Assisted Seismic History Matching in Different Domains: What Seismic Data Should We Compare?

Abstract

Time-lapse (4D) seismic data can be integrated into history matching by comparing predicted and observed data in various domains. These include the time domain (time traces), seismic attributes, or petro-elastic properties such as acoustic impedance. Each domain requires different modelling methods and assumptions as well as data handling workflows. The aim of this work is to investigate the degree to which the choice of domain influences theoutcome of history matching on the choice of best model and associated uncertainties. Another aspect of history matching is that long simulations often pose an obstacle for an automatic approach. In this study we use appropriately upscaled models manageable in the automatic history matching loop.We apply manual and assisted seismic history matching to the Schiehallion field. In the assisted approach, the optimization loop is driven by a stochastic algorithm, while the manual workflow is based on a qualitative comparison of 4D seismic maps. By upscaling we obtained an order of magnitude gain in performance. Accurate upscaling was ensured by thorough volume and transmissibility calculation within regions. The parameterisation of the problem is based on a pattern of seismically derived geobodies with specified transmissibility multipliers between the regions. Seismic predictions are made through petroelastic modelling, 1D convolution, coloured inversion and calculation of different attributes.We were able to achieve a reasonable match of production and 4D seismic data using coarse scale models in manual and assisted approaches. We observed that the misfit surfaces are different when working in the various seismic domains considered. Use of equivalent domains for observed and predicted data was found to give a more unique misfit response and better result.Accurate comparison of predicted and observed 4D seismic data in different domains is necessary for tackling nonuniqueness of the inverse problem and hence reducing the uncertainty of field development predictions.

Using Crosswell Data To Enhance History Matching

“…The petro-elastic model uses pressure, saturation and static reservoir properties to calculate density and velocity for each grid cell (Sagitov et al 2012). The most used approaches to generate seismic data are direct methods, which approximate the geological model on a numerical mesh.…”

Estimation of reservoir fluid saturation from seismic data: amplitude analysis and impedance inversion as a function of noise

“…Thus, from an engineering perspective, data integration on this domain would be the first choice. Then information can be directly applied without any domain conversion or issues associated with global objective functions involving production data and maps derived from seismic data (Sagitov and Stephen, 2012). However from a geophysics perspective, this domain is the most complex as it requires two inversions that involve compounding errors and non-uniqueness issues Yet the ability to distinguish between pressure and saturation effects relies on the 4D amplitude versus offset (AVO) response of the reservoir (Tura and Lumley, 2000;Landrø et al, 2003), and uncertainties in the rock physics models remain (Johnston, 2013).…”