TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIntegrated surface-subsurface modeling has been shown to have a critical impact on field development and optimization. Integrated models are often necessary to analyze properly the pressure interaction between a reservoir and a constrained surface facility network, or to predict the behavior of several fields, which may have different fluid compositions, sharing a common surface facility. The latter is gaining a tremendous significance in recent deepwater field development.These applications require an integrated solution with the following capabilities:• To balance a surface network model with a reservoir simulation model in a robust and efficient manner. • To couple multiple reservoir models, production and injection networks, synchronising their advancement through time. • To allow the reservoir and surface network models to use their own independent fluid descriptions (black oil or compositional descriptions with differing sets of pseudocomponents). • To apply global production and injection constraints to the coupled system (including the transfer of re-injection fluids between reservoirs). In this paper we describe a general-purpose multi-platform reservoir and network coupling controller having all the above features. The controller communicates with a selection of reservoir simulators and surface network simulators via an open message-passing interface. It manages the balancing of the reservoirs and surface networks, and synchronizes their advancement through time. The controller also applies the global production and injection constraints, and converts the hydrocarbon fluid streams between the different sets of pseudo-components used in the simulation models. The controller's coupling and synchronization algorithms are described, and example applications are provided. The flexibility of the controller's open interface makes it possible to plug in further modules (to perform optimization, for example) and additional simulators.

A controller providing a link between reservoir and network simulators has been developed to facilitate reservoir and production management. An optimizer included in the network simulator ensures that optimal management of the coupled system is achieved. The utility of the tool is first tested using a very simple reservoir model with one injector and a single smart producer, and subsequently demonstrated for water-alternating-gas (WAG) injection in two North Sea field cases. For the synthetic test case, various optimization scenarios are explored with the coupled system and compared to standalone flow simulation results. In the North Sea Field 1, optimum oil production is achieved by adjusting the settings of the surface valves as well as the downhole intelligent completion valves (ICVs); in Field 2, gas lift is optimized over time. The coupled system has given more accurate and realistic results in all cases. For the synthetic test case, the coupling gives a significantly higher production than the standalone reservoir simulator run whatever the optimization scenario. For the Field 1 smart well case, the coupled results match historic performance more closely because the pressure drop across the flow lines and surface facilities, the interaction among the wells in the production network, and the boundary conditions are all accounted for. For the gas lift optimization case, the coupled system gives more realistic results with respect to the potential for increased oil production and recovery than the standalone reservoir and network models. Introduction Achieve Reservoir and Production Management Goals Reservoir management is normally achieved using numerical simulation to model the performance of the reservoir under different scenarios of well placement, number of wells, and production and/or injection profiles. However, reservoir simulators do not generally model the production downstream of the wellhead, and so the production network effects on the behavior of the overall system are not fully acknowledged. Flow simulation of the reservoir system also does not account for all the boundary conditions set at the surface, such as the suction pressure of the separator. This may have a direct impact on the evaluation of the production targets that will actually be achieved. On the other hand, production management typically uses surface network nodal analysis tools that fully account for those effects but can only model the reservoir as a homogeneous ‘tank’ of uniform properties. Moreover, reservoir management aims at optimizing the reservoir performance over the field life by maximizing the recovery factor at the minimum cost, while production management is concerned with optimizing the production system capabilities on a day-to-day basis. Thus, reservoir and production management have complementary goals in field development, but on different time scales, and by using separate tools there is no guarantee that one will achieve a solution that satisfies both aims. Therefore, the integration of the capabilities of both reservoir and production system simulators appears to be a critical technology for field development and optimization.

An integrated asset modeling (IAM) approach has been implemented for the Alpine Field and eight associated satellite fields on the Western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir and facilities management options under existing and future operating constraints. Oil, gas and water production from these fields are processed at the Alpine Central Facility (ACF). A number of local constraints exist for the asset, such as the requirement that all associated gas be used for facilities power generation, gas lift or re-injection. All produced water must be re-injected and, for pipeline integrity reasons, must be segregated from imported make-up sea water used for injection. Additionally, surface gas and water handling capacity is limited at the ACF. To further complicate matters, gas injected for EOR purposes is enriched such that it is miscible or near-miscible at reservoir conditions. These conditions create a unique and changing relationship between the oil, gas and water production, gas lift, miscible water alternating gas (MWAG) injection, lean gas injection, facilities constraints and injection availability.The IAM technology utilized for managing the WNS fields consists of full-field compositional reservoir simulation models for each reservoir integrated with a pipeline surface network model and a process facility model. Spreadsheet based allocation routines and advanced mathematical coupling algorithms complete the IAM model enabling not only the prediction of the assets' performance under the aforementioned constraints, capacities and operating conditions, but to optimize overall performance and analyze the impact of decisions. To the authors' knowledge, this is the first time integrated asset modeling has been applied to bring the entire production stream including reservoir, wellbore, surface network and process simulation together for planning and managing MWAG injection to optimize recovery from an existing development.

Shale and tight gas reservoirs consist of porous structures with pore diameter in the range of some mm to m. At these scales, the pore diameter becomes comparable to the gas mean free path. Flows in these structures fail often in the transition and slip regimes. Standard continuum fluid methods such as the Navier-Stokes-Fourrier set of equations fail to describe flows of these regimes. We present a Direct Simulation Monte Carlo study of a 3D porous structure in an unlimited parallel simulation. The three-dimensional geometry was obtained using a microcomputed-tomography (micro-CT) scanner with a resolution in the scale of some m. The gas considered is CH 4 (100%) and the gas inter-molecular collision model for the simulation is the variable hard sphere (VHS). We present results for different Knudsen numbers. The DSMC is applied in porous structures for the flow regimes where it was found in the cavity case to be appropriate. Our results demonstrate that significant differences appear in gas properties depending on the Knudsen number and the flow regime. pure hydrodynamic regime [9]. It is well accepted that the breakdown of the NSF can be quantified by the general criterion of Alexander et. al [10].Shale gas reservoirs have pressures near some MPa. The mean free path () is comparable to the characteristic length of the pore and the gas is often in rarefied regime [11,12]. The ratio of the mean free path to the characteristic length of the flow domain (pore size) is the Knudsen number.Computer simulations are widely used in our days due to the expansion and capability of personal computers and the use of High Performance Computers (H.P.C). Their relatively low cost and their ability to deliver data that is not easily obtained through experiment in lab made them extensively attractive. Simulation for reservoir modeling and transport through porous media become increasingly important in order to optimize and improve gas recovery [13].

Simulation technology from reservoir through process facility has advanced so much, that field development strategies can be developed within a new systematic workflow, using existing applications from many E&P departments. Detailed production data from many sources can be used within simulation models to give a good representation of future field wide behavior. In this paper a fictional case study of a reservoir that has been producing for some 12 years will be examined. The wells are all producing into a sub-sea manifold and then tied back via a 60km flow line and riser system. The reservoir is in severe decline with field production well below the original design capacity of the production system and surface facilities. Hence, further development options are being investigated for this asset. A new, nearby, reservoir has been discovered. A reservoir simulation model has been constructed for the new discovery. This second reservoir is a gas condensate system, much smaller than the existing reservoir and located 90 kms to the east. The current development plan shows six wells drilled and brought into production over an 18 month period. Reservoir 2 is a marginal development, the viability of producing this reservoir will depend on quantification of the reservoir uncertainty and finding a cost effective development strategy with existing processing facilities. The Business Development Team has suggested a number of possible options for developing this new reservoir; Option 1 involves tying in the new reservoir to the existing sub-sea infrastructure. Option 2 is to install a complete new flow line from the sub-sea template of the new reservoir and run this directly to the existing platform. But how do these options effect reservoir management and surface facilities performance? Evaluation is achieved by constructing an integrated asset model of the entire field, allowing the reservoir through facilities interaction to be evaluated in detail. Introduction Everybody wants one, but nobody has one. The Integrated Asset Model (IAM) has been the pursuit of many Oil & Gas companies in the last decade. Finally, the industry shows signs of achieving the prize of the IAM under the banner of the "Digital Oil Field". From reservoir to facility and from today to the end of field life, the IAM promises multi-discipline answers. This paper is intended to serve as a road map for the development and adoption of the IAM into the culture of Oil & Gas Operating Companies. Years from now, new graduates to the industry will have IAM training as part of their Oil & Gas company inductions and they will use the technology to solve many pains from production optimization, operations surveillance & asset planning to uncertainty analysis and fiscal determinations. However, existing work flows and applications will have to change. The questions are by how much, by when, at what cost, and with what benefit? Multiple vendors must collaborate to create cross-discipline compatibility and Oil & Gas companies will need to pilot, evaluate and recommend changes to the resulting IAM technology, which will evolve through a number of rounds of deployment. Collaboration that has never been seen before in the Oil & Gas industry will need to be established if suggested improvements such as $30mn per year per asset for optimization and over $90mn per year in improved Net Present Value (NPV) from planning solutions can be routinely exploited within the average asset. What is needed is a road map for the adoption and development of these IAMs, along with a statement and agreement of the principles that govern the IAM.