Turner R.,Brunei Shell Petroleum Co.
Society of Petroleum Engineers - International Petroleum Technology Conference 2012, IPTC 2012
In 1999 Brunei Shell Petroleum (BSP) completed its first Smart Well, leading to over 100 Smart Wells being installed over the subsequent decade. BSP defines Smart as having remotely operated control devices in the completion string; a well only containing a downhole pressure gauge is not considered Smart. Typical wells in Brunei intersect multi-layered reservoirs containing oil, water and/or gas, many of them oil rims. The relatively low hydrocarbon volume per reservoir requires each well to develop numerous reservoirs in order to be economic. Smart completions allow flow to be controlled on a per zone basis, enabling different dynamic reservoir responses to be managed over the field life (e.g. by controlling drawdown or optimising gas development). This ability, particularly for oil rims, has enabled BSP to develop fields that would not have been sanctioned otherwise. Obtaining optimum value from Smart Wells is demanding. Additional resources are required as more data needs to be reviewed and more frequently, in order to perform the many zonal optimisations necessary for optimum reservoir management. Workflows become more complex, additional interfaces with other disciplines are required and new work competencies need to be acquired. The challenge for operators contemplating the use of Smart Technology is that Smart Capabilities are required in all major company departments. Subsurface teams need to justify Smart requirements in Field Development Plans, Well Engineering want to standardise Smart completions, Production Departments require processes to efficiently maximise lifecycle production, whilst IT personnel have to provide reliable two way communications between the wells and head office. Smart completions lend themselves to being able to prove to stakeholders that fields are being optimally managed, particularly with regards to reserves recovery. In the future having Smart Field capability may become a requirement as opposed to an option, possibly turning it into a licence to operate. Copyright 2011, International Petroleum Technology Conference. Source
Xu C.,Brunei Shell Petroleum Co. |
Gehenn J.-M.,Royal Dutch Shell |
Zhao D.,Royal Dutch Shell |
Xie G.,Petrochina |
Teng M.-K.,Saudi Aramco
The calcarenites and siliciclastic sandstones in the Upper Triassic Xujiahe Formation are key targets of the unconventional gas exploration in Sichuan Basin. To resolve the long-debated stratigraphic correlation of the interfingering lithostratigraphic members between the different exploration blocks, a facies classification scheme integrating sedimentary structures in borehole images (BHI) and lithology from wireline logs is calibrated in the cored wells and extrapolated into the noncored wells. Three major sedimentary systems are recognized in Xujiahe by mapping the facies trends and paleocurrent directions in the coarse-grained sediments. One system may dominate over the others at different times and/or locations in the foreland basin in response to the oro-genic movements in the surrounding mountains. The Longmen Shan in the northwest of the study area is the main source provenance for the carbonate conglomerates and calcarenites. Meanwhile, the systems in the northeast and southeast deposited the sandstone wedges. Alternating lacustrine, swamp, and fluvial sediments are penetrated in both proximal and distal areas in the basin, although there is no indication of a basinwide lake in the Xujiahe period. Abundant sandy shale-breccia conglomerates containing large shale breccias, plant materials, and contorted shale blocks in association with thick foreset-bedded sandstones suggest subaqueous gravity flows possibly because of the collapse of a multistoried delta front. Characterization of these dynamic sedimentary systems through multidisciplinary interpretation of the seismic data, cores, BHI, and wireline logs is not only fundamental to the sedimentary analysis, but also the exploration strategy in the areas with optimal juxtaposition of reservoir sandstones and coaly source rocks. Copyright © 2015. The American Association of Petroleum Geologists. All rights reserved. Source
Falivene O.,Royal Dutch Shell |
Frascati A.,Royal Dutch Shell |
Gesbert S.,Royal Dutch Shell |
Pickens J.,Royal Dutch Shell |
And 4 more authors.
Understanding and predicting reservoir presence and characteristics at regional to basin scales is important for evaluating risk and uncertainty in hydrocarbon exploration. Simulating reservoir distribution within a basin by a stratigraphie forward model enables the integration of available prior information with fundamental geologic processes embedded in the numerical model. Stratigraphie forward model predictions can be significantly improved by calibrating the models to independent constraints, such as thicknesses from seismic or well data. A three-dimensional basin-scale stratigraphie forward-modeling tool is coupled with an inversion algorithm. The inversion algorithm is a modification of the neighborhood algorithm (a type of genetic algorithm), which is designed to sample complex multimodal objective functions and is parallelized on computer clusters to accelerate convergence. The process generates a set of representative geological models that are consistent with prior ranges for uncertain parameters, calibration constraints, and associated tolerance thresholds. The workflow is first demonstrated on two data sets: a synthetic example based on a clastic passive margin and a real hydrocarbon exploration example for slope and basin-floor stratigraphie traps in the Neocomian (Lower Cretaceous) of the West Siberian Basin. The analysis of calibrated models provides constraints on stratigraphie controls, and allows prediction of locations with higher potential to develop stratigraphie traps. These locations are related to complex interactions between pale-obathymetry, subsidence, eustatic fluctuations, characteristics of sediment-input sources, and sediment-transport parameters. Results show the potential of stratigraphie forward modeling combined with inverse methods as an additional tool to support conventional play-based exploration and reservoir-presence prediction. Copyright © 2014. The American Association of Petroleum Geologists. All rights reserved. Source
Yong I.,Brunei Shell Petroleum Co.
Society of Petroleum Engineers - SPE Intelligent Energy International 2012
Brunei Shell Petroleum (BSP) first started completing Smart Wells in 1999, trialing standalone technologies such as permanent downhole gauges and inflow control valves in individual wells. Once these were seen as successful, the technology was used extensively on a single platform. This was later extended to application in a whole field, taking advantage of refinements such as variable downhole control valves and multiphase flow metering. Learning from the successes of other oil producing fields such as Champion West and Bugan, Seria North Flank was planned and designed as a fully Smart field. Seria North Flank would be the first field to fully integrate Smart technology with Smart field processes, improving the efficiency of Well and Reservoir Management activities and accelerating reservoir understanding in order to reduce uncertainties for future development. This resulted in the development of over 120 million barrels of oil, with improved Unit Technical Costs compared to an offshore development. Copyright 2012, Society of Petroleum Engineers. Source
Langhi L.,CSIRO |
Zhang Y.,CSIRO |
Gartrell A.,CSIRO |
Gartrell A.,Brunei Shell Petroleum Co. |
And 2 more authors.
Three-dimensional (3-D) coupled deformation and fluid-flow numerical modeling are used to simulate the response of a relatively complex set of trap-bounding faults to extensional reactivation and to investigate hydrocarbon preservation risk for structural traps in the offshore Bonaparte Basin (Laminaria High, the Timor Sea, Australian North West Shelf). The model results show that the distributions of shear strain and dilation as well as fluid flux are heterogeneous along fault planes inferring lateral variability of fault seal effectiveness. The distribution of high shear strain is seen as the main control on structural permeability and is primarily influenced by the structural architecture. Prereactivation fault size and distribution within the modeled fault population as well as fault corrugations driven by growth processes represent key elements driving the partitioning of strain and up-fault fluid flow. These factors are critical in determining oil preservation during the late reactivation phase on the Laminaria High. Testing of the model against leakage indicators defined on 3-D seismic data correlates with the numerical prediction of fault seal effectiveness and explains the complex distribution of paleo- and preserved oil columns in the study area. Copyright © 2010. The American Association of Petroleum Geologists. All rights reserved. Source