Processing Frequent 4D Land Seismic Data with Buried Sensors for CO2 Monitoring

Abstract

Abstract In 2015, Saudi Aramco started a CO2 Water-Alternating-Gas (WAG) EOR pilot project in an onshore carbonate reservoir. To monitor lateral expansion of the CO2 plume, the area was instrumented with a hybrid surface/downhole permanent seismic monitoring system. This system consists of over 1000 buried seismic sensors at a depth of around 70 m, below the the depth of expected weathering layer to mitigate the time-lapse noise. Despite receiver burial, seismic data still suffers from numerous challenges including: significant amounts of high-amplitude coherent noise such as guided waves, mode conversions, and scattered energy; amplitude variations over space and time caused by source and receiver coupling; variability of wavelet shape and arrival times due to seasonal near-surface variations; and low signal-to-noise ratio (SNR). A novel processing workflow was designed for 4D processing of such data. The workflow involves five critical processes. First, the high-amplitude coherent noise is eliminated using FK-based techniques that are 4D compliant to preserve the reservoir changes between repeated seismic surveys. Second, a four-term joint surface-consistent amplitude-scaling algorithm resolves the amplitude variations. The algorithm allows both source and receiver terms to have different scalars for the same positions, but it restricts the other two terms to be position-invariant over different time-lapse surveys, as the window of analysis does not include the reservoir. This is to guarantee that the source and receiver terms are survey-dependent while the other two terms are survey-independent. Thus, the amplitude variability is linked to source and receiver positions over space and time. It also assures that the reservoir changes are not affected by changes in the overburden. Third, wavelet shape variations are addressed using a four-term joint surface-consistent spiking deconvolution algorithm that applies similar principle as the scaling algorithm. Fourth, the small variations in reflection times between different surveys (4D statics) caused by seasonal variations are corrected by a specialized surface-consistent residual statics algorithm using a common pilot derived from the base survey. Fifth, the pre-stack data is supergrouped to enhance the signal-to-noise ratio and repeatability. The processing workflow has been applied to frequent land 3D seismic data acquired over a CO2 WAG EOR pilot project in Saudi Arabia. As a result, we obtained very repeatable seismic images that may successfully detect small CO2-related changes in a stiff carbonate reservoir.