Using slowness and azimuth fluctuations as new observables for four‐dimensional reservoir seismic monitoring

Abstract

Four‐dimensional imaging using geophysical data is of increasing interest in the oil and gas industries. While travel‐time and amplitude variations are commonly used to monitor reservoir properties at depth, their interpretation can suffer from a lack of information to decipher the parts played by different parameters. In this context, this study focuses on the slowness and azimuth angle measured at the surface using source and receiver arrays as complementary observables. In the first step, array processing techniques are used to extract both azimuth and incidence angles at the source side (departure angles) and at the receiver side (arrival angles). In the second step, the slowness and angle variations are monitored in a laboratory environment. These new observables are compared with traditional arrival‐time variations when the propagation medium is subject to temperature fluctuations. Finally, field data from a heavy‐oil permanent reservoir monitoring system installed onshore and facing steam injection and temperature variations are investigated. The slowness variations are computed over a period of 152 days. In agreement with Fermat's principle, strong correlations between the slowness and arrival‐time variations are highlighted, as well as good consistency with other techniques and field pressure measurements. Although the temporal variations of slowness and arrival time show the same features, there are still differences that can be considered for further characterization of the physical changes at depth.

Evidence of metamaterial physics at the geophysics scale: the METAFORET experiment

“…The source-receiver subarray pairs are chosen such that the distance between the subarray centres is always >20 m. The use of subarrays aligned along the same direction inside the forest allows the extraction of the Rayleigh wave component from the total wavefield through a double beamforming (DBF) process. DBF was recently applied to dense seismic networks to extract surface wave components from either active sources (De Cacqueray et al 2011;Boué et al 2013) or ambient noise correlations (Boue et al 2014;De Cacqueray et al 2015;Roux et al 2016;Wathelet et al 2018). Following the same procedure, the goal is to time-delay and coherently stack the N × M source-receiver signals according to: (1) the distance between each source and each receiver and (2) the Rayleigh wave slowness in the propagation medium.…”