[1] The oxygen isotope ratio in seawater (d 18 O sw ) is an important indicator of past hydroclimatic conditions in the tropics. In this study, we apply a dual proxy approach using

The oxygen isotopic ratio in the seawater (δ 18 O sw ) recorded in δ 18 O of coral skeleton for several centuries in tropical regions is an important variable for reconstructing the past climate. However, the relationship between δ 18 O sw and hydrological balance has not been clearly uncovered yet. In this study, a one-dimensional ocean budget model forced by the global quasi-reanalysis isotope data, which is the output of an isotope-incorporated global spectral model, is employed to simulate δ 18 O sw . The results from the simulations are compared with 31 coral records in tropical oceanic regions. The 1-D model successfully reproduces δ 18 O sw in 21 coral records, indicating these corals have the potential to reconstruct the local hydrological budget. The results also confirm that the reproducibility of the 1-D model generally increases with the annual precipitation variability. However, in the regions where the precipitation variability is less, it is more difficult to reconstruct climatic information from the δ 18 O sw , since the nonlocal physical processes unaccounted for in the 1-D model may affect δ 18 O sw . The sensitivity analysis of the 1-D model shows that in tropical oceanic regions, the large-scale precipitation is the dominant factor for δ 18 O sw , confirming that δ 18 O sw recorded in the coral records is a proxy for the precipitation anomaly. This study implies that δ 18 O sw is more closely associated with precipitation and local water isotope budget in tropical oceanic regions than elsewhere, and that the 1-D model is a useful approach for the proxy interpretation in tropical oceanic regions.

Sedimentary forward modeling is an effective and economical approach for exploration of sandstone reser voirs, especially in deepwater settings. Computational fluid dynamics brought innovation to modeling and simulating the behaviors of sediment gravity flows(turbidity currents in most cases)along with the prediction of turbidite distribution pattern. Furthermore, establishment of structural and isostatic balancing technique contributed to quantitative reconstruction of paleo-basin structure and paleo-bathymetry. Recent dramatic improvements of computer operating speed and software performance have allowed us to apply such numerical methods to a three-dimensional (3-D), several ten to hundred kilometer wide dataset. The integration of these sedimentary and structural approaches has nally produced an advanced forward sedimentary model that numerically predicts a turbidite distribution on the reconstructed 3-D paleo-bathymetry. Such an integrated model is powerful and useful for better understanding of lobate turbidite distribution and of turbidite deposition in topographically complicated basins, or con ned basins, and also for stochastic determination on input parameters of turbidity current (s)based on a limited number of available well-log data. Combination with seismic geomorphology and seismic attribute analysis can improve the quality and accuracy of geological evaluation in deepwater turbidite play.

Seismic processing is expected to deliver a reliable product for seismic structure interpretation and reservoir characterization to support subsequent reservoir modeling and drilling operation. 3D OBC wide-azimuth (WAZ) seismic data has a significant advantage in this respect, since it contains large offset and wide azimuth information, which can be utilized for amplitude versus offset (AVO) and amplitude versus azimuth (AVAz) analysis respectively. However, these advanced studies require area-specific data processing efforts in this region for strong linear noise, multiple noise, designature uncertainty etc. Seismic data re-processing was newly conducted in pre-stack time migration (PrSTM) sequence to overcome such technical challenges. Surface-wave noise attenuation was achieved by 2D and 3D model-based dispersive energy attenuation and multi-pass structure oriented filtering in pre-stack and post-stack stages. Source-side deghosting highlighted a low frequency residual bubble and was followed by another pass of de-bubbling based on a smooth spectrum assumption. Shallow water reverberation was minimized by 3D deconvolution and high-resolution radon demultiple. 5D regularization and interpolation with trace densification provided evenly populated traces for each angle and azimuth sectors. Intensive signal preservation protected diffraction energy and resulted in better fault imaging than the vintage processing outcome. Both the vintage and the re-processed seismic data were evaluated in quantitative manners to compare with each other. Angle stacks from the re-processing showed more reasonable response for AVO analysis. Azimuth stacks showed stable S/N ratio leading to higher confidence in azimuthal anisotropy analysis. All these seismic deliverables enable accurate elastic property estimation and detail fault interpreation at target reservoir intervals. The novelty of the proposed workflow is to overcome common technical problems with targeted noise and multiple reduction focusing on angle / azimuth stacks. It unlocks advanced geophysical study for carbonate reservoir in shallow water areas. Processing deliverables will be fully utilized in fault interpretation, AVO and AVAz analysis and will support the forthcoming field development activity.

Reservoir simulation is widely used for field development planning in many fields and the evaluation of uncertainty range in production forecast is indispensable to make decision for further investment. Reservoir simulation model consists of geological, petrophysical and reservoir engineering parameters for each cell and cell boundary. These reservoir model parameters are usually defined based on limited available data in consideration of their uncertainty range. Therefore, the identification of influential parameters and the reduction of uncertainty range for these parameters are key components to mitigate the prediction uncertainty. An Upper Jurassic carbonate reservoir in Field A located in offshore Abu Dhabi has long production history for more than 30 years. Field A experienced several development schemes including natural depletion, crestal gas injection and crestal water injection. The current reservoir simulation model reasonably replicates historical performance on pressure, water cut evolution and GOR trend in field and well-by-well scales. On the other hand, we identified some reservoir model parameters have high uncertainty due to reservoir complexity and lack of reliable data. In this study, we focused on the identification of influential parameters on production forecast and the reduction of parameter uncertainty range using an experimental design approach. More than 200 simulation cases were generated with different combination of selected parameters using Latin Hypercube Sampling method. In each case, we evaluated history matching quality in field scale and relationship between history matching quality and each parameter. We found some parameters have correlation with history matching quality independently from the other parameters settings. This means that the uncertain range of those parameters can be reduced to achieve an acceptable history match irrespective of the other parameters. Furthermore, the prediction uncertain range was analyzed using the selected cases showing reasonable history matching quality to investigate the relationship between cumulative oil production and each parameter. The results indicated some parameters have a stronger impact on production forecast and their uncertainty range need to be reduced by further data gathering or considering other mitigation plans. This study successfully demonstrated that the proposed multiple parameter sensitivity analysis by effective use of experimental design approach enables to reduce the parameter uncertain range and identify the key influential parameters. Furthermore, this study result contributes to the prioritization and optimization of future data gathering plan in Field A.