Porosity and permeability estimation by integration of production and time-lapse near and far offset seismic data

Abstract

This study presents a method based on the Gauss-Newton optimization technique for continuous reservoir model updating with respect to production history and time-lapse seismic data in the form of zero offset amplitudes and amplitude versus offset (AVO) gradients. The main objective of the study is to test the feasibility of using these integrated data as input to reservoir parameter estimation problems. Using only production data or zero offset time-lapse seismic amplitudes as observation data in the parameter estimation process cannot properly limit the solution space. The emphasis of this work is to use the integrated data combined with empirical knowledge about rock types from laboratory measurements, to further constrain the inversion process. The algorithm written for this study consists of three parts: the reservoir simulator, the rock physics petro-elastic model and the optimization algorithm. The Gauss-Newton inversion is tested at a 2D semi-synthetic model inspired by real field data from offshore Norway. The algorithm reduces the misfit between the observed and simulated data which make it possible to estimate porosity and permeability distributions. The Gauss-Newton optimization technique is an efficient parameter estimation technique. However, the numerical estimation of the gradient is time consuming, and it can be prohibitive for practical applications. This method is suitable for distributed computing which considerably reduces the total optimization time. The amount of reduction depends mainly on the number of available processors.

The porosity of saturated shallow sediments from seismic compressional and shear wave velocities

“…Seismic velocities have been used in previous research projects to address such problems (e.g. Dadashpour et al, 2009;Bozcu et al, 2007;Soupios et al, 2006;Tezcan et al, 2006;Soupios et al, 2005;Karastathis et al, 2002;Al-Hamoud and Tanash, 2000;Savvaidis et al, 1999;Dutta, 1984). Many researchers have investigated empirical or analytical relationships between in-situ experiments and porosity.…”

Reservoir characterization based upon the onset of time-lapse amplitude changes

“…Estimating reservoir properties such as permeability, based on onset times, contrasts with previous efforts that relied upon timelapse amplitude, traveltime, and waveform changes (Huang et al, 1998;Vasco et al, 2004;Vasco, 2004a;Dong and Oliver, 2005;MacBeth and Al-Maskeri, 2006;Dadashpour et al, 2008Dadashpour et al, , 2009Feng and Mannseth, 2010;Rey et al, 2012). Here, we demonstrate that onset times, extracted from seismic time-lapse reflection data recorded in a few surveys, can provide a reliable basis for reservoir characterization.…”

Streamline-Based Time Lapse Seismic Data Integration Incorporating Pressure and Saturation Effects

“…where HM is the bulk modulus at critical porosity (Dadashpour 2009(Dadashpour , 2010. Here, eff is the effective pressure, which is the difference between the lithostatic pressure ext and the hydrostatic pressure (Christensen and Wang, 1985), ma is the bulk modulus of the matrix, and n is the coordination number.…”

“…Kjelstadli et al (2005) used the convolution of a wavelet with the seismic reflection coefficients, creating a set of synthetic amplitude traces and generate maps of the summation of negative amplitude (SNA) as the observation data and calibrated zonal heterogeneity multipliers with a genetic algorithm. Dadashpour et al, (2008Dadashpour et al, ( , 2009Dadashpour et al, ( , and 2010 used the propagatormatrix method (Stovas and Arntsen 2006) to generate the seismic traces from a stack of plane layer and calibrate reservoir properties by a Gauss-Newton optimization technique.…”