Annual Simulation Symposium 

About: Annual Simulation Symposium is an academic conference. The conference publishes majorly in the area(s): Discrete event simulation & Reservoir simulation. Over the lifetime, 2416 publications have been published by the conference receiving 30744 citations.

Tenth SPE Comparative Solution Project: a comparison of upscaling techniques

Abstract: This paper presents the results of the Tenth SPE Comparative Solution Project on Upscaling. Two problems were chosen. The first problem was a small 2D gas injection problem, chosen so that the fine grid could be computed easily, and both upscaling and pseudoisation methods could be used. The second problem was a waterflood of a large geostatistical model chosen so that it was hard (though not impossible) to compute the true fine grid solution. Nine participants provided results for one or both problems. Introduction The SPE Comparative Solution Projects provide a vehicle for independent comparison of methods and a recognized suite of test datasets for specific problems. The previous nine comparative solution projects – 9 have focussed on black-oil, compositional, dual porosity, thermal or miscible simulations, as well as horizontal wells and gridding techniques. The aim of the tenth comparative solution project was to compare upgridding and upscaling approaches for two problems. Full details of the project, and data files available for downloading can be found on the project web site. The first problem was a simple 2000 cell 2D vertical cross section. The tasks specified were to apply upscaling or pseudoization methods and obtain solutions for a specified coarse grid, and a coarse grid selected by the participant. The second problem was a 3D waterflood of a 1.1 million cell geostatistical model. This model was chosen to be sufficiently detailed that it would be hard, though not impossible, to run the fine grid solution and use classical pseudoisation methods. We will not review the large number of upscaling approaches here. For a detailed description of these methods see any of the reviews of upscaling and pseudoisation techniques, for example – . Description of Problems Model 1 The model is a 2-phase (oil and gas) model that has a simple 2D vertical cross-sectional geometry with no dipping or faults. The dimensions of the model are 762 meters long by 7.62 meters wide by 15.24 meters thick. The fine scale grid is 100 x 1 x 20 with uniform size for each of the grid blocks. The top of the model is at 0.0 metres with initial pressure at this point of 100 psia. Initially the model is fully saturated with oil (no connate water). The permeability distribution is a correlated geostatistically generated field, shown in Fig 1. The fluids are assumed to be incompressible and immiscible. The fine grid relative permeabilities are shown in Fig 2. Capillary pressure was assumed to be negligible in this case. Gas was injected from an injector located at the left of the model and dead oil was produced from a well on the right of the model. Both wells have a well internal diameter of 1.0 ft and are completed vertically throughout the model. The injection rate was set to give a frontal velocity of 0.3 m/d (about 1 foot/day or 6.97 m per day), and the producer is set to produce at a constant bottom pressure limit of 95 psia. The reference depth for the bottom hole pressure is at 0.0 meters (top of the model). The tasks specified were to apply upscaling or pseudoization method in the following scenarios: 1. 2D – 2D uniform 5 x 1 x 5 coarse grid model 2. 2D – 2D nonuniform coarsening. Max 100 cells. Directional pseudo relative permeabilities were allowed if necessary. Model 2 This model has a sufficiently fine grid to make use of any method that relies on having the full fine grid solution almost impossible. The model has a simple geometry, with no top structure or faults. The reason for this choice is to provide maximum flexibility in selection of upscaled grids. SPE 66599 Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques M A Christie, SPE, Heriot-Watt University, and M J Blunt, SPE, Imperial College 2 M A CHRISTIE & M J BLUNT SPE 66599 At the fine geological model scale, the model is described on a regular cartesian grid. The model dimensions are 1200 x 2200 x 170 (ft). The top 70 ft (35 layers) represents the Tarbert formation, and the bottom 100 ft (50 layers) represents Upper Ness. The fine scale cell size is 20 ft x 10 ft x 2 ft. The fine scale model has 60 x 220 x 85 cells (1.122x10 cells). The porosity distribution is shown in Fig 3. The model consists of part of a Brent sequence. The model was originally generated for use in the PUNQ project. The vertical permeability of the model was altered from the original: originally the model had a uniform kv/kh across the whole domain. The model used here has a kv/kh of 0.3 in the channels, and a kv/kh of 10 -3 in the background. The top part of the model is a Tarbert formation, and is a representation of a prograding near shore environment. The lower part (Upper Ness) is fluvial. Participants and Methods Chevron Results were submitted for model 2 using CHEARS, Chevron’s in house reservoir simulator. They used the parallel version and the serial version for the fine grid model, and the serial version for the scaled-up model. Coats Engineering Inc Runs were submitted for both model 1 and model 2. The simulation results were generated using SENSOR. The simulator runs used the conventional 5or 7-point finite difference formulation, zero capillary pressure, and no directional relative permeability. GeoQuest A solution was submitted for model 2 only, with coarse grid runs performed using ECLIPSE 100. The full fine grid model was run using FRONTSIM, a streamline simulator, to check the accuracy of the upscaling. The coarse grid models were constructed using FloGrid, GeoQuest’s gridding and upscaling application. Landmark Landmark submitted entries for both model 1 and model 2 using the VIP simulator. The fine grid for model 2 was run using parallel VIP. Phillips Petroleum Solutions were submitted for both model 1 and model 2. The simulator used was SENSOR. Roxar Entries were submitted for both model 1 and model 2. The simulation results presented were generated using Roxar’s Black Oil, Implicit Simulator, Nextwell. The upscaled grid properties were generated using Roxar’s Geological Modelling software, RMS, in particular the RMSsimgrid option. Streamsim Streamsim submitted an entry for model 2 only. Simulations were run using 3DSL, a streamline based simulator. TotalFinaElf TotalFinaElf submitted a solution for model 2 only. The simulator used for the results presented was ECLIPSE; results were checked using the streamline code 3DSL. University of New South Wales The University of New South Wales submitted results for model 1 only using CMG’s IMEX simulator. Results Model 1 Fine Grid Solution All participants were able to compute the fine grid solution, and the solutions from the different simulators used were very close, as shown in Fig 4. The University of New South Wales fine grid solution departs slightly from the other fine grid solutions; it was not possible to track down the source of this discrepancy in the short time between receiving this solution and the paper submission deadline. Upscaled Solutions Participants were asked to generate solutions on a 5 x 5 grid, and on a grid of their choice with a maximum of 100 cells. The reason for the choice of the 5 x 5 grid was that, with that grid size, the coarse grid boundaries fall on high permeability streaks which is generally a problem for upscaling methods which don’t compute the fine grid solution. The solutions submitted for the 5 x 5 grid used single phase upscaling only (Roxar), or single phase upscaling plus regression based pseudoisation of relative permeabilities (Coats, Phillips, Landmark). The solutions with pseudo relative permeabilities are very close to the fine grid solution, and Roxar’s solution using only single phase upscaling shows a significant discrepancy (Fig 5). A second set of solutions was presented by some participants (shown in Fig 6). Here Roxar used single phase upscaling in conjunction with a streamline approach to generate local grid refinements (with a total of 96 cells) which captured the details of the flow in the early, mid, and late-time regions. Coats showed that good results could also be obtained with homogeneous absolute permeability and no alteration of relative permeability, and Phillips showed that good results could be obtained from a 6 x 2 grid. The University of New South Wales solution was based on a global upscaling and upgridding approach which attempts to minimize the variance of permeability within a cell. Their solution is close to their fine grid solution, although the difference between their fine grid solution and the other fine grid solutions tends to make their method appear to perform less well. Model 2 Fine Grid Solution Five participants provided fine grid results as well as an upscaled solution. Landmark and Chevron ran the full fine grid on a parallel reservoir simulator. GeoQuest and SPE 66599 TENTH SPE COMPARATIVE SOLUTION PROJECT: A COMPARISON OF UPSCALING TECHNIQUES 3 Streamsim provided results using streamline codes (TotalFinaElf also provided streamline results using 3DSL. We have not shown their production curves as they are the same as Streamsim’s). A comparison of the fine grid results is shown in Fig 7, 8, 9, 10. All the figures shows very good agreement between all four fine grid submissions. Although only producer 1 well plots are shown here for reasons of space, plots of the remaining well rates and watercuts show equally high levels of agreement between the four fine grid solutions. The differences that occur likely to be due to either different time steps early on, where the production rate is very sensitive to the transient pressure response, or to different treatment of the injection well, which was at the corner of four cells in the fine model, leading to different injectivity indices. Upscaled Solutions There were two methodologies used to generate the upscaled solutions. Some participants used finer scale information in some way, and then history matched a coarser grid to the finer grid results. Others made no use of fine sc

TraCI: an interface for coupling road traffic and network simulators

Abstract: Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to asses the usability of vehicular applications. In this article, we present Traffic Control Interface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns.In contrast to the existing approaches, i.e., generating mobility traces that are fed to a network simulator as static input files, the online coupling allows the adaptation of drivers' behavior during simulation runtime. This technique is not limited to a special traffic simulator or to a special network simulator. We introduce a general framework for controlling the mobility which is adaptable towards other research areas.We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.

Vehicular Mobility Simulation for VANETs

Abstract: During the last few years, continuous progresses in wireless communications have opened new research fields in computer networking, aimed at extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular traffic is attracting a growing attention from both academia and industry, due to the amount and importance of the related applications, ranging from road safety to traffic control, up to mobile entertainment. Vehicular ad-hoc networks (VANETs) are self-organized networks built up from moving vehicles, and are part of the broader class of mobile ad-hoc networks (MANETs). Because of their peculiar characteristics, VANETs require the definition of specific networking techniques, whose feasibility and performance are usually tested by means of simulation. One of the main challenges posed by VANETs simulations is the faithful characterization of vehicular mobility at both macroscopic and microscopic levels, leading to realistic non-uniform distributions of cars and velocity, and unique connectivity dynamics. In this paper, we first present and describe VanetMobiSim, a freely available generator of realistic vehicular movement traces for networks simulators. Then, VanetMobiSim is validated by illustrating how the interaction between featured macro- and micro-mobility is able to reproduce typical phenomena of vehicular traffic

Closed Loop Reservoir Management

Abstract: Closed-loop reservoir management is a combination of model-based optimization and data assimilation (computer-assisted history matching), also referred to as ‘real-time reservoir management’, ‘smart reservoir management’ or ‘closed-loop optimization’. The aim is to maximize reservoir performance, in terms of recovery or financial measures, over the life of the reservoir by changing reservoir management from a periodic to a near-continuous process. The key sources of inspiration for our work are measurement and control theory as used in the process industry and data assimilation techniques as used in meteorology and oceanography. We present results of a numerical example to illustrate the scope for closed-loop water flooding using real-time production data under uncertain reservoir conditions. The example concerns a 12-well water flood in a channelized reservoir. Optimization was performed using a reservoir simulator with functionality for adjoint-based life cycle optimization under rate and pressure constraints. Data assimilation was performed using the ensemble Kalman filter. Applying an optimization frequency of respectively once per 4 years, once per 2 years, once per year and once per 30 days resulted in an increase of net present value (NPV) with 6.68, 8.29, 8.30 and 8.71% compared to a conventional reactive control strategy. Moreover, the results for the 30-day cycle were very close (0.15% lower NPV) to those obtained by open-loop optimization using the ‘true’ reservoir model. We illustrate that for closed-loop reservoir management with a fixed well configuration, the use of considerably different reservoir models may lead to near-identical results in terms of NPV. This implies that in such cases the essential information may be represented with a much less complex model than suggested by the large number of grid blocks in typical reservoir models. We also illustrate that the optimal rates and pressures as obtained by openor closedloop optimization are often too irregular to be practically applicable. Fortunately, just as is the case for the data assimilation problem, the flooding optimization problem usually contains many more control variables than necessary, allowing for optimization of long-term reservoir performance while maintaining freedom to perform short-term production optimization. Introduction Our work aims at increased reservoir performance, in terms of recovery or financial measures, using a measurement and control approach to reservoir management. This idea has been around for many years in different forms, often centered around attempts to improve reservoir characterization from a geosciences perspective; see e.g. Chierici (1992). Moreover, recently ‘closed-loop’ or ‘real-time’ approaches to hydrocarbon production have received growing attention as part of various industry initiatives with names as ‘smart fields’, ‘i-fields’, ‘e-fields’, ‘self-learning reservoir management’ or ‘integrated operations’; see Jansen et al. (2005) for some further references. However, whereas the focus of most of these initiatives is primarily on optimization of short-term production, in our work we concentrate on life-cycle optimization, i.e. on processes at a timescale from years to tens of years. We perform reservoir flooding optimization, based on numerical simulation models, in combination with frequent model updating through data assimilation (computer-assisted history matching). This approach has lately also been referred to as ‘closed-loop reservoir modeling’ or ‘closed-loop production optimization’ and some recent references will be discussed below. In contrast to the geosciences-focused approach, we emphasize the need to focus on those elements of the modeling process that can both be verified from measurements and that bear relevance to controllable parameters such as well locations or, in particular, production parameter settings. The underlying hypothesis is that “It will be possible to significantly increase life-cycle value by changing reservoir management from a batch-type to a near-continuous model-based controlled activity.”