Cave Reservoir Forward Modeling And Optimization Of Acquisition And Processing Parameters

Carbonate reservoir has many types and high abundance, due to the strong reservoir heterogeneity, large depth, complex seismic and geological conditions, low quality of seismic data, the predicting is difficult. The main reservoir spaces of carbonate rock are fractures and karst caves, and caves remain predominantly. We based cave models mainly and combine with the actual data of TH area to discuss the caves’ seismic features, acquisition parameters, processing steps to caves imaging.First we analyse the features of a variety of cave models’ single-shot, stacked section and migration profile by using 2D finite difference simulatio n in-depth. Including horizontally layered medium, the presence of weathering and erosion unconformity plane and low velocity near-surface, and summarize the seismic characteristics of the TH actual fractured reservoirs.Then based on the above work, through making a large number of cave models, we discussed the imaging characteristics and vertical resolution, horizontal resolution of cave reservoir. We get the following conclusions: The range of string beads is far larger than the actual cave size after imaging for small cave, with the increase of cave’s width, string beads’ width increase and is gradually closer to the cave’s width, they are basically same when cave’s width is 2 ?, the energy of string beads increase gradually and finally tends to be stable; with the increase of cave’s height, string beads’ length increase, beads are gradually separate and become two center when to ?, the variety of string beads’ energy have similar characteristics with tuning curve. When cave spacing is small than ? /4 coupling is very serious, it can be separated basically when equal to ? /4 and can clearly distinguish them visually when big than ?, so ? is the appropriate imaging resolution for cave reservoir.Finally, proposing a MPI parallel three-dimensional modeling method based on data partition by patch, using 2D modeling and 3D modeling method, focused on discussing the influence of cave imaging by acquisition bin size, fold, maximum offset, static correction, noise removal, accuracy of velocity spectrum and migration aperture. Then, we combine with the actual data processing and analysing of TH area to verify the conclusions are consistent. Research shows : With the increase of bin, string beads’ resolution deteriorate, small scale karst cave’s depiction is gradually blurred, the imaging results have a little difference when bin reduce to a certain extent. Fold increase does not affect the string beads’ resolution, the energy of string beads enhance and SNR increase, but both are not obvious when increase to a certain extent; too small offset and aperture, inaccurate migration velocity will make diffraction’s convergence worse, static correction, denoising will affect the imaging quality of string beads. Considering the cost, bin 15 m × 15 m, fold 143~176 and offset 6000 m are ideal choice for TH area.