Online Journal for E&P Geoscientists

Sliz, Krzysztof K.^*1; Jiao, Jianwu ¹
(1) GPTSD, Saudi Aramco, Dhahran, Saudi Arabia.

Fractured rocks usually provide high permeability pathways for hydrocarbons. Unfortunately, fractures are difficult to image using seismic waves because they are below the typical seismic resolution. The majority of the prestack fracture detection methods, based on P-wave reflection data, use either amplitude variation with angle and azimuth or travel-time changes. Travel-time methods in practice measure velocity variation with azimuth and usually use Root Mean Square (RMS) velocities computed on the Prestack Time Migration (PSTM) gathers. Typical azimuthal anomalies of the RMS velocities represent a 1% to 5% change in the background velocity field. However, these changes can reflect interval velocity differences of 5% to 30%. In this paper, we use azimuthal Prestack Depth Migration (PSDM) followed by residual depth moveout (RMO) analysis to detect interval velocities changes. However direct conversion of the RMO picks to velocity values is usually unstable, thus requiring tomography to be used instead. The disadvantage of the tomographic approach is that updates are usually low frequency and can conceal azimuthal variations. To overcome this problem, we use azimuthal RMO volume scans to detect fracture zones. We compute the RMO volumes using an automatic picking algorithm at every Common Image Gather (CIG) and each data sample. These RMO volumes are computed for each azimuth separately. We then simultaneously scan the RMO values of all volumes to identify five azimuthal attributes: (1) the azimuth of the highest positive RMO, (2) the highest value of the positive RMO, (3) the azimuth of the lowest negative RMO, (4) the lowest value of the negative RMO, and (5) the difference between values of the RMO from the most positive and negative volumes.

This method is more sensitive to velocity changes and/or the presence of fractures than RMS velocity based methods. It is also more suitable for structurally complex areas.

This workflow was successfully applied to a wide azimuth 3D dataset acquired in the northern part of Saudi Arabia and several fractured zones were identified along with their orientation.