Histrorically, Upper Safa is considered to be the source rock of the gas and condensate accumulated in Lower Safa stratum in Obaiyed Field. Both of Upper and Lower Safa units are parts of Khatatba formation "Jurassic age, Western Desert coulumn, Egypt". The integration of all petrophysical and geochemical data indicated that, there is a rich organic Carbon embedded in the formation with a high britteleness ratio. As a result of the opportunity identification, there is an operational scope being studied now to proceed with a haydrulic fracturing stimulation targeting the sweet intervals "Intervals of high TOC and high Britteleness ratio" aiming to maximize the whole gas and condensate production of the field. This paper is summarizing the opportunity identification process and results using available petrophysical and geochemical data.Six wells had been used in this study where there is a complete set of well and continuous petrophysical data exist in all of them supported by geochemical analysis reports. Specific interpretation techniques were utilized to identify the opportunity from the logs. The property of Total Organic Carbon was estimated from logs using standered DeltaLogR Passey Technique and then verified using measured data. The rock briteleness property was estimated from avilable acoustic sonic logs "Compressional and Shear slowness". The type of Kerogen and level of Maturity were recognized from geochemical sources. The data integration provided a well identification of the shale gas opportunity.As a part of complete assessment study of unconventional resources, a dedicated subsurface team was formed in order to evaluate the connectivity of Upper Safa, estimate the in place volumes and define the development options. The team also proposed on short term scale performing a vertical hydraulic fracturing in one of the sweet wells in order to prove the evaluation concept and increase total field production.The success of this project is measured by three aspects: first, proving the presence of commercial shale gas plays in Upper Safa unit, second, maximizing the gas and condensate production from the field and finally, on the long term scale, unlocking commercial unconventional gas resource for future generations in Western Desert, Egypt.

The paper discusses conceptualization, design and implementation of the first ever inflow tracer technology application in UAE carried out in an Abu Dhabi offshore field. Working in offshore environment has challenges related to operations, cost, resource requirements and HSE that requires innovative and cost-effective solutions to improve efficiency. In recent years, controlled release smart tracers have carved out a niche as a proven solution for extended life fluid flow monitoring, thus allowing the engineers and geoscientists to better understand fluid inflow patterns in a well leading to informed decisions on reservoir management and production optimization. Smart tracers have the capability to detect, quantify and monitor phase breakthroughs and understand subsequent influx behavior in the well. Being a pioneer project, critical focus was placed on design, execution, and cost optimization. Smart tracer technology was chosen over conventional production logging as it provided production profile monitoring over time compared to single time measurement when using production logging, substantially lower operating cost as well as no production intervention. A flowback calculation was used inputting static and dynamic reservoir data to understand the flow dynamics that the tracers would encounter. Reservoir permeability profiles, image logs and hole rugosity were utilized to identify potential areas of influx along the wellbore and strategically place specially designed smart oil and water tracers along the ~3300 feet long lateral. Strictly adhering to local environmental regulations, a thorough offshore job hazard analysis was carried out and a risk matrix was framed. A specialized first of a kind closed loop customized sampling procedure was invented to de-risk a hydrogen sulfide (H2S) hazard present during sampling operations. The paper describes the initial results for the first well in the campaign. Sampling strategy consisted of two phases: high-frequency immediately after well commissioning followed by steady state sampling. Samples were collected at the wellhead and analyzed for tracer breakthroughs. Results showed a good calibration with conventional production logging, confirmed well clean-up and yielded crucial information on zonal flow contribution. Utilizing a local cost model, smart tracer technology was found to offer typical cost savings in the order of US$10 million for a ten well program over five years as compared to conventional production logging. The paper offers insights into the first application of controlled release tracers in offshore Abu Dhabi highlighting the best practices in project design, techno-economics, hazard analysis and operational excellence. The success of the project is the first major step towards embracing this advanced technology for reservoir monitoring and surveillance. This opens opportunities for similar applications elsewhere with significant potential to incentivize life-cycle cost of reservoir management and improve hydrocarbon recovery.

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Summary Nine multimillion-cell geostatistical earth models of the Marrat reservoir in Magwa field, Kuwait, were upscaled for streamline (SL) screening and finite-difference (FD) flow simulation. The scaleup strategy consisted of (1) maintaining square areal blocks over the oil column, (2) upscaling to the largest areal-block size (200 x 200 m) compatible with 125-acre well spacing, (3) upscaling to less than 1 million gridblocks for SL screening, and (4) upscaling to less than 250,000 gridblocks for FD flow simulation. Chevron's in-house scaleup software program, SCP, was used for scaleup. SCP employs a single-phase flow-based process for upscaling nonuniform 3D grids. Several iterations of scaleup were made to optimize the result. Sensitivity tests suggest that a uniform scaled-up grid overestimates breakthrough time compared to the fine model, and the post-breakthrough fractional flow also remains higher than in the fine model. However, preserving high-flow-rate layers in a nonuniform scaled-up model was key to matching the front-tracking behavior of the fine model. The scaled-up model was coarsened in areas of low average layer flow because less refinement is needed in these areas to still match the flow behavior of the fine model. The final ratio of pre- to post-scaleup grid sizes was 6:1 for SL and 21:1 for FD simulation. Several checks were made to verify the accuracy of scaleup. These include comparison of pre- and post-scaleup fractional-flow curves in terms of breakthrough time and post-breakthrough curve shape, cross-sectional permeabilities, global porosity histograms, porosity/permeability clouds, visual comparison of heterogeneity, and earth-model and scaled-up volumetrics. The scaled-up models were screened using the 3D SL technique. The results helped in bracketing the flow behavior of different earth models and evaluating the model that better tracks the historical performance data. By initiating the full-field history-matching process with the geologic model that most closely matched the field performance in the screening stage, the amount of history matching was minimized, and the time and effort required were reduced. The application of unrealistic changes to the geologic model to match production history was also avoided. The study suggests that single realizations of "best-guess" geostatistical models are not guaranteed to offer the best history match and performance prediction. Multiple earth models must be built to capture the range of heterogeneity and assess its impact on reservoir flow behavior. Introduction The widespread use of geostatistics during the last decade has offered us both opportunities and challenges. It has been possible to capture vertical and areal heterogeneities measured by well logs and inferred by the depositional environments in a very fine scale with 0.1- to 0.3-m vertical and 20- to 100-m areal resolution (Hobbet et al. 2000; Dashti et al. 2002; Aly et al. 1999; Haldorsen and Damsleth 1990; Haldorsen and Damsleth 1993). It also has been possible to generate a large number of realizations to assess the uncertainty in reservoir descriptions and performance predictions (Sharif and MacDonald 2001). These multiple realizations variously account for uncertainties in structure, stratigraphy, and petrophysical properties. Although impressive, the fine-scale geological models usually run into several millions of cells, and current computing technology limits us from simulating such multimillion-cell models on practical time scales. This requires a translation of the detailed grids to a coarser, computationally manageable level without compromising the gross flow behavior of the original fine-scale model and the anticipated reservoir performance. This translation is commonly referred to as upscaling (Christie 1996; Durlofsky et al. 1996; Chawathe and Taggart 2001; Ates et al. 2003). The other challenge is to quantify the uncertainty while keeping the number of realizations manageable. This requires identifying uncertainties with the greatest potential impact and arriving at an optimal combination to capture the extremes. Further, these models require a screening and ranking process to assess their relative ability to track historical field performance and to help minimize the number of models that can be considered for comprehensive flow simulations (Milliken et al. 2001; Samier et al. 2002; Chakravarty et al. 2000; Lolomari et al. 2000; Albertão et al. 2001; Baker et al. 2001; Ates et al. 2003). In some situations, often a single realization of the best-guess geostatistical model is carried forward for conventional flow simulation and uncertainties are quantified with parametric techniques such as Monte Carlo evaluations (Hobbet et al. 2000; Dashti et al. 2002). Using the case study of this Middle Eastern carbonate reservoir, the paper describes the upscaling, uncertainty management, and SL screening process used to arrive at a single reference model that optimally combines the uncertainties and provides the best history match and performance forecast from full-field flow simulation. Fig. 1 presents the details of the workflow used.

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