This paper revisits classic flood-surveillance methods applied to injection/production data and demonstrates how such methods can be improved with streamline-based calculations. Classic methods rely on fixed patterns and geometric-based well-rate allocation factors (WAFs). In this paper, we compare conclusions about pattern performance from classic surveillance calculations to conclusions about pattern performance from a streamline surveillance model using flow-based WAFs. We show that very different conclusions on pattern performance can be reached, depending on which approach is used. We introduce streamline-defined, timevarying injector-centered patterns as the basic pattern unit, with offset producers being those to which the injector is connected. Such patterns give a better measure of an injector's true effectiveness because of the improved estimation of offset oil production compared to fixed, predefined patterns.In the second part of this paper, we illustrate how to build a relevant streamline-based surveillance model. We compare WAFs and offset oil production computed from much more laborintensive, history-matched flow-simulation models to that from much simpler surveillance models and illustrate the difference with a field example. As long as offset-well rates are a function of neighboring-well rates-as is typical in many waterfloodscapturing first-order flow effects is sufficient to produce a surveillance model that is useful for reservoir-engineering purposes. Properly accounting for well locations, historical rates, gross geological bodies, and major flow barriers is generally sufficient to produce a useful surveillance model that replicates well pairs and total interwell fluxes that are similar to those of more-complex and more-expensive history-matched models. We believe that this similarity arises because historical well rates already mirror reservoir connectivity, and it is well rates that mainly impact how the streamlines connect well pairs.
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.
Significant incremental recovery can be obtained when vertical sweep is improved in the mature Phase I Miscible Flood area of the Virginia Hills Field. This study advances our knowledge of the gravity override processes operating in this complex carbonate reservoir. Furthermore it shows that horizontal injection wells improve sweep efficiency thereby alleviating the impact of solvent gravity override. As a result of this simulation, a successful horizontal injection well was drilled, with an actual 18 E3m3 incremental oil recovery to date and a predicted 65 E3m3 total incremental oil. The results of this study suggest redevelopment in mature EOR areas is expected to extend the life of the miscible flood and increase overall recovery. Introduction Miscible recovery in the Virginia Hills Field Hydrocarbon Miscible Flood (HCMF) project is significantly limited by poor vertical sweep of the solvent within individual layers of the reservoir. A vertically continuous interval within the reef margin (30 m thick) was investigated and shown to have a large bypassed tertiaryoil target. This study demonstrated that recovery could be significantly increased through the use of horizontal solvent injection wells. To have confidence in the results of the study it was critical to utilize geological and production history data to address the interwell extent of significant heterogeneities. This required complete integration of geological and engineering staff in all phases of the study. Background Historical The Virginia Hills Beaverhill Lake A Pool was discovered in 1957. The field (Figure 1) produces oil from a Mid-Upper Devonian limestone, stromatoporoid build-up. The field was unitized in 1963 and a waterflood pressure maintenance scheme was implemented. In the late 1980s the patternswere normalized to inverted nine spots and in 1989 a tertiary horizontal hydrocarbon miscible flood project was initiated in the south half of the Pool. The original oil in place (OOIP) for the pool has been calculated at 59.6 E6m3 by the operator. Cumulative oil production to the end of July 1996 was 22.9 E6m3, or 38.4﹪ of the OOIP. Current oil rates of 1,000 m3/d have fallen from peak rates of 4,400 m3/d. Field average water cut is greater than 90%. Within the EOR area, well spacing is 32 ha., approximately 400 m interwell distance. Geological The internal architecture of this limestone reservoir is multilayered and compartmentalized, due to several stages of carbonate accumulation. From the base up these stages are: S1 (equivalent to the regionally extensive Slave Point platform), S2A, S2, S3L, S3U, and S4, (herein called S-units) reaching a total maximum thickness of 150 m. A high energy windward margin exists along the eastern portion of the build-up with superior reservoir quality (an average range of.09 to.12 phi, and 20 to 120 mD permeability). For this study the S3L in the margin area of the build-up was investigated (Figure 1). Solvent Gravity Override Solvent gravity override (Figure 2) occurs when there is sufficient time for injected solvent to segregate prior to its withdrawal due to gravity differences and therefore a smaller miscible oil target is swept.
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