The multiple challenges associated with a large scale offshore water disposal project are set out in this paper, which goes on to describe how these challenges have been overcome using an integrated multi-disciplinary approach that optimizes disposal well performance, while at the same time significantly reducing the OPEX associated with produced water handling. The difficulties associated with maintaining injectivity in the disposal wells have been overcome through a combination of strategies, including targeting alternative aquifer intervals, which lie above existing oil-producing zones, modifying the completion design, improving the water treatment process and implementing topside modifications. A comprehensive surveillance program has also been implemented to ensure that well and flowline integrity is maintained, and to understand the distribution of injection between the aquifers. A key outcome of this project has been to scale back the requirement for high pressure water disposal (HPWD). The resulting low pressure water disposal (LPWD) strategy has achieved a cost saving of approximately $0.5 per barrel of oil produced which is highly significant in the prevailing low oil price environment.

In many infill development scenarios, including those in shallow, heavy oil intervals, horizontal wells are required, and are positioned as high as possible within the reservoir. In other cases, horizontal wells are drilled to tap undeveloped oil in thin reservoirs with high uncertainty due to seismic resolution limitations. Mubadala Petroleum successfully deployed a new advanced Geosteering technology to overcome these technical challenges. Although Geosteering is often conducted in this Mubadala Petroleum Field, there was a need to mitigate the additional complications of well positioning in complex fluvial reservoirs using innovative approaches and technologies. The solution was a new multi-layer bed boundary detection scheme using a deep azimuthal resistivity distance-to-boundary tool. This was coupled with a novel sophisticated high definition stochastic seismic inversion, providing the ability to resolve multiple bed boundaries above and below the tool, clearly understand formation dip and improve understanding of the boundary azimuth angle. We present two case studies illustrating different applications of the new technology: The seismic inversion provided a clear image of the reservoir sand, however the new multi-layer bed boundary detection technology enhanced the ability to steer through the structural heterogeneous variations in the upper parts of the sand normally beyond seismic resolution of the data. The multi-layer bed boundary detection with high definition inversion provided valuable insight during the real-time horizontal drilling, which helped in maintaining the well in 75% high quality reservoir pay zone.For the horizontal drilling of the thinnest part of the reservoir where pre- and post-conventional seismic inversion volumes were insufficient to provide detailed stratigraphic and geometrical images, we successfully used the new technology to overcome the difficulties. In this approach and after landing the horizontal well into the sand, multi-layer bed boundary detection was used to navigate through the channel sand and maintain the well within the reservoir. This was assisted by streaming real-time high-definition stochastic inversion into the asset team's G&G software, which provided a highly accurate sand thickness, revising pay sand thickness from 15 feet to 25 feet and improving the accuracy of the volumetric estimation. As a result, the horizontal section was successfully navigated in 100% of the section and within high quality reservoir. Furthermore, we used this accurate data for a post-job recalibration of seismic and updating of the geological model and hence improving reserves estimation accuracy. This is the first Mubadala Petroleum implementation in this basin of multi-layer bed boundary detection and streaming high definition stochastic inversion, providing vital information to real-time execution and to post job improvement of the field model.