Reservoir Characterization by Integration of Time-lapse Seismic and Production Data

Abstract

Today, geostatistical reservoir characterization from 3D seismic volumes provides most static descriptions for reservoir models. These models can be improved by integrating the dynamic data in the reservoir description process. 3-D time-lapse seismic surveys have been proposed to relate time dependent-changes in seismic attributes to the flow processes in the reservoir. This paper presents a new approach to reservoir characterization by integrating time-lapse seismic and production data. The issues involved in the integration will be examined. A case study was conducted over a turbidite sheet sand reservoir in the Gulf of Mexico. Seismic data from the base survey were combined with log and production data to build an initial reservoir model which was run forward to the time of a second monitor seismic survey. Dynamic history matching by a simulated annealing type of optimization further improved the model. The output from this simulation was then converted to a synthetic monitor seismic survey using Gassmann's equations and a simple convolutional approach. A quantitative combined seismic and production history-matching methodology was then tested. It constrains the modeling process to match the production history and simultaneously minimize the differences between the synthetic and real 3-D seismic time-lapse data. This new systematic approach provides us with a quantitative time-lapse seismic analysis and reservoir characterization tool which has the potential to improve reservoir management. Introduction Once an oil reservoir has been found and is being produced, it is important to understand the movement of the fluid which is related to the flow mechanism and the reservoir heterogeneity. Recently, 3D seismic data have been used successfully to improve reservoir characterization by extraction of reservoir parameters using inversion, geostatistics and petrophysics to understand the coupling of seismic and rock properties. More than 10 years ago, laboratory work demonstrated that different fluid substitutions can cause acoustic changes, and time-lapse seismic was proposed to capture the changes in reservoir properties with time. These mostly qualitative studies showed that time-lapse seismic even on legacy data resulted in a better understanding of the production behavior of the reservoir. Time-lapse seismic data are mainly used for monitoring of the fluid movement in the reservoir. By integrating other data, they can provide another avenue for reservoir characterization not only including the fluid movement but also other reservoir properties. The following sections document a case study on a turbidite sheet sand reservoir in the Gulf of Mexico where 3-D time-lapse seismic data were integrated with production data from three wells over five years to characterize the reservoir by optimization, and time lapse seismic data were used to validate a seismic history matching methodology. Background This study uses two volumes of "off-the-shelf" data for timelapse seismic analysis. The base 3D seismic volume was acquired in 1988 before the production of the reservoir, while the monitor 3D seismic volume was acquired in 1994 after more than 5 years of production. The difference in orientation of the two surveys was small. The acquisition parameters were different for the two volumes. The base survey was reprocessed to improve the imaging part of the processing sequence to match the 1994 processing sequence more closely. Both volumes were processed through deterministic and adaptive deconvolution, 3-D dip move out and one-pass 3-D migration. The monitor volume was interpolated to smaller bins before migration. The limitations in using "off-the-shelf" data are well known and were recently discussed by Beasley et al., 1996. Since the focus of the present study is to demonstrate an improved seismic history matching methodology, it was decided to use legacy data rather than data reprocessed from field tapes to minimize differences. The accuracy of the results is expected to improve when using carefully reprocessed data sets. The case study was conducted over a sheet sand reservoir in the Gulf of Mexico, offshore Lousiana. The sand is of Pliocene age and pinches out onto a salt-related structural high that controlled its deposition. The average porosity is about 31 % and its permeability is about 500 md. P. 439^

Petro-Elastic Models: How Many and at What Scale?

“…Similar quantitative work was proposed by other authors (Huang et al 1997;Waggoner et al 2002) who applied different techniques to the objective functions to minimize the error between inverted seismic impedences and those obtained from petro-elastic models. Several authors (Falcone et al 2004; Menezes and Gosselin 2006; Mezghani et al 2004) have discussed the issues related to PEM calibration and the scale at which it should be applied.…”

“…Previous studies (Huang et al 1997;Waggoner et al 2002;Menezes and Gosselin 2006) employed the use of an optimization technique to adapt the fine-scale PEMs to an upscaled reservoir grid. The optimization technique involved minimizing the mismatch between observed and predicted impendences.…”

Petro-Elastic Parameters Effects on History Matching Procedures

“…Some authors are used to work with amplitudes differences Stanley, 1997 and. Nevertheless, the good correlations between Acoustic Impedance (AI) and rock and fluid properties allow a quantitative workflow of this attribute and reservoir properties such as saturations, mass densities, pressure and effective porosity.…”

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

“…These involve computation of a misfit between predicted and observed data, and then a mathematical routine is used to choose new models either stochastically or deterministically. Many researchers have investigated different inversion algorithms, parameterization schemes, and appropriate methods to compare data (Huang et al 1997;Gosselin et al 2001;Aanonsen et al 2001;Arenas et al 2003;Lygren et al 2003;Kretz et al 2002;Mantica et al 2001;Vasco et al 2003;Portella and Emerick 2005;Jin et al 2007;Roggero et al 2007;Skjervheim et al 2007). Often, observed acoustic-impedance changes, which may be of undefined units, are compared to equivalent synthetic seismic data (Guerillot and Pianelo 2000;Clifford et al 2003;Mezghani et al 2004;Emerick et al 2007).…”