Advances in reservoir characterization and modeling have given the industry improved ability to build detailed geological models of petroleum reservoirs. These models are characterized by complex shapes and structures with discontinuous material properties that span many orders of magnitude. Models that represent fractures explicitly as volumetric objects pose a particular challenge to standard simulation technology with regard to accuracy and computational efficiency. We present a new simulation approach based on streamlines in combination with a new multiscale mimetic pressure solver with improved capabilities for complex fractured reservoirs. The multiscale solver approximates the flux as a linear combination of numerically computed basis functions defined over a coarsened simulation grid consisting of collections of cells from the geological model. Here, we use a mimetic multipoint flux approximation to compute the multiscale basis functions. This method has limited sensitivity to grid distortions. The multiscale technology is very robust with respect to fine-scale models containing geological objects such as fractures and fracture corridors. The methodology is very flexible in the choice of the coarse grids introduced to reduce the computational cost of each pressure solve. This can have a large impact on iterative modeling workflows.

Advances in reservoir characterization and modeling have given the industry improved ability to build detailed geological models of petroleum reservoirs. These models are characterized by complex shapes and structures with discontinuous material properties that span many orders of magnitude. Models that represent fractures explicitly as volumetric objects pose a particular challenge to standard simulation technology with regard to accuracy and computational efficiency.We present a new simulation approach based on streamlines in combination with a new multiscale mimetic pressure solver with improved capabilities for complex fractured reservoirs. The multiscale solver approximates the flux as a linear combination of numerically computed basis functions defined over a coarsened simulation grid consisting of collections of cells from the geological model. Here, we use a mimetic multipoint-flux approximation to compute the multiscale-basis functions. This method has limited sensitivity to grid distortions. The multiscale technology is very robust with respect to fine-scale models containing geological objects such as fractures and fracture corridors. The methodology is very flexible in the choice of the coarse grids introduced to reduce the computational cost of each pressure solve. This can have a large impact on iterative modeling workflows.

A pattern flood management method based on a streamline simulator was developed to support waterflood designs. The methodology was applied on a structurally complex oil field in the North German basin. Studies are being conducted to understand the potential for extending the current waterflood in this oil field. The objective of this study was to investigate if a conventional simulation-based waterflood design could be enhanced using streamline simulation. An alternative to using streamline simulation could be the post-processing of streamlines based on outputs of a full-field finite difference (FD) simulation model. However, there are limitations to this approach, including robustness and time considerations, especially when multiple runs with field-scale reservoir models are required. The streamline simulator contains a pattern flood management algorithm designed for optimizing the performance of waterfloods using multiple value criteria. The algorithm continuously balances patterns during forecasting runs converging to optimal injection and production rates while honoring well and field production constraints. A unique set of pattern performance diagnostics are ancillary products, for example pattern efficiencies and leakage fractions. The full-field FD dynamic model of the aforementioned oil field was adapted for the streamline simulator. Both simulation models delivered similar results at the field and well levels and matched historical observed data satisfactorily. The best pattern flood model converged on a rate schedule that led to a 4% increase in oil production, a 17% decrease in water production, and a 5% reduction in the water injection volumes over the best performance achieved using a conventional voidage replacement strategy in the FD model. These findings were validated by executing the full-field model on a FD simulator with the recommendations from the pattern flood simulation run. The streamline simulation runs executed about seven times faster. To investigate the well count optimization potential, rigorous analyses were performed on the pattern information produced by the enhanced runs. A 12.5% reduction in well count, in terms of injectors and producers, could be achieved, and the pattern flood management algorithm converged on a rate schedule that still led to an increase of 2.3% in oil production, a 22% decrease in water production, and a 10% reduction in injection volumes. The streamline-based simulation study proved useful in improving the existing waterflood design. Speedup in runtime allowed ample investigations and analysis within a given time period. Detailed analysis of allocated rate schedules and pattern information across numerous forecast runs gave deeper insight on the problem. The study highlighted that any well pattern has associated with it an optimal rate-scheduling strategy. Hence, the two components are important aspects of any successful waterflood design. The recommended rate schedules are model based and hence subject to uncertainty, requiring updates as additional information becomes available over time.

A new implementation of a multiscale sequential fully implicit (MS SFI) reservoir simulation method is applied to a set of reservoir engineering problems to understand its utility. An assessment is made to highlight areas where the approach brings substantial advantage in performance as well as address problems not successfully resolved by existing methods. This work makes use of the first ever implementation of the multiscale sequential fully implicit method in a commercial reservoir simulator. The key features of the method and implementation are briefly discussed. The learnings gained during field testing and commercialization on about forty real world models is illustrated through simpler, but representative data sets, available in the public domain. The workhorse robust fully implicit (FI) method is used as a reference for benchmarking. The MS SFI method can faithfully reproduce FI results for black oil problems. We conclude that the MS SFI method has the capability to support reservoir engineering decision making especially in the areas of subsurface uncertainty quantification, representative model selection, model calibration and optimization. The MS SFI method shows immense potential for handling prominent levels of reservoir heterogeneity. The challenge of including fine-scale heterogeneity, which is often overlooked, when scaling up EOR processes from laboratory to field, appears to have found a practical solution with a combination of MS SFI and high-performance computing (HPC).

Summary Desktop computing is undergoing a revolution with parallel processing on workstations. Parallel streamline simulators have been developed for shared memory architecture systems. In this paper, we discuss the implementation and performance analysis of a shared memory architecture commercial parallel streamline simulator based on native threading technology for both Windows® and Linux® operating systems. In general the streamline simulation algorithm is relatively straightforward to parallelize, however, there are several challenges that require special attention in order to avoid computing bottlenecks and inconsistent results. Repeatability of parallel simulation results is a well-known challenge. A data-accumulation scheduling algorithm designed to ensure repeatable results independent of the number of processing units has been implemented. The algorithm is supplemented by an efficient loadbalancing algorithm, to minimize processor idle time. Parallel scalability for various model characteristics and streamline solver options is analyzed. We have observed almost linear scalability up to 10 threads and a speed up factor of three to seven for simulation runs using a 1D explicit upwind finite difference solver to solve the transport problem along streamlines. The front tracking solver is inherently fast and does not gain significantly from parallelization. The run time of a serial simulation using the front-tracking solver is less than that of a parallel explicit solver run using up to 12 threads. The memory consumption of the front tracker is slightly higher than the explicit solver but overall streamline simulations are very memory efficient, enabling large geo-scale models to be simulated efficiently on 64-bit workstations. Complex three-phase blackoil simulation problems require a larger number of timesteps steps and speedup degrades as run time becomes dependent on the efficiency of the pressure solver. The results produced by our parallel streamline simulator are repeatable for any number of threads, and the algorithm used to achieve repeatability has little impact on performance.