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Collaborating Authors
Results
A Novel Upscaling Method for the In-Situ Conversion Process
Tan, Qizhi (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Ministry of Education) | Wang, Yanji (School of Petroleum Engineering, China University of Petroleum (East China)) | Li, Hangyu (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Ministry of Education) | Liu, Shuyang (School of Petroleum Engineering, China University of Petroleum (East China)) | Xu, Jianchun (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Ministry of Education)
Summary The simulation of the in-situ conversion process (ICP) is a challenging endeavor that involves complicated thermal-reactive- compositional coupling processes. The upscaling of ICP simulation has been investigated, but the developed methods have significant limitations, which hinder their use in complicated models. The most constraining limitation of previous ICP upscaling techniques is that they all require modifications of the simulation code, which make them difficult to use in closed-source commercial simulators. In this paper, we introduce a novel upscaling method for ICP simulation. In this new method, we introduce two correction factors (namely ฮฑ and ฮฒ in this paper) to adjust the coarse-scale reaction frequency factor and activation energy. The calculation of the two factors is based on the reactions on both coarse-scale and fine-scale models. The new upscaling method does not entail any additional modifications of the underlying simulation source code, and thus it is more feasible to implement. We demonstrate the accuracy and efficiency of our upscaling method with 2D and 3D models, respectively. Apart from model dimensions, the availability of the novel upscaling method for ICP simulations with different values of kinetic parameters is considered as well. It is shown that the novel upscaling method provides reasonably accurate results, and significant computational savings are also achieved.
- Geology > Geological Subdiscipline > Geomechanics (0.46)
- Geology > Rock Type > Sedimentary Rock (0.32)
The Application of Machine Learning Algorithm in Relative Permeability Upscaling for Oil-Water System
Wang, Yanji (School of Petroleum Engineering, China University of Petroleum, East China) | Li, Hangyu (School of Petroleum Engineering, China University of Petroleum, East China) | Tian, Ji (CNOOC Research Institute) | Fan, Ling (School of Petroleum Engineering, China University of Petroleum, East China) | Xu, Jianchun (School of Petroleum Engineering, China University of Petroleum, East China)
Abstract Traditional two-phase relative permeability upscaling requires the fine-scale two-phase flow simulation over the target regions/blocks. It can be very computationally expensive especially for cases with multiple (hundreds of) geological realizations (as commonly used in subsurface uncertainty quantification or optimization). In this paper, we develop a machine learning assisted relative permeability upscaling procedure, in which the full numerical upscaling is performed for only a portion of the coarse blocks, while the upscaled functions for the rest of the coarse blocks are calculated by the machine learning algorithm. The upscaling procedure was tested for generic (left to right) flow problems using 2D models for scenarios involving multiple realizations. Numerical results have shown that the coarse-scale simulation results using the newly developed machine learning assisted upscaling procedure are of similar accuracy to the coarse results using full numerical upscaling. Because the fine-scale numerical simulation is only performed for a small fraction of the model, significant speedup is achieved.
- North America > United States (0.28)
- Europe (0.28)
A Dual-Grid Method for the Upscaling of Solid-Based Thermal Reactive Flow, With Application to the In-Situ Conversion Process
Li, Hangyu (Stanford University) | Vink, Jeroen C. (Shell International Exploration and Production Incorporated) | Alpak, Faruk O. (Shell International Exploration and Production Incorporated)
Summary Thermal-reactive compositional-flow simulation in porous media is essential to model thermal-oil-recovery processes for extraheavy-hydrocarbon resources, and an example is the in-situ conversion process (ICP) developed by Shell for oil-shale production. Computational costs can be very high for such a complex system, which makes direct fine-scale simulations prohibitively time-consuming for large field-scale applications. This motivates the use of coarse grids for thermal-reactive compositional-flow simulation. However, significant errors are introduced by use of coarse-scale models without carefully computing the appropriate coarse parameters. In this paper, we develop an innovative dual-grid method to effectively capture the fine-scale reaction rates in coarse-scale ICP-simulation models. In our dual-grid method, coupled thermal-reactive compositional-flow equations are solved only on the coarse scale, with the kinetic parameters (frequency factors) calculated on the basis of fine-scale computations, such as temperature downscaling and fine-scale reaction-rate calculation. A dual-grid treatment for the heater-well model is also developed with coarse-scale heater-well indices calculated on the basis of fine-scale well results. The dual-grid heater-well treatment is able to provide accurate heater temperatures. The newly developed dual-grid method is applied to realistic cross-sectional ICP-pattern models with a vertical production well and multiple horizontal heater wells operated subject to fixed and time-varying heater powers. It is shown that the dual-grid model delivers results that are in close agreement with the fine-scale reference results for all quantities of interest. Despite the fact that the dual-grid method is implemented at the simulation-deck level, by use of the flexible scripting and monitor functionalities of our proprietary simulation package, significant computational improvements are achieved for all cases considered.
- North America > United States > Texas (0.67)
- North America > United States > California (0.46)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Colorado Field (0.99)
- North America > United States > Colorado > Piceance Basin > Piceance Creek Field (0.91)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
Summary Compositional flow simulation, which is required for modeling enhanced-oil-recovery (EOR) operations, can be very expensive computationally, particularly when the geological model is highly resolved. It is therefore difficult to apply computational procedures that require large numbers of flow simulations, such as optimization, for EOR processes. In this paper, we develop an accurate and robust upscaling procedure for oil/gas compositional flow simulation. The method requires a global fine-scale compositional simulation, from which we compute the required upscaled parameters and functions associated with each coarse-scale interface or wellblock. These include coarse-scale transmissibilities, upscaled relative permeability functions, and so-called ฮฑ-factors, which act to capture component flow rates in the oil and gas phases. Specialized near-well treatments for both injection and production wells are introduced. An iterative procedure for optimizing the ฮฑ-factors is incorporated to further improve coarse-model accuracy. The upscaling methodology is applied to two example cases, a 2D model with eight components and a 3D model with four components, with flow in both cases driven by wells arranged in a five-spot pattern. Numerical results demonstrate that the global compositional upscaling procedure consistently provides very accurate coarse results for both phase and component production rates, at both the field and well level. The robustness of the compositionally upscaled models is assessed by simulating cases with time-varying well bottomhole pressures that are significantly different from those used when the coarse model was constructed. The coarse models are shown to provide accurate predictions in these tests, indicating that the upscaled model is robust with respect to well settings. This suggests that one can use upscaled models generated from our procedure to mitigate computational demands in important applications such as well-control optimization.
- North America > United States > Texas > Permian Basin > Midland Basin > University Field > Wolfcamp Formation (0.98)
- North America > United States > Arkansas > Smart Field (0.98)
Summary Numerical modeling of the in-situ conversion process (ICP) is a challenging endeavor involving thermal multiphase flow, compositional pressure/volume/temperature (PVT) behavior, and chemical reactions that convert solid kerogen into light hydrocarbons, which are tightly coupled to temperature propagation. Our investigations of grid-resolution effects on the accuracy and performance of ICP simulations have demonstrated that ICP-simulation outcomesโspecifically, chemical-reaction rates, kerogen-accumulation profiles, and oil-/gas-production rates, may exhibit relatively large errors on coarse grids. Coarse grids are attractive because they deliver favorable computational performance. We have developed a novel multiscale modeling method for simulating ICP that reduces numerical-modeling errors and reproduces fine-scale-simulation results on relatively coarse grids. The method uses a two-scale solution method, in which the reaction kinetics of the solids is solved locally on a fine-scale grid, with interpolated temperatures obtained from coarse-grid simulations of thermal flow and fluid transport. We demonstrate the accuracy and efficiency of our multiscale method with representative 1D models. It is shown that the method delivers accurate solutions for key ICP performance indicators with very little computational overhead compared with corresponding coarse-scale models. The robustness of the multiscale method has been verified over a number of physical-parameter ranges with a limited-scope sensitivity study. Numerical results show that the multiscale method consistently improves the simulation results and matches the fine-scale reference results closely.
- Europe (0.93)
- North America > United States > Texas (0.28)
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
Abstract Compositional flow simulation, which is required for modeling enhanced oil recovery (EOR) operations, can be very expensive computationally, particularly when the geological model is highly resolved. It is therefore difficult to apply computational procedures that require large numbers of flow simulations, such as optimization, for EOR processes. In this paper we develop an accurate and robust upscaling procedure for compositional flow simulation. The method requires a global fine-scale compositional simulation, from which we compute the required upscaled parameters and functions associated with each coarse-scale interface or well block. These include coarse-scale transmissibilities, upscaled relative permeability functions, and so-called ฮฑ-factors, which act to capture component flow rates in the oil and gas phases. Specialized near-well treatments for both injection and production wells are introduced. An iterative procedure for optimizing the ฮฑ-factors is incorporated to further improve coarse-model accuracy. The upscaling methodology is applied to two example cases, a two-dimensional model with eight components and a threedimensional model with four components, with flow in both cases driven by wells arranged in a five-spot pattern. Numerical results demonstrate that the global compositional upscaling procedure consistently provides very accurate coarse results for both phase and component production rates, at both the field and well level. The robustness of the compositionally upscaled models is assessed by simulating cases with time-varying well bottom-hole pressures that are significantly different from those used when the coarse model was constructed. The coarse models are shown to provide accurate predictions in these tests, indicating that the upscaled model is robust with respect to well settings. This suggests that upscaled models generated using our procedure can be used to mitigate computational demands in important applications such as well control optimization.
Abstract Thermal-reactive-compositional flow simulation in porous media is essential to model unconventional thermal oil recovery processes for extra-heavy hydrocarbon resources, e.g., the In-situ Conversion Process (ICP) for oil-shale production. Computational costs can be very high for such a complex system, which may make simulation studies prohibitively time consuming for large field-scale applications on fine grids. On the other hand, significant errors are introduced with the use of coarse-scale models. In this paper, we developed an innovative multipoint multiscale modeling method to effectively capture the fine-scale reaction rates in coarse-scale ICP-simulation models. In our multiscale method, coupled thermal-reactive-compositional flow equations are solved only on the coarse-scale, with the kinetic parameters (frequency factors) calculated based on fine-scale reaction rates. We perform the temperature downscaling by solving the heat diffusion equation in local regions subject to temperature-gradient boundary conditions obtained from a multipoint evaluation on the coarse grid. A multiscale treatment is also developed for the heater well model. Coarse-scale heater well indices are calculated from fine-scale well models using downscaled temperatures. The newly developed multiscale method is applied to realistic cross-sectional ICP-pattern models with a vertical production well and multiple horizontal heater wells operated subject to a time-varying power. It is shown that the multiscale model delivers results that are in close agreement with the fine-scale reference results for all quantities of interest. Despite the fact that the multiscale method is implemented at the simulation-deck level, using the flexible scripting and monitor functionalities of our proprietary simulation package, significant computational improvements are achieved for all cases considered.
- North America > United States > Texas (0.93)
- North America > Canada (0.68)
Development and Application of Near-Well Multiphase Flow Upscaling for Forecasting of Heavy Oil Primary Production
Li, Hangyu (Stanford University) | Chen, Yuguang (Chevron Energy Technology Company) | Rojas, Danny (Chevron Energy Technology Company) | Kumar, Mridul (Chevron Energy Technology Company)
Copyright 2013, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Western Regional & AAPG Pacific Section Meeting, 2013 Joint Technical Conference held in Monterey, California, USA, 19 25 April 2013. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Near-well effects can have a strong impact on reservoir flow. Current reservoir modeling practice often uses coarse-scale flow simulation models, which may lead to biased results, compared with fine-scale models. In this work, we extend and apply a recently developed near-well multiphase flow upscaling technique to the coarse-scale simulation of heavy-oil primary production. For heavy oils, oil viscosity is a strong function of pressure when the pressure is below the bubble point. Therefore, the upscaled mobility functions (from near-well multiphase upscaling) depend on both pressure and saturation, which cannot be directly input to general purpose reservoir simulators. This is very different than the upscaled mobility functions for typical black-oil fluids, in which oil viscosity does not vary significantly with pressure. Accordingly, the upscaled mobility functions are often equivalent to upscaled relative permeabilities (as functions of saturation only). In this work, we develop two procedures to derive either the upscaled relative permeability or viscosity functions from the phase mobility functions, thus decoupling the dependency on pressure and saturation. It is found that the upscaled oil viscosity provides more accurate predictions than the upscaled relative permeabilities, especially at early time. This is because that the rapid change of pressure at early stage is captured sufficiently in the upscaled oil viscosity (as a function of pressure). The use of upscaled viscosity function in multiphase upscaling is new, and has not been presented in previous studies.