SUMMARYModelling of multi-scale fracture and fault networks is highly uncertain, relying typically on a range of parameters (size, shape, flow properties, connectivity, spacing, orientation) with individually large or sometimes unknown ranges which are very difficult to quantify. Analog data can be used, but resulting models are often unsuitable for well placement or detailed production monitoring in fields under development.Borehole image data are critical in the static (geological) description of the fracture network, but in the absence of dynamic information (e.g. mud losses while drilling, PLT, production, long-term well test) the relative contribution of fractures and fracture networks is speculative. This study highlights the challenges of building a hierarchical description of fractures and connected conduits in a sour gas reservoir which has not started production. In addition, constraining dynamic behaviour is hampered by extreme operational limitations (only short duration well tests and production logging is not feasible).This case study describes a workflow for characterising fault / fracture networks in the pronounced mechanically layered Arab Formation (Late Jurassic) reservoirs of onshore UAE.
TX 75083-3836, U.S.A., fax 01-972-952-9435.
AbstractThe need to rapidly produce a notional field development plan, to reduce costs and cycle-time has driven a fast-track reservoir characterization and reservoir model-building project for one of many fields offshore North West Borneo, Malaysia. The objectives of the study have been defined based on the deliverables stated by the reservoir engineers. Considering the list of data and limited time available, an efficient generalised inversion workflow was designed to process a large volume of deepwater 3D seismic data with a single exploration well to:1. Locate and map all sands (3D geobody identification) versus non-reservoir rocks. 2. Sub-divide reservoir sands into 4 lithofacies (well log neural net based calibration). 3. Propagate 4 lithofacies to entire reservoir volume (within two fluid types). 4. Integrate the fault framework with the sand and fluid distribution to build a first pass reservoir model. 5. Include multiple realizations to manage uncertainty. 6. Check sand connectivity and compute volumes of reserves in place. An innovative combination of 3D geostatistical and neural network techniques was used both for the well log data and for 3D seismic attributes (AVO, Acoustic impedance and dipazimuth combinations) to map the spatial distribution of the sand and their lithofacies. The results of the calibrated and quantitative generalised inversion were used in four different modes to assess the best way to build a first pass reservoir model and compute independent reserves within a short time-frame. This case study illustrates how a purposefully designed 3D/3D reservoir characterisation workflow can reduce the time required to build a first pass static reservoir model and how a similar process can be applied to other complex deepwater hydrocarbon accumulations. It focuses specifically on the different ways a static reservoir model can be built from 3D seismically derived volumes (3D/3D, Hybrid and grid-based).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.