Seismic Motion Inversion Based on Geological Conditioning and Its Application in Thin Reservoir Prediction, Middle East

Abstract With the development of exploration and development, thin reservoir prediction is becoming more and more important. However, due to the limit of seismic resolution, thin reservoir prediction has always been an important challenge in the Middle East. Thin reservoir prediction based on conventional geophysical techniques is not accurate enough to meet the requirements of development. In order to improve the accuracy of thin reservoir prediction, a new thin reservoir prediction technique is proposed. This technical workflow main includes 4 steps: (1) Sedimentary facies identification based on multidisciplinary analysis, (2) Sedimentary facies model and seismic forward modelling, (3) Seismic response characteristics analysis and seismic data conditioning under the guide of forward modelling, (4) Seismic meme inversion and thin reservoir prediction. The new drilled wells demonstrate the successful application of the techniques in the M field in the Middle East. The 5 layers of thin sandstones reservoir can be divided into two sets of high stand systems of sand and low stand systems of incised valley deposits. Geological model seismic forward modelling shows that the most sensitive seismic dominant frequency for effectively identifying the two groups of sandstone is 35Hz (Fig.1). Through the high resolution seismic processing, the main frequency of seismic data was optimized from 25Hz to 35Hz, which improves the recognition ability for the thin sand groups (Fig.2). Seismic facies analysis based on previous and new seismic data shows that different thin reservoir layer can be effectively identified by seismic facies. Under the constraints of seismic facies, the Seismic meme inversion can effectively predict the two sand groups (Fig.3). 51 km2 of thin reservoir favourable area was discovered and 16 wells were drilled with 91% success rate based on the new seismic inversion result in the southeast part of the oilfield. This technology can effectively integrate geological information and seismic conditioning techniques, and improve the accuracy of thin reservoir prediction results more reasonably, which can not only provide support for the exploration for thin reservoir but also efficient development. This technique is applicable not only to thin clastic reservoir but also to thin carbonate reservoir.