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Simulation of reservoir flow processes at the finest scale is computationally expensive and in some cases impractical. Consequently, upscaling of several fine-scale grid blocks into fewer coarse-scale grids has become an integral part of reservoir simulation for most reservoirs. This is because as the number of grid blocks increases, the number of...
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... Therefore, despite the availability of unlimited computer resources, a reservoir analyst may have a larger impact on field performance by quickly history matching dozens of wells Energies 2020, 13, 1604 2 of 27 using a more economical upscaled model (coarser model) than by slowly history matching a single well using a higher accuracy fine-scale model. Upscaling is the process of reducing a large number of the fine-scale grid blocks of a geological model to a smaller number of coarse-scale grid blocks while retaining the underlying geological properties [1][2][3][4]. There are copious upscaling methods that include arithmetic, harmonic, geometric, power law, pressure solver, and weighted averages algorithms, where the choice depends on the rock property [4]. ...
... Upscaling is the process of reducing a large number of the fine-scale grid blocks of a geological model to a smaller number of coarse-scale grid blocks while retaining the underlying geological properties [1][2][3][4]. There are copious upscaling methods that include arithmetic, harmonic, geometric, power law, pressure solver, and weighted averages algorithms, where the choice depends on the rock property [4]. ...
... However, upscaling a fine-scale reservoir model must be performed with care. Excessive upscaling leads to loss of the detailed heterogeneities in the geologic model that directly affects the result accuracy [4]. ...
Optimal upscaling of a high-resolution static geologic model that reflects the flow performance of the reservoir is important for reasons such as time and calculation efficiency. In this study, we demonstrate that honoring reservoir heterogeneity is critical in predicting accurate production and reducing the time and cost of running reservoir flow simulations for the Hunton Group carbonate. We integrated three-dimensional (3D) seismic data, well logs, thin sections, multiscale fracture studies, discrete fracture networks, and geostatistical methods to create a 100 × 150 × 1 ft gridded representative geologic model. We calibrated our gridded porosity and permeability parameters, including the evaluation of fractures, by history matching the simulated production rate and cumulative production volumes from a baseline fine-scale model generated from petrophysical and production data obtained from five wells. We subsequently reperformed the simulations using a suite of coarser grids to validate our property upscaling workflow. Compared to our baseline history matching, increasing the horizontal grid cell sizes (i.e., horizontal upscaling) by factors of 2, 4, 8, and 16 results in cumulative production errors ranging from +0.5% for two time (2×) coarser to +74.22% for 16× coarser. The errors associated with vertical upscaling were significantly less, i.e., ranging from +0.5% for 2× coarser to +10.8% for 16× coarser. We observed higher production history matching errors associated with natural fracture size. Results indicate that greater connectivity provided by natural fracture length has a larger effect on production compared to the higher permeability provided by larger apertures. We also estimated the trade-off between accuracy and run times using two methods: (a) using progressively larger grid cell sizes; (b) applying 1, 5, 10, and 20 parallel processes. Computation time reduction in both scenarios may be described by simple power law equations. Observations made from our case study and upscaling workflow may be applicable to other carbonate reservoirs.
... Upscaling is the process of reducing a large number of the fine-scale grid blocks of a geological model to a smaller number of coarse-scale grid blocks while retaining the underlying geological properties (King et al., 1998;Stern, 2005;Ezekwe, 2010;Mehmood and Awotunde, 2016). The necessity of the upscaling process arises from the large size of geologic models with respect to the amount of computer processing capability. ...
... Upscaling a fine-scale reservoir model must be performed with care. Excessive upscaling will lead to loss of the detailed 70 heterogeneities in the geologic model that directly affect the accuracy of the results (Mehmood and Awotunde, 2016). Ma et al. (2013) found a 50% error in the simulated production volumes after upscaling. ...
... Meddaugh (2006) There are alternative method of upscaling including arithmetic, harmonic, geometric, power law, pressure solver, and weighted averages algorithms, where the choice depends on the rock property (Mehmood and Awotunde, 2016). While in general, arithmetic averaging works well for low-variability porosity and saturation properties, Deutsch (2017) finds simple arithmetic averages fail to upscale thin layers. ...
This study fully integrates multidisciplinary, multi-scalar subsurface and surface data for successful exploration and development programs in a fractured rock reservoir. Unconventional Sycamore/Meramec and conventional Hunton Carbonate plays in Oklahoma are the focus of this study. The following questions are addressed in this thesis: 1) what factors control natural fracture distributions, parameters, and their effect on fluid flow?, 2) what are the effects of geological upscaling on fluid flow simulations? and 3) what is the lithology and depositional environment of the Mississippian Sycamore/Meramec strata in the South Central Oklahoma Oil Province (SCOOP) area?
Hydrocarbon production in naturally fractured reservoirs vary because some areas are more prone to fracturing than others. Also, some fracture parameters are more important than others. To address these issues, multiscale data was used to build a realistic fracture model for fluid flow simulations. As a result, a generic fracture model was developed to predict the lithology and structure of the rocks as two main factors controlling fracture distributions. Grain supported rock and/or curvature were found to be more prone to fracturing than mud supported rock and/or negative curvature. Also, fracture length was found to have a greater influence on production response than aperture. The implication of these findings helps optimize landing well locations.
The upscaling process of geologic models can lead to losing fine-scale geological features, resulting in errors in production and reservoir performance predictions. To overcome this issue, the upscaling workflow was validated with history matching to find the optimal level of upscaling (OLU). OLU preserves the geological features and balance between simulation accuracy and simulation run time. As a result, horizontal upscaling larger than 100x150 ft results in increasing hydrocarbon production prediction errors. Logarithmic equations for different levels of upscaling were developed to define the production accuracy. Also, power law and lognormal relationships among grid cell size, computational simulation running time, and the number of processes were obtained. The implication of these findings can help predict the error in production if excessive upscaling is required. This workflow might be applied to other reservoirs to find optimal levels of upscaling.
Additionally, many operators in the oil industry have been actively exploring the Mississippian Sycamore/Meramec strata in southern Oklahoma. The optimum drilling locations are not well known because the depositional environment, lithology, and reservoir quality, are still not well understood. To shed light on these issues, comprehensive quantitive and qualitative field, lab, and machine learning studies were conducted on two outcrops and a subsurface well. The lithofacies from the outcrop and subsurface are identified, outcrop-to-subsurface correlation were determined, and the depositional environment was interpreted. Sediment gravity flows was interpreted as the process of transport and deposition. I suggest that the bioturbated shale and/or the sandy siltstone of the Sycamore rock types can be potential target zones due to their reservoir quality, lithology, bed continuity, and brittleness. The implication of this specific study can be of direct benefit to the exploration and development programs of many companies in the Ardmore Basin of South Central Oklahoma.
The computational cost of simulation on fine-scale reservoir models can be prohibitively expensive. While upscaling typically helps in reducing cost, it also results in a reduction in accuracy. Two-scale approaches such as dual mesh methods (DMM) have been developed over time in an attempt to reduce simulation cost while maintaining near fine-scale levels of accuracy and resolution. While these two-scale methods can be very effective in improving accuracy, they can be quite expensive as well. This paper presents two multimesh methods, namely, the Triple Mesh Method (TMM) and the Extended Triple Mesh Method (ETMM). Both methods involve introducing a third grid at an intermediate scale between the coarse and fine scales. These involve two levels of upscaling of the grid properties and two levels of downscaling the solutions to the flow equation. TMM involves two successive local downscaling steps of the flow solution from the coarsest to the finest mesh. ETMM on the other hand involves two successive extended local downscaling steps of the flow solution from the coarsest to the finest mesh. Each downscaling step in ETMM involves the application of ‘Directional Oversampling’ first introduced in the extended dual mesh method EDMM. These two methods along with DMM and EDMM methods were tested on different waterflooding problems and results compared with the coarse-scale and fine-scale solutions. The results show both ETMM and TMM to be effective in error reduction and also more cost effective than their respective dual mesh alternatives.
Modern geological models typically contain too many cells that make running reservoir simulations at times impractical or prohibitively costly. While upscaling helps with this problem, upscaled models typically suffer loss in accuracy. Different multiscale and dual mesh methods have been developed in an attempt to achieve fine-scale accuracy at reduced computational cost. This paper presents a dual-grid simulation method called Extended Dual Mesh Method (EDMM). EDMM is aimed at reducing homogenization and numerical dispersion errors inherent in upscaled models. In EDMM, velocities are first solved on the coarse scale. Fine-scale velocity fields are then computed by solving extended (oversampled) local flow problems with Neumann boundary conditions similar to Dual Mesh Method (DMM). These extended local problems are constructed such that local and global conservation is guaranteed. To achieve this, we propose a new concept of called Directional Oversampling (DO). DO ensures flux continuity between oversampled partitions therefore guaranteeing that the obtained global fine-scale velocity field is conservative. The resulting fine flux field is then used to compute fine-scale saturation. Also proposed is a new approach for calculating the coarse block interface transmissibilities using fine-scale-block mobilities thereby improving the coupling between the coarse-scale and fine-scale models. EDMM combines the simplicity of the DMM with the accuracy improvement of oversampling and is compatible with any upscaling technique. Examples were used to test the accuracy and robustness of EDMM and results were compared to the fine-scale, DMM and coarse-scale solutions. Results show that the method is a significant improvement on the DMM, much better than coarse grid results and comparable in quality to fine-scale solutions whilst maintaining speed comparable to DMM. Two error indicators, the water cut error and the breakthrough error, were employed in quantify accuracy of the different solution methods relative to the fine scale solution. The indicators show EDMM to be multiple times more accurate than DMM and in some examples orders of magnitudes more accurate. This work demonstrates EDMM to be accurate not only in predicting the water breakthrough at producer wells but also at predicting water-cut after breakthrough.
