Laurent J. Meister

Determination of the principal directions of azimuthal anisotropy from P-wave seismic data

In an azimuthally anisotropic medium, the principal directions of azimuthal anisotropy are the directions along which the quasi-P- and the quasi-S-waves propagate as pure P and S modes. When azimuthal anisotropy is induced by oriented vertical fractures imposed on an azimuthally isotropic background, two of these principal directions correspond to the directions parallel and perpendicular to the fractures. S-waves propagating through an azimuthally anisotropic medium are sensitive to the direction of their propagation with respect to the principal directions. As a result, primary or mode‐converted multicomponent S-wave data are used to obtain the principal directions. Apart from high acquisition cost, processing and interpretation of multicomponent data require a technology that the seismic industry has not fully developed. Anisotropy detection from conventional P-wave data, on the other hand, has been limited to a few qualitative studies of the amplitude variation with offset (AVO) for different azimutha...

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

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 Gassmanns 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.

Improving production history matching using time‐lapse seismic data

This paper presents a new approach to reservoir management by integrating time‐lapse seismic data with production data. The basic steps involve combining the seismic data from the base survey with log and production data to build an initial reservoir model which is run forward to the time of the repeat seismic survey. The output from this simulation was then converted to a synthetic monitor survey, using Gassmann’s equations and a simple convolutional approach. Finally, the differences between the synthetic and real seismic time‐lapse data are minimized using an optimization algorithm.

Repeatability of 3-D ocean-bottom cable seismic surveys

Recent advances allow time‐lapse 3-D (sometimes called 4-D) seismic surveys to map gas‐oil and oil‐water contacts. Some have used “legacy” 3-D data (i.e., data not originally acquired for a 4-D study) and found that the varying quality of the different vintages of the data limited the resolution that could be attained. How then, do we best carry out acquisition and processing for a 4-D seismic program so that the data are repeatable to the extent that differences are attributable to movement of the reservoir fluids and not to the acquisition, processing, or other factors?

Integration of production and time-lapse seismic data

Methods for application of time lapse seismic data to reservoir management range from purely qualitative evaluation, to its use as a constraint in rigorous numerical dynamic model optimization. The advantage of numerical optimization over qualitative analysis is that the former provides a quantitative reservoir model much needed for practical reservoir management applications. However, the large gap existing between these qualitative and quantitative methods not only inhibits wider use of time lapse seismic data as a reservoir monitoring tool, but also represents the unnecessary delay of integration between seismic and production data until very advanced stages of reservoir model building. Recent work by Huang et al (1999a) lead to a method for early integration of time-lapse seismic and production data in a Gulf of Mexico gas field 4D study. Such early integration helps to reduce ambiguity in time-lapse seismic data processing and provides useful semi-quantitative results which may be used for some engineering applications. The numerical optimization process data benefits from this preliminary step through improved initial estimate and more efficient convergence. The overall results of this two stage integration approach are; useful intermediate products, improved turn-around time, and increased reliability in model based time lapse analysis.

Improvement on the Quantitative Seismic History Matching

In general only a single 3D seismic data set is used for the characterization of the static properties of a reservoir, even though these static properties could be integrated with dynamic properties [Huang and Kelkar, 1996]. But sometimes, more than one 3D seismic data volume has been acquired over the same field after the production starts. We propose to use this additional data not only to understand the dynamic properties of the reservoir using a time-lapse seismic analysis as presented last year, but to improve the accuracy of the initial reservoir description and to study the sensitivity of the optimization process to the input data. Using two 3D seismic data sets acquired over a field in the Gulf of Mexico, this paper demonstrates a method to improve the porosity map obtained from the geostatistical characterization of the base 3D seismic data set by an analysis of the second 3D data volume. It is not possible using just the base data to differentiate factors such as fluid content from the geostatistical porosity characterization. But by using the flow information from the wells and the second 3D seismic data which was acquired after the production, it is possible to decouple the porosity, fluid contents. Consequently, it provides a more representative porosity characterization compared to one from using a single 3D seismic data set. The relative permeability curves are optimized by comparing actual time-lapse seismic data to synthetic ones generated with a Gassmann rock physics model.

Production history matching with time lapse seismic data

Today geostatistical reservoir characterization from 3D seismic volumes provides most of the static descriptions for reservoir models. These models can be improved by integrating dynamic data in the reservoir description process. Recently 3D 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 an improved approach to seismic reservoir monitoring by integrating reservoir simulation with the time-lapse seismic technique. A case study was conducted over a turbidite sheet sand reservoir in the Gulf of Mexico. The seismic data from the base survey were combined with log and production data to build an initial simulator model which was run forward to the time of the monitor seismic survey. Dynamic history matching performed by a simulated annealing type of optimization further improved the simulator model. The output from the simulation was then converted to a synthetic monitor seismic survey using Gassmann’s equations and a simple convolutional approach. A quantitative seismic history matching methodology was then tested. It constrains the modeling process to match the production history and minimize the error between the synthetic and measured 3D seismic time-lapse difference. This new systematic approach provides us with a quantitative time-lapse seismic analysis tool which has the potential to improve reservoir management.