Improved Upscaling for Flow Simulation of Tight Gas Reservoir Models

Zhou, Yijie; King, Micháel J.

doi:10.2118/147355-ms

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…Porosity and permeability are two of the most important parameters required for reservoir modeling (Zhou and King 2011), well placement, and reservoir management (Zhou and King, 2013), etc. Both vary as a function of pore geometry, grain packing, grain shape, pore-filling solids, sorting, and any associated diagenetic facies.…”

Section: Literature Reviewmentioning

confidence: 99%

Petrophysics and rock physics modeling of diagenetically altered sandstone

Hossain¹,

Zhou²

2015

Interpretation

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We worked to establish relationships among porosity, permeability, resistivity, and elastic wave velocity of diagenetically altered sandstone. Many such relationships are documented in the literature; however, they do not consider diagenetic effects. Combining theoretical models with laboratory measured data, we derived mathematical relationships for porosity permeability, porosity velocity, porosity resistivity, permeability velocity, velocity resistivity, and resistivity permeability in diagenetically altered sandstone. The effects of clay and cementation were evaluated using introduced coefficients in these relationships. We found that clean sandstone could be modeled with Kozeny's relation; however, this relationship broke down for clay-bearing and diagenetically altered sandstone. Porosity is the first-order parameter that affects permeability, electrical, and elastic properties; clay and cement cause secondary effects on these properties. Rock physics modeling results revealed that cementation had a greater effect on elastic properties than electrical properties and clay had a larger effect on electrical properties than elastic properties. The relationships we provided can greatly help to determine permeability, resistivity, and velocity from porosity and to estimate permeability from resistivity and velocity as well as to determine resistivity from velocity measurements.

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Section: Literature Reviewmentioning

confidence: 99%

Petrophysics and rock physics modeling of diagenetically altered sandstone

Hossain¹,

Zhou²

2015

Interpretation

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“…These specific calculations were performed with the commercial Nexus™ simulator running with unstructured input. As reported previously (Zhou and King (2011)) the performance of an unstructured adaptive upscaling algorithm may be strongly dependent upon the choice of simulator, and/or simulator workflow.…”

Section: Onshore Us Tight Gas Examplementioning

confidence: 78%

“…To both achieve this kind of large scale pressure distribution and to correctly predict pressure and rate, plane source half-cell transmissibility upscaling was required. Once correctly upscaled, 85% of the cells have less than a 5 psi difference between the fine and coarse models, calculated at the end of field life (Zhou and King (2011)). This is a technical success, but the strong constraint of 1x1xN pillar based grid coarsening restricted the reduction in active cell count, and the corresponding reduction in CPU time to about a factor of 6.…”

Section: Onshore Us Tight Gas Examplementioning

confidence: 99%

Novel Diffuse Source Pressure Transient Upscaling

Nunna

Zhou

King

2015

SPE Reservoir Simulation Symposium

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Static high resolution three dimensional geological models are routinely constructed to provide an integrated description of a reservoir which includes seismic, well log, and core data, and which characterize the reservoir heterogeneity at multiple scales. These models also represent the structure and stratigraphy of the reservoir within the design of the modeling grid, which may include fault blocks, faults, pinch-outs, layering and cross-bedding. The growth of computational resources has remained rapid, and both geologic models and flow simulation models have increased in size. 50 million cell geologic models are routine, while simulation models are typically one or two orders of magnitude coarser. Hence upscaling of the geologic models for flow simulation remains part of the subsurface workflows.The industry also faces new reservoir engineering challenges. Unconventional reservoirs (tight gas / shale oil / shale gas) have sufficiently low permeabilities that the time for pressure transients are no longer measured in hours or days, but instead are measured in decades or longer. The separation between transient testing and steady state reservoir management is no longer applicable. Similarly, our upscaling algorithms have relied upon steady state concepts of flow, which may no longer be applicable.In the current study, a novel diffuse source transmissibility upscaling approach is described. It applies pressure transient concepts to the calculation of the effective transmissibility between reservoir simulation coarse cell pairs. Unlike the usual steady state upscaling algorithms, it is a completely local calculation and is not dependent upon knowledge of, or assumptions about, global reservoir flow patterns. It is well suited to performance prediction within unconventional reservoirs as it utilizes drainage volume concepts to the calculation of coarse cell average pressures, although its use is not restricted to unconventional reservoirs. The approach is tested using the conventional reservoir SPE10 waterflood data set. It is then validated at field scale using an onshore US tight gas reservoir model. The approach is shown to reduce simulation run times by up to two orders of magnitude without significant loss of accuracy in performance prediction.

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“…4 X/Y effective flow-based transmissibility computed using pressure/no-flow boundary conditions practically coincides with the sum of the fine-scale transmissibility, as highlighted by Zhou and King (2011). This is less true if some fine-scale cells act as a barrier diverting the flow along the vertical direction.…”

Section: Sampling Issues and Proportionalmentioning

confidence: 78%

Advanced Upscaling for Kashagan Reservoir Modeling

Panfili¹,

Cominelli²,

Calabrese³

et al. 2012

SPE Reservoir Evaluation &Amp; Engineering

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The Kashagan field is a huge carbonate formation located 4.5 km below the bottom of the North Caspian sea. The reservoir is saturated by overpressured light oil, and the development is based on first-contact-miscible gas injection.The reservoir is highly stratified, with a fine sequence of depositional cycles and long-range lateral correlations. Three porosity systems (matrix, karst, and fractures) can be organized in two main environments: a massive, low-permeability, matrix-like inner platform and a highly fractured/karstified rim.The reservoir geology is modeled by means of detailed geological grids consisting of tens of millions of cells, with vertical spacing of 1 m or even less to account for high-order depositional cycles. Geological grid cannot be used to run compositional simulations, and much-coarser grids, in which hundreds of geological layers are lumped in few tens of dynamic layers, are used by reservoir engineers. To minimize coarse-scale errors, an average lateral spacing of 250×250 m is used for both simulation and geological grid; nonetheless, upscaling remains a challenge. Traditional permeability (k*) upscaling methods, including flow-based methods, overestimate Kashagan field/wells production and injection potentials.We implemented a method in which the outcome of the upscaling is effective transmissibility (T*) instead of k*. T* upscaling has been proposed in the past as an alternative to k* upscaling, but it is neither part of commercial workflows nor widely accepted in the reservoir-modeling community. In our T* upscaling, the solution of local flow problems around coarse-cell interfaces is used to compute coarse transmissibility. T* and k* upscaling were compared by simulating both single-phase and gas-injection problems, including platform and rim, using the results of fine-scale simulation as a reference. We considered (1) single-porosity simulations with geological grid populated by only matrix (first medium) and karst+fracture (second medium) properties and (2) dual-porosity/ dual-permeability simulations encompassing both media. Contrary to k* upscaling, T*-based coarse simulations perfectly replicate fine-scale field and well injection/production potentials.Using T* upscaling as a cornerstone for company activities on Kashagan, we can run coarse-scale full-field simulations in a few hours without loss of consistency with the results provided by weeks-long, often unpractical, fine-scale simulations. On the contrary, the inaccuracy of k* upscaling would have required much finer and more computationally-expensive simulation grids together with the implementation of ad hoc multiphase upscaling.

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Improved Upscaling for Flow Simulation of Tight Gas Reservoir Models

Cited by 16 publications

References 34 publications

Petrophysics and rock physics modeling of diagenetically altered sandstone

Petrophysics and rock physics modeling of diagenetically altered sandstone

Novel Diffuse Source Pressure Transient Upscaling

Advanced Upscaling for Kashagan Reservoir Modeling

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