Schlumberger

Quantitative Analysis of Seismic Uncertainty

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Developments in quantitative analysis techniques reduce event uncertainty in oil and gas reservoirs

Schlumberger Geosolutions has developed an innovative quantitative analysis of seismic uncertainty. This provides a quantitative measurement of the uncertainty associated with the structural framework and positioning from seismic depth imaging. Even with our best efforts to combine all available data, there remains ambiguity in our models given the inherent non-uniqueness of the seismic experiment: disparate equiprobable models can match the observed seismic data and this ambiguity grows rapidly as we move away from known control points such as well logs. The resulting uncertainty in the true positions of events in the image can ultimately lead to exploration risk (e.g., trap failure), drilling hazards, and volumetric surprises. This methodology delivers a unique solution to quantify uncertainty, thereby reducing the risk across the board.

Geosolutions quantitative analysis methodology

At the outset an initial anisotropy model is calibrated with available well data and interpolated inter-well using existing geological structural interpretation. This is followed by an iterative multiscale non-linear tomography involving migrating the data, picking common-image-point (CIP) gathers and dips, ray tracing, and solving a huge, but sparse system of linear equations. Quantitative analysis is applied after the last non-linear tomography iteration when the solution has converged and driven the flatness of the gathers to acceptable levels. 

The resulting sets of target horizons are statistically analyzed and structural uncertainty estimates derived. If well data are available, we select the models that tie wells based on the least-squares criterion. To accommodate and test the dependence of the constraints provided by prior information, the above analysis is performed several times with varying geologic scenarios. Based on this sensitivity analysis, structural uncertainty can be calibrated by available mistie information.

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Quantify Uncertainty For Risk Reduction

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