Integrated History-Matching of Production and 4D Seismic Data

Abstract

fax 01-972-952-9435. AbstractThe reservoir characterisation methods are still improving the integration of all available data for more accurate predictions, but the history-matching confrontation between observed and simulated data as predicted by the model remains the crucial test. Time-lapse 3D seismic data now provides a new type of information on the dynamics of fluids in the reservoir, relating variations of seismic signal to movement and/or depressurisation of hydrocarbons. This powerful reservoir-monitoring tool can be fully exploited when it is used to update the reservoir model in an integrated matching loop of all history data -4D data, spatially distributed over the whole reservoir, jointly with time series of well observations. In a classical gradient-based minimisation of an objective function, measuring the mismatch between observed and simulated data, we add a seismic term to the well term. This contribution, defined in terms of elastic parameter variations within the reservoir, comes from an inversion of the seismic signals. On the other hand the fluid flow simulator is coupled with a petro-elastic module to convert the simulated saturation/pressure and static rock properties into simulated elastic properties. The optimiser uses gradients of both terms to minimise the global mismatch, updating the reservoir properties. In this paper, we justify our approach using elastic parameters, describe the relationships to derive elastic properties from reservoir properties, and present the integrated iterative loop matching both kinds of dynamic data (production and 4D seismic), based on sensitivity analysis to select the most efficient parameters and applied to the synthetic PunqS3 case 6,1 .

Robust scheme for inversion of seismic and production data for reservoir facies modeling

“…Walker and Lane (2007) present a case study that includes time-lapse seismic data as a part of the production history matching process, and show how the use of seismic monitoring can improve reservoir prediction. Integration of disparate data has been suggested in a number of publications (Huang et al, 1998, Gosselin et al, 2001, Waggoner et al, 2002and Aanonsen et al, 2003. Though these clearly identify data integration as a means for better constraining the problem, they do not provide a clear and precise optimization methodology.…”

Reservoir description using a binary level set model

“…From the last section, we see that every piecewise constant function can be represented as in (15) under the requirement that the level set functions satisfy (16). In order to find a piecewise constant function, we just need to find the corresponding c j values and the level set functions φ i .…”

“…A quantitatively incorporation of these data has been discussed by Gosselin et al [16]. Aanonsen et al [1,2] have taken this approach further and considered the effect of using proper weights for the seismic data in the objective function.…”

Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models

“…As soon as 4D seismic is also considered, advanced workflows must be designed to incorporate both production and seismic data in a consistent way. The improvements in the quantitative processing of 4D seismic motivated many works about this topic (Landa and Horne, 1997;Pianelo et al, 2000;Gosselin et al, 2001;Waggoner et al, 2002;Stephen and McBeth, 2006;Roggero et al, 2007;. However, there is still no definite matching methodology and several issues remain to be addressed.…”

“…However, there is still no definite matching methodology and several issues remain to be addressed. 4D seismic can be accounted for under various forms: amplitudes (Arenas et al, 2001) or acoustic impedances derived from amplitudes in a preliminary step , impedances at base and monitor times or impedance differences between base and monitor times, impedances given in depth (Gosselin et al, 2001) or time (Fornel et al, 2007; domain. All of these may significantly impact the number of data, but also their informative content.…”