Summary Modern streamline-based reservoir simulators are able to account for actual field conditions such as 3D multiphase flow effects, reservoir heterogeneity, gravity, and changing well conditions. A streamline simulator was used to model four field cases, with approximately 400 wells and 150,000 gridblocks. History-match run times were approximately 1 CPU hour per run, with the final history matches completed in approximately 1 month per field. In all field cases, a high percentage of wells were history matched within the first two to three runs. Streamline simulation not only enables a rapid turnaround time for studies, but it also serves as a different tool in resolving each of the studied fields' unique characteristics. The primary reasons for faster history matching of permeability fields using 3D streamline technology as compared to conventional finite-difference (FD) techniques are as follows: Streamlines clearly identify which producer-injector pairs communicate strongly (flow visualization). Streamlines allow the use of a very large number of wells, thereby substantially reducing the uncertainty associated with outer-boundary conditions. Streamline flow paths indicate that idealized drainage patterns do not exist in real fields. It is therefore unrealistic to extract symmetric elements out of a full field. The speed and efficiency of the method allows the solution of fine-scale and/or full-field models with hundreds of wells. The streamline simulator honors the historical total fluid injection and production volumes exactly because there are no drawdown constraints for incompressible problems. The technology allows for easy identification of regions that require modifications to achieve a history match. Streamlines provide new flow information (i.e., well connectivity, drainage volumes, and well allocation factors) that cannot be derived from conventional simulation methods. Introduction In the past, streamline-based flow simulation was quite limited in its application to field data. Emanuel and Milliken1 showed how hybrid streamtube models were used to history match field data rapidly to arrive at both an updated geologic model and a current oil-saturation distribution for input to FD simulations. FD simulators were then used in forecast mode. Recent advances in streamline-based flow simulators have overcome many of the limitations of previous streamline and streamtube methods.2-6 Streamline-based simulators are now fully 3D and account for multiphase gravity and fluid mobility effects as well as compressibility effects. Another key improvement is that the simulator can now account for changing well conditions due to rate changes, infill drilling, producer-injector conversions, and well abandonments. With advances in streamline methods, the technique is rapidly becoming a common tool to assist in the modeling and forecasting of field cases. As this technology has matured, it is becoming available to a larger group of engineers and is no longer confined to research centers. Published case studies using streamline simulators are now appearing from a broad distribution of sources.7–12 Because of the increasing interest in this technology, our first intent in this paper is to outline a methodology for where and how streamline-based simulation fits in the reservoir engineering toolbox. Our second objective is to provide insight into why we think the method works so well in some cases. Finally, we will demonstrate the application of the technology to everyday field situations useful to mainstream exploitation or reservoir engineers, as opposed to specialized or research applications. The Streamline Simulation Method For a more detailed mathematical description of the streamline method, please refer to the Appendix and subsequent references. In brief, the streamline simulation method solves a 3D problem by decoupling it into a series of 1D problems, each one solved along a streamline. Unlike FD simulation, streamline simulation relies on transporting fluids along a dynamically changing streamline- based flow grid, as opposed to the underlying Cartesian grid. The result is that large timestep sizes can be taken without numerical instabilities, giving the streamline method a near-linear scaling in terms of CPU efficiency vs. model size.6 For very large models, streamline-based simulators can be one to two orders of magnitude faster than FD methods. The timestep size in streamline methods is not limited by a classic grid throughput (CFL) condition but by how far fluids can be transported along the current streamline grid before the streamlines need to be updated. Factors that influence this limit include nonlinear effects like mobility, gravity, and well rate changes.5 In real field displacements, historical well effects have a far greater impact on streamline-pattern changes than do mobility and gravity. Thus, the key is determining how much historical data can be upscaled without significantly impacting simulation results. For all cases considered here, 1-year timestep sizes were more than adequate to capture changes in historical data, gravity, and mobility effects. It is worth noting that upscaling historical data also would benefit run times for FD simulations. Where possible, both SL and FD methods would then require similar simulation times. However, only for very coarse grids and specific problems is it possible to take 1-year timestep sizes with FD methods. As the grid becomes finer, CFL limitations begin to dictate the timestep size, which is much smaller than is necessary to honor nonlinearities. This is why streamline methods exhibit larger speed-up factors over FD methods as the number of grid cells increases.

The reservoir in the central part of the Alberta portion of the Deep Basin Montney Trend yields the promise of a resource play that may be as extensive, productive and rich in liquids as the best portion of the Eagle Ford Shale in southern Texas. Various geological and petro-physical parameters are compared, along with initial production signatures to show the similarities with the liquids rich, Eagle Ford analog. Liquid and gas analyses are recombined at various, observed, condensate/gas ratios and subsequently input into a compositional simulation model. The extreme representations of these fluid characterizations, varying from rich gas to volatile oil, can be used to achieve comparable history matches of producing wells. The reservoir fluid grades from a rich gas (50 Bbl/MMcf condensate) to a light crude system (3,350 scf/Bbl), from west to east, with a coincident rise in elevation of 100 m. There are no ‘traps" holding the fluid in place other than the very low permeability of the reservoir. Large flow potential gradients exist from the westerly, down-dip, gas rich portion of the reservoir, toward the easterly more liquids rich region. This dynamic liquid on top of gas situation is upside down relative to conventional trapped hydrocarbon deposits. It is the result of dissipation of hydrocarbons away from their point of source and the fact that the catagenesis process converts source materials preferentially to methane with increased depth and temperature. This situation is known to occur in a number of deep basin, over pressured, mixed hydrocarbon deposits in North America including the Montney, the Eagle Ford and the Utica. Reservoir modeling is complicated by the need to initialize a model with lower density fluid underlying more dense fluid and with large potential gradients through the hydrocarbon column. This study illustrates a method used to establish the initial, dynamic as to geologic time, state of fluid distribution. It goes on to illustrate why conventional means of characterizing reservoir fluids are inappropriate due to the nature of the reservoir fluid distribution and, possibly, misleading. Fluids sampled from a point source, be it from a surface location or the bottom of a horizontal well’s vertical section, is not representative of the phase distribution along the entire horizontal well lateral. The 2,430 meter long, Montney horizontal lateral, located at 9–12–64–4W6M, straddles a transition zone that penetrates myriad hydrocarbon phases and compositions, the aggregate of which cannot be represented by a single phase envelope. These types of wells could be classified as either "Gas" or "Oil" if using conventional criteria, depending upon which liquid/gas ratio was used to recombine fluids. The usual, conventional, methods of classifying should therefore be discontinued for wells producing from deep-basin, over-pressured, mixed-hydrocarbon-saturated reservoirs. Data from a detailed core analysis (61m core), and various re-combined fluid analyses, are used to achieve a history match of initial production data from the liquids rich Montney well producing within the Kakwa field. The compositional simulation model is used to run sensitivities on 1) liquid yields and corresponding re-combined reservoir fluid, 2) permeability modifications to the hydraulic fracture and stimulated reservoir volume (affecting fracture conductivity), and 3) quantities of reservoir gas that has migrated up-structure from the source, if necessary, to achieve comparable history matches. The quantity of gas migration is controlled by invoking a miscible flood, 200 years prior to the beginning of well production and varying the permeability within high permeability streaks, or "fractures", to essentially replicate gas migration from the high temperature source in this unconventional reservoir. The "injector" and "producer" used at either end of the reservoir structure, in this model, are used to facilitate the movement of relatively small amounts of gas. The model is initialized, for the purpose of history matching (and forecasting), with a model that not only has a "fingering" distribution of phases and compositions, within the transition zone, but also represents the varying pressure gradients observed along the reservoir dip. Up-dip the pressure gradient approaches 10.5 kPa/m while the down-structure end of this reservoir yields gradients that exceed 13.5 kPa/m. The ultimate purpose of this study is to show what geological conditions prevail within this particular area of the Montney play and why they make this the ideal location for liquids rich gas production. It will show how much detail is required (or not) to generate a representative forecast model or type curve. It will also attempt to quantify error bars associated with parameters typically defined for this purpose, particularly those related to the range of solution gas/oil ratios (or liquid yields) that generate comparable history matches. And finally, this study will show what impact the characterization of reservoir fluids may have on well spacing and reservoir development plans.

Reservoir characterization and simulation modeling of naturally fractured reservoirs (NFRs) presents unique challenges that differentiate it from conventional, single porosity continuum reservoirs. Not only do the intrinsic characteristics of the fractures, as well as the matrix, have to be characterized, but the interaction between matrix and fractures must also be modeled accurately. Three field case studies have been evaluated combining the "forward" modeling approach, typically used by geoscientists, with "inverse" techniques, usually incorporated by reservoir engineers. The forward approach examines various causes of natural fractures and its' associated properties (e.g. fracture spacing, height, stress distribution, etc.) while the inverse approach focuses more on the effect created by the NFR (e.g. decline analysis, material balance, productivity, etc.). This study shows how a more powerful methodology is created, for the evaluation of naturally fractured reservoirs, when combining two techniques that have, historically, been applied in relative isolation.

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TX 75083-3836, U.S.A., fax 01-972-952-9435.Abstract A simple spreadsheet model has been developed to estimate Original Gas In Place (OGIP), layer productivity and recoverable reserves for wells with commingled production, completed in multi-layered tight gas reservoirs. Differentiating the productivity between multiple layers of contrasting permeability is old technology. This model, however, replicates the observed material balance trend while also honouring total well production data by varying layer properties. The P/Z trend of the higher permeability layers and lower permeability layers is mapped to "envelope" the productivity index (PI) weighted P/Z curve that is used to match historical data. This technique has been made applicable to the multi-layered reservoir environment by grouping the various kh terms, from all "high permeability" layers, into one model layer and all "low permeability" kh values into the "tighter" model layer. Published literature 1 has already shown that the generation of the layer P/Z curves is applicable for reservoirs with permeability in the range of 0.1 to 10 md. The model has been successfully applied to match and predict the productivity for various wells in Cooper Basin fields, with permeability in this range, and P/Z plots that exhibit curvature. Case studies show that any change in bottom hole pressure conditions (eg. compression) or skin (eg. stimulation) can also be accounted for in the model. Various simulation models have been generated to confirm this technique's applicability to wells in Australia's Cooper Basin, and to establish the PI weighting method.