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Seismic data plays a crucial role in reservoir characterization. Quantitative seismic reservoir characterization workflow aims to extract reservoir properties from seismic data to identify sweet spots for exploration, appraisal and development drilling. This is all the more true if the data is to be used in 4D seismic surveys (Time-lapse seismic) for reservoir monitoring. 4D seismic data can help to capture the reservoir change, realize the reservoir dynamic characterization and facilitate an enhanced understanding for reservoir potential, so as to have the better business decisions and optimized field development plans. In this study, use a SAGD (Steam-Assisted Gravity Drainage) field to demonstrate how to extract reservoir dynamic change and evaluate the reservoir performance from 4D seismic data.

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.

Accurate modelling of fault seal is crucial in understanding fluid flow and connectivity in mature fields. Numerous methods of modelling the sealing capacity of faults have been developed, such as lithological juxtapositions, SGR and PSSF. However, due to the uncertainty of structure, throw and lithology prediction, conventional methods are difficult to meet the needs of reservoir development. In order to improve the accuracy of fault seal analysis, a new method was proposed in this abstract. Integrated PSDM seismic data, well logging, and reservoir dynamic data, this new fault seal analysis method include 4 steps: 1) Horizons and faults update based on PSDM seismic data. In this step, more accurate structure model would be generated; 2) Integrated well logging and seismic motion inversion (SMI) for reservoir lithology modelling. In this step, the accuracy of lithology prediction results on both sides of the fault will be improved; 3) Fault seal analysis was ran efficiently in 3D geological model by lithology juxtaposition and SGR. 4) Fault seal analysis QC and optimization by using reservoir dynamic data. This method has been successfully applied to the K mature oilfield in Central Asia. The detailed faults interpretation was done based on new PSDM seismic data. The number of faults increased from 18 to 44, and the new structural model was improved obviously. The reservoir model based on the combination of well logging interpretation and seismic motion inversion (SMI) is not only consistent with drilled well, but also consistent with seismic inversion trend, which improves the accuracy of lithology prediction. Automatic fault seal analysis algorithm based on 3D geological model can efficiently generate the fault seal analysis results, and find potential areas. Fault seal analysis results can be validated and optimized by dynamic data from drilled wells. 6 potential faulted seal traps below conventional oil-water contract (OWC) were found in the slope area of oilfield. 3 wells have been drilled and confirmed 3 fault seal reservoirs with high oil production and much lower water-cut (6%) than conventional water-cut (96%) in the slope of K mature oilfield. It is proved that the method is beneficial for development in mature oil field. This new fault seal analysis method integrated the information from PSDM seismic data, well logging, and reservoir performance data to improve the accuracy of faults model and lithology model, and generated a more reasonable fault seal analysis result. This allows more confidence in estimating the sealing or leaking capacity of faults and reduces risk of re-exploration and development in mature oil field.

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