impedance distributions. However, the V-p/V-s ratio helps resolve these ambiguities. We establish a statistically representative training database by identifyin g seismic lithofacies from thin sections, cores, and well log data for a ty pe well. This procedure is guided by diagnostic rock physics modeling. Base d on the training data, we perform multivariate classification of data from other wells in the area. From the classification results, we can create cu mulative distribution functions of seismic properties for each facies. Pore fluid variations are accounted for by applying the Biot-Gassmann theory. Next, we conduct amplitude-variation-with-offset (AVO) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO respo nses related to the inherent natural variability of each seismic lithofacie s using a Monte Carlo technique. Based on the Monte Carlo simulation, we ge nerate bivariate probability density functions (pdfs) of zero-offset reflec tivity [R(0)] versus AVO gradient (G) for different facies combinations. By combining R(0) and G values estimated from 2-D and 3-D seismic data with t he bivariate pdfs estimated from well logs, we use both discriminant analys is and Bayesian classification to predict lithofacies and pore fluids from seismic amplitudes. The final results are spatial maps of the most likely f acies and pore fluids, and their occurrence probabilities. These maps show that the studied turbidite system is a point-sourced submarine fan in which thick-bedded clean sands are present in the feeder-channel and in the lobe channels, interbedded sands and shales in marginal areas of the system, an d shales outside the margins of the turbidite fan. Oil is most likely prese nt in the central lobe channel and in parts of the feeder channel.