Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
As the demand for energy increases, more and more low-permeability reservoirs are being developed with the help of advancements in hydraulic fracturing and horizontal drilling technologies. Integrated reservoir modeling studies become increasingly important to understand and improve reservoir management of such reservoirs for optimum depletion planning. This paper presents the approach used for applying an integrated reservoir modeling workflow to the tight gas sands of the Cotton Valley reservoir. The main objective of the study is to understand well performance for horizontal infill wells with multiple hydraulic fractures. In order to accurately simulate gas flow in hydraulically fractured wells (HFW) in full-field or regional models of unconventional gas reservoirs with many HFW, it is critical to appropriately represent these wells in the simulation models. Various methods have been used in the industry to numerically simulate hydraulic fractures in large reservoir models. In this paper, we will also review these methods and show that almost all the methods require calibration with fine grid models in which the hydraulic fracture is explicitly gridded. For homogeneous models calibration is relatively easy, but it is almost impossible for heterogeneous models. Therefore, we used local grid refinement (LGR) as the solution for modeling hydraulically fractured wells in coarse grid simulation. Our approach is different than other LGR approaches presented in the literature in that we only refine the coarse gridblocks that contain the fractures and wells. Finally, since it is very time consuming to generate LGRs manually for models with many wells a software tool was developed to generate LGR gridding automatically for HFW for commercial simulators.
As the demand for energy increases, more and more low-permeability reservoirs are being developed with the help of advancements in hydraulic fracturing and horizontal drilling technologies. Integrated reservoir modeling studies become increasingly important to understand and improve reservoir management of such reservoirs for optimum depletion planning. This paper presents the approach used for applying an integrated reservoir modeling workflow to the tight gas sands of the Cotton Valley reservoir. The main objective of the study is to understand well performance for horizontal infill wells with multiple hydraulic fractures. In order to accurately simulate gas flow in hydraulically fractured wells (HFW) in full-field or regional models of unconventional gas reservoirs with many HFW, it is critical to appropriately represent these wells in the simulation models. Various methods have been used in the industry to numerically simulate hydraulic fractures in large reservoir models. In this paper, we will also review these methods and show that almost all the methods require calibration with fine grid models in which the hydraulic fracture is explicitly gridded. For homogeneous models calibration is relatively easy, but it is almost impossible for heterogeneous models. Therefore, we used local grid refinement (LGR) as the solution for modeling hydraulically fractured wells in coarse grid simulation. Our approach is different than other LGR approaches presented in the literature in that we only refine the coarse gridblocks that contain the fractures and wells. Finally, since it is very time consuming to generate LGRs manually for models with many wells a software tool was developed to generate LGR gridding automatically for HFW for commercial simulators.
Upscaling of a reservoir model is normally conducted when a high resolution model has been generated and it is practically impossible to run the simulation using a high resolution model. Reservoir model upscaling is done in order to have a reasonably coarse model for reservoir history matching and forecasting purposes without losing some of heterogeneity in the reservoir model. A high resolution static coal reservoir model (around 0.1 metre thickness) is generated based on the detailed single coal ply correlation in order to capture complexity and heterogeneity of the coal sediment. A case study is presented in this paper. Several upscaling methods for reservoir properties such as permeability and gas content were tested and analyzed. The upgridding process on the geocellular model mainly focused on vertical direction in order to identify which ply could be merged into the sub layer level. The next step identifies whether the ply can be merged with neighboring ply individually. The results of the upscaling and upgridding are analyzed using the combination of recovery ratio, simulation running time ratio and cell count ratio plot. Histogram and area map analysis is also used to evaluate the result of the upscaling work. The expected results of the upscaled model are lower running time, lower cell count and very close recovery value to the high resolution model, while still capturing the heterogeneity. The results of the upscaling exercise for this case are reduction of running time by 72%, reduction of the cell count by 64% and recovery difference of 3%. Some of the plies in one of the layers cannot be merged into a sub layer level which indicates a high degree of heterogeneity. This upscaling technique for the coal reservoir model provides more information about the reservoir characterization methodology for unconventional reservoirs, and coal bed methane reservoirs specifically.
In this study, various upscaling techniques and their effects on Barik tight gas Formation simulation modelling results were investigated. The intent of this upscaling study is to recommend coarse models that provide approximately the same flow behavior or well performance as the fine grid model. The study will help to develop an awareness of the range of applicability for the upscaled coarse models. It will also allow coarse grid models to be used appropriately and with greater confidence in production optimization. These techniques comprised several alternative ways of grouping reservoir data, with respect to petrophysical rock types (herein referred to as RT). This scheme defines 5 individual rock types, with RTs 1 to 4 broadly defining pay and RT 5 defining non-pay. The layers in the simulation models are made up of single or multiple grouped RTs from the same zone. Keeping each layer in the model, ordered as it was in the well log, resulted in 85 simulation layers for the fine grid model. The upscaled or coarse models have 16 to 35 simulation layers, with the smallest being the model where RTs 1 to 4 are grouped together. Different RTs sorting were tested in each of the upscaled models. The results of this study suggest that re-ordering the log information so that all of the rock for each rock type was grouped together inside each stratigraphic unit appears to be an acceptable upscaling technique that gives reasonable efficiency and accuracy. This type of upscaling was required since the study showed that any upscaling method that averaged the properties of RT 1 with the properties of other rock types within the same simulation layer could result in optimistic EUR estimates of up to 30% relative absolute error when compared to a fine grid model. The choice of upscaling method is particularly significant in a low Kv/Kh ratio environment, but less so within environments with a high Kv/Kh ratio. The upscaling technique has only been tested for the Barik formation and should not be used elsewhere without proper testing. The upscaling technique works for the Barik formation because of several distinctive features of the reservoir and the wells. In applying this upscaling technique we assume that: The wells are all hydraulically fractured and thus the flow is generally horizontal into the fractures and from the fractures into the well.The fracture extends from the top to the bottom of each stratigraphic unit that the fracture encounters. If a fracture only partially penetrates a stratigraphic unit, this method may not work.The gas is relatively dry and thus the flow is less affected by gravity than for reservoirs containg flowing liquids.The reservoir is made up of rock with a wide range of permeability, but the flow is dominated by the well connected, higher permeability rocks (RT 1).The reservoir is very stratified, and the correlation length of the rock types has to be very much greater than the well spacing. Re-ordering of the RTs in a given upscaling method will result in acceptable accurate estimates of hydrocarbon recovery, compared to the fine grid model. It showed that the order of layering did not matter for the area studied, because a conductive fracture connects all the layers. This method probably really is only applicable for dry gas reservoirs (where gravity is not important) and in fractured wells (where horizontal flow into the fractures and into wells dominates). In oil reservoirs and rich condensate reservoirs where gravity is an important factor, the ordering of the rock types within the simulation layer may matter. It will also be shown that an approximately 90% reduction in the simulation modelling computing time could be achieved if the appropriate upscaling technique is used. To achieve this reduction in computing time, some compromises were made, including assuming RT 4 is non-pay in upscaled models where RTs 4 & 5 are grouped together in the same simulation layers, resulting in reduction of HCPV. It is important to mention that some RT 5 zones in the log have thin instances of other rock types, which are not accounted for in the upscaled models and could result in an error in average pore volume preservation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.