6,479 results on '"Reservoir simulation"'
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2. Assessment of core samples through the analysis of CT measurements and its implications for CO2 sequestration potential in a Hungarian depleted oil field
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Veres, Gábor Pál, Földes, Tamás, and Szunyog, István
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- 2024
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Catalog
3. Underpinnings of reservoir and techno-economic analysis for Himalayan and Son-Narmada-Tapti geothermal sites of India
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Kiran, Raj, Upadhyay, Rajeev, Rajak, Vinay Kumar, Kumar, Ashutosh, and Datta Gupta, Saurabh
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- 2024
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4. CO2 trapping dynamics in tight sandstone: Insights into trapping mechanisms in Mae Moh's reservoir
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Ramadhan, Romal, Tapanya, Chetsada, Akamine, Thakheru, Leelasukseree, Cheowchan, and Tangparitkul, Suparit
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- 2024
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5. On the feasibility of an ensemble multi-fidelity neural network for fast data assimilation for subsurface flow in porous media
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Wang, Yating and Yan, Bicheng
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- 2025
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6. Reproduction of channel stacking patterns in geomodeling: Metrics and impact of the modeling strategy on reservoir flow behavior
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Scarpa, Enrico, Collon, Pauline, Panfilova, Irina, and Caumon, Guillaume
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- 2025
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7. Enhancing interpretability of AI models in reservoir operation simulation: Exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks
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Mahmoud, Ali, Hu, Tiesong, Jing, Peiran, Liu, Yong, Li, Xiang, and Wang, Xin
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- 2024
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8. Applying a deep-learning surrogate model to simulate and compare achievable oil recovery by different waterflood scenarios.
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Singh, Vishal, Ruwali, Nabindra, Pandey, Rakesh Kumar, Vaferi, Behzad, and Wood, David A.
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ARTIFICIAL neural networks , *LONG short-term memory , *DEEP learning , *SUPPORT vector machines , *MACHINE learning - Abstract
Reservoir pressure depletion is a critical aspect leading to declined oil production. Water flooding becomes mandatory to increase the recovery efficiency from certain reservoirs. Establishing well placements, injection rates, and injection schedules enables improved fluid displacement in such reservoirs. Standard reservoir simulators are widely used to provide near-optimal scenarios for maximizing oil recovery. Such reservoir simulations involved numerous iterations and required significant computational effort. Also, the conventional machine learning methods provide a relatively high uncertainty to predict the considered problem. In this context, a deep-learning surrogate model comprising long short-term memory networks and densely connected networks is proposed to predict cumulative oil production using various waterflood injection well patterns, injection rates, and injection period information. The relevance test clarifies that the oil recovery mainly controls by water injection duration and injection pattern. A total of 1485 data records are considered to validate the model's performance, demonstrating less than 3.5% absolute average relative error and a prediction versus actual value regression coefficient of 0.99989. In addition, the prediction accuracy of the proposed deep-learning model is also shown to outperform two well-known traditional machine-learning models (i.e. artificial neural networks and support vector regressions). [ABSTRACT FROM AUTHOR] more...
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- 2024
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9. A modified Flux Corrected Transport method coupled with the MPFA-H formulation for the numerical simulation of two-phase flows in petroleum reservoirs using 2D unstructured meshes.
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da Silva, Phillipe C. G., Pacheco, Gustavo L. S. S., Albuquerque, Pedro V. P., Souza, Márcio R. A., Contreras, Fernando R. L., Lyra, Paulo R. M., and Carvalho, Darlan K. E.
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FLOW simulations , *COMPUTATIONAL mathematics , *TWO-phase flow , *PETROLEUM reservoirs , *ANISOTROPY - Abstract
The numerical simulation of multiphase and multicomponent flows in oil reservoirs is a significant challenge, demanding robust and computationally efficient numerical formulations. Particularly, scenarios with high mobility ratios between injected and resident fluids can lead to Grid Orientation Effects (GOE), where numerical solutions strongly depend on the alignment between flow and computational grid and mobility ratio. This phenomenon relates to an anisotropic distribution in truncation error tied to the numerical approximation of the transport term. Although the oil industry commonly uses linear Two Point Flux Approximation (TPFA) for diffusive fluxes and the First Order Upwind (FOU) method for advective fluxes, both lack rotational invariance and TPFA struggles with non-k-orthogonal grids. This paper proposes a comprehensive cell-centered finite-volume formulation to simulate reservoir oil-water displacements, integrating the classical IMPES (Implicit Pressure Explicit Saturation) segregate approach with unstructured, non-k-orthogonal meshes. Diffusive flux discretization employs a Multipoint Flux Approximation with Harmonic Points (MPFA-H), capable of handling heterogeneous and strongly anisotropic media. A modified second-order Flux Corrected Transport (FCT) approach curbs artificial numerical diffusion for transport term discretization. Additionally, we incorporate a Flow-Oriented Scheme (FOS) for computing low-order and high-order approximations of the numerical fluxes to enhance multidimensional approximation and reduce GOE. The proposed strategy is validated through benchmark problems, yielding precise outcomes with reduced numerical diffusion and GOE effects, underscoring its efficiency for complex reservoir flow simulations. [ABSTRACT FROM AUTHOR] more...
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- 2024
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10. Determining optimal controls placed on injection/production wells during waterflooding in heterogeneous oil reservoirs using artificial neural network models and multi-objective genetic algorithm.
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Nwanwe, Onyebuchi Ivan, Izuwa, Nkemakolam Chinedu, Ohia, Nnaemeka Princewill, Kerunwa, Anthony, and Uwaezuoke, Nnaemeka
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ARTIFICIAL neural networks , *OPTIMIZATION algorithms , *STANDARD deviations , *PETROLEUM reservoirs , *STATISTICAL errors , *RESERVOIRS , *OIL field flooding - Abstract
The objective of this study is to propose a computationally inexpensive and effective approach that addresses the challenges faced with computationally expensive and time-consuming trial-and-error and direct optimization methods in well-control optimization. This approach involves combining proxy models such as artificial neural network (ANN) models with optimization algorithms to determine an optimal solution much faster. It was implemented in a heterogeneous oil reservoir undergoing waterflooding. The controllable parameters of the reservoir simulation model were identified as bottom-hole pressure for the producers and water injection rate for the injectors. Minimum and maximum values of each input parameter were defined based on reservoir conditions and used with a Box Behnken design (BBD) method to generate realizations for conducting reservoir simulations to obtain cumulative oil produced (COP) and cumulative water produced (CWP). The input and output data were normalized before being used for model development such that 70:15:15% of data was used for training, validation, testing, and all of the ANN model in which a coefficient of correlation (R) of 0.99756, 0.94354, 0.95813, and 0.98589 were obtained respectively. This indicates the accuracy, validity, and reliability of the model. The coefficient of determination (R2) for training, validation, testing, and all datasets as well as statistical error and trend analysis were used to validate the model. R2 values for each case were not less than 0.80, and the responses were reproduced by the ANN model with average relative error and root mean square error of not more than 0.7%. Weights and biases were extracted from the trained and validated ANN model to aid in outputting a visible ANN model that can be used for optimization studies. A multi-objective genetic algorithm was used to determine an optimal solution that maximized COP and minimized CWP. Average and optimized input data were used to run the developed ANN model. Results revealed that the optimized case outperformed the case for which average input values were used evidenced by the production of 34.198 MSm3 more oil and 14.297 MMSm3 less water. The findings of this study showed that using an ANN-MOGA approach will eliminate the computationally expensive, time-consuming, and inefficient trial-and-error approach for well-control optimization. Oil recovery was improved while water production was reduced resulting in low expenditure on treatment and disposal of produced water. [ABSTRACT FROM AUTHOR] more...
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- 2024
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11. Less-Intrusive Consistent Discretization Methods for Reservoir Simulation on Cut-cell Grids – Algorithms, Implementation, and Testing.
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Alpak, Faruk O., Jammoul, Mohamad, Wheeler, Mary F., and Onyeagoro, Kachi
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DISCRETIZATION methods , *CONCEPTUAL models , *SIMULATION methods & models , *ALGORITHMS - Abstract
Consistent discretization methods are a natural fit for the novel cut-cell gridding technique for reservoir simulation, which preserves the orthogonality characteristic in the lateral direction. Both uniform (global) and novel hybrid (local) variants of consistent discretization methods are implemented and tested in the vicinity of fault representations on cut-cell grids. Novel consistent discretization methods, which do not require major intrusive changes to the solver structure of industrial-grade reservoir simulators, are investigated in this work. Cell-centered methods such as multi-point flux approximation (MPFA), average multi-point flux approximation (AvgMPFA), and nonlinear two-point flux approximation (NTPFA) methods fit naturally into the framework of existing industrial-grade simulators. Thus, cut-cell compatible variants of AvgMPFA and NTPFA and their novel hybridizations with TPFA are implemented and tested. An implementation of the relatively more computationally expensive MPFA is also made to serve as accuracy reference to AvgMPFA and NTPFA. AvgMPFA and NTPFA multiphase simulation results are compared in terms of accuracy and computational performance against the ones computed with reference MPFA and TPFA methods on a set of synthetic cut-cell grid models of varying complexity including conceptual models and a field-scale model. It is observed that AvgMPFA consistently yields more accurate and computationally efficient simulations than NTPFA on cut-cell grids. Moreover, AvgMPFA-TPFA hybrids run faster than NTPFA-TPFA hybrids when compared on the same problem for the same hybridization strategy. On the other hand, the computational performance of AvgMPFA degrades more rapidly compared to NTPFA with increasing "rings" of orthogonal blocks around cut-cells. Auspiciously, only one or two "rings" of orthogonal blocks around cut cells are sufficient for AvgMPFA to deliver high accuracy. [ABSTRACT FROM AUTHOR] more...
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- 2024
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12. A Comprehensive Review of Biogeochemical Modeling of Underground Hydrogen Storage: A Step Forward in Achieving a Multi-Scale Approach.
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Vasile, Nicolò Santi
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This paper presents an in-depth investigation of the biogeochemical modeling approaches applied to underground hydrogen storage. It delves into the intricate dynamics of hydrogen in the subsurface, focusing on small (pore-lab scale) and reservoir-scale models, highlighting the importance of capturing microbial, geochemical, and fluid flow dynamic interactions in porous media to simulate storage performance accurately. Small-scale models offer detailed insights into localized phenomena, such as microbial hydrogen consumption and mineral reactions, and can be verified and calibrated against laboratory data. Conversely, large-scale models are essential to assess the feasibility of a project and forecast the storage performance, but cannot be proven by real data yet. This work addresses the challenge of transitioning from fine-scale to reservoir models, integrating spatial heterogeneity and long-term dynamics while retaining biogeochemical complexity. Through the use of several simulation tools, like PHREEQC, Comsol, DuMuX, Eclipse, CMG-GEM, and others, this study explores how modeling approaches are evolving to incorporate multiphysics processes and biochemical feedback loops, which are essential for predicting hydrogen retention, flow, and potential risks. The findings highlight the strengths and limitations of current modeling techniques and suggest a workflow for exploiting at best existing modeling capabilities and developing reservoir models to support hydrogen storage appraisal and management. [ABSTRACT FROM AUTHOR] more...
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- 2024
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13. Retrograde Condensation in Gas Reservoirs from Microporous to Field-Scale Simulation.
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Canova, Manoela Dutra, Barbosa Machado, Marcos Vitor, and Carvalho, Marcio da Silveira
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GAS condensate reservoirs ,GAS reservoirs ,FLOW velocity ,INTERFACIAL tension ,FLOW simulations - Abstract
Hydrocarbon fields that contain non-associated gas, such as gas condensate, are highly valuable in terms of production. They yield significant amounts of condensate alongside the gas, but their unique behavior presents challenges. These reservoirs experience constant changes in composition and phases during production, which can lead to condensate blockage near wells. This blockage forms condensate bridges that hinder flow and potentially decrease gas production. To address these challenges, engineers rely on numerical simulation as a crucial tool to determine the most effective project management strategy for producing these reservoirs. In particular, relative permeability curves are used in these simulations to represent the physical phenomenon of interest. However, the representativeness of these curves in industry laboratory tests has limitations. To obtain more accurate inputs, simulations at the pore network level are performed. These simulations incorporate models that consider alterations in interfacial tension and flow velocity throughout the reservoir. The validation process involves reproducing a pore network flow simulation as close as possible to a commercial finite difference simulation. A scale-up methodology is then proposed, utilizing an optimization process to ensure fidelity to the original relative permeability curve at a microporous scale. This curve is obtained by simulating the condensation process in the reservoir phenomenologically, using a model that captures the dependence on velocity. To evaluate the effectiveness of the proposed methodology, three relative permeability curves are compared based on field-scale productivities and the evolution of condensate saturation near the wells. The results demonstrate that the methodology accurately captures the influence of condensation on well productivity compared to the relative permeability curve generated from laboratory tests, which assumes greater condensate mobility. This highlights the importance of incorporating more realistic inputs into numerical simulations to improve decision-making in project management strategies for reservoir development. [ABSTRACT FROM AUTHOR] more...
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- 2024
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14. History Matching Reservoir Models With Many Objective Bayesian Optimization.
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Samoil, Steven, Fare, Clyde, Jordan, Kirk E., and Chen, Zhangxin
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LARGE scale systems ,HYBRID cloud computing ,GRID cells - Abstract
Reservoir models for predicting subsurface fluid and rock behaviors can now include upwards of billions (and potentially trillions) of grid cells and are pushing the limits of computational resources. History matching, where models are updated to match existing historical data more closely, is conducted to reduce the number of simulation runs and is one of the primary time‐consuming tasks. As models get larger the number of parameters to match increases, and the number of objective functions increases, and traditional methods start to reach their limitations. To solve this, we propose the use of Bayesian optimization (BO) in a hybrid cloud framework. BO iteratively searches for an optimal solution in the simulations campaign through the refinement of a set of priors initialized with a set of simulation results. The current simulation platform implements grid management and a suite of linear solvers to perform the simulation on large scale distributed‐memory systems. Our early results using the hybrid cloud implementation shown here are encouraging on tasks requiring over 100 objective functions, and we propose integrating BO as a built‐in module to efficiently iterate to find an optimal history match of production data in a single package platform. This paper reports on the development of the hybrid cloud BO based history matching framework and the initial results of the application to reservoir history matching. [ABSTRACT FROM AUTHOR] more...
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- 2024
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15. Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir.
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Bui, Dung, Koray, Abdul-Muaizz, Appiah Kubi, Emmanuel, Amosu, Adewale, and Ampomah, William
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ARTIFICIAL neural networks ,MACHINE learning ,PARTICLE swarm optimization ,INJECTION wells ,GEOLOGICAL modeling - Abstract
This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. An approach leveraging a machine learning workflow for reservoir characterization, history matching, sensitivity analysis, field development and optimization was proposed to accomplish the above goal. A 3D subsurface model representing studied sand-shale sequences was constructed based on geophysical and petrophysical logs, core measurements, and advanced machine learning techniques. After that, a robust sensitivity analysis and history matching process were conducted using a machine learning workflow. The most sensitive control variables were the aquifer properties, permeability heterogeneity in different directions, and water–oil contacts. The history matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. Several field development scenarios were generated using the validated model to optimize cumulative oil recovery. Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. Following the detailed proposed machine learning-based workflow, one can effectively decide on the development strategy and apply the findings from this research to their field. [ABSTRACT FROM AUTHOR] more...
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- 2024
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16. Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques.
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Salasakar, Sameer, Prakash, Sabyasachi, and Thakur, Ganesh
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ARTIFICIAL neural networks ,MACHINE learning ,PROPER orthogonal decomposition ,OPTIMIZATION algorithms ,NET present value - Abstract
Well placement optimization refers to the identification of optimal locations for wells (producers and injectors) to maximize net present value (NPV) and oil recovery. It is a complex challenge in all phases of production (primary, secondary and tertiary) of a reservoir. Reservoir simulation is primarily used to solve this intricate task by analyzing numerous scenarios with varied well locations to determine the optimum location that maximizes the targeted objective functions (e.g., NPV and oil recovery). Proxy models are a computationally less expensive alternative to traditional reservoir simulation techniques since they approximate complex simulations with simpler models. Previous review papers have focused on analyzing various optimization algorithms and techniques for well placement. This article explores various types of proxy models that are the most suitable for well placement optimization due their discrete and nonlinear natures and focuses on recent advances in the area. Proxy models in this article are sub-divided into two primary classes, namely data-driven models and reduced order models (ROMs). The data-driven models include statistical- and machine learning (ML)-based approximations of nonlinear problems. The second class, i.e., a ROM, uses proper orthogonal decomposition (POD) methods to reduce the dimensionality of the problem. This paper introduces various subcategories within these two proxy model classes and presents the successful applications from the well placement optimization literature. Finally, the potential of integrating a data-driven approach with ROM techniques to develop more computationally efficient proxy models for well placement optimization is also discussed. This article is intended to serve as a comprehensive review of the latest proxy model techniques for the well placement optimization problem. In conclusion, while proxy models have their own challenges, their ability to significantly reduce the complexity of the well placement optimization process for huge reservoir simulation areas makes them extremely appealing. With active research and development occurring in this area, proxy models are poised to play an increasingly central role in oil and gas well placement optimization. [ABSTRACT FROM AUTHOR] more...
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- 2024
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17. Infill Well Placement Optimization for Polymer Flooding in Offshore Oil Reservoirs via an Improved Archimedes Optimization Algorithm with a Halton Sequence.
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Tang, Engao, Zhang, Jian, Xia, Anlong, Jin, Yi, Li, Lezhong, Chen, Jinju, Hu, Biqin, and Sun, Xiaofei
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POLYMER flooding (Petroleum engineering) , *OPTIMIZATION algorithms , *PETROLEUM reservoirs , *NET present value , *PETROLEUM industry - Abstract
Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved Archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimization algorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 × 108, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement. [ABSTRACT FROM AUTHOR] more...
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- 2024
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18. Application of Analytical Solutions of the Reactive Transport Equation for Underground Methanation Reactors.
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Hagemann, Birger, Hogeweg, Sebastian, and Strobel, Gion
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TRANSPORT equation ,MOLE fraction ,HYDROGEN storage ,UNDERGROUND storage ,CHEMICAL reactions - Abstract
Fluctuations in the production of renewable-based electricity have to be compensated by converting and storing the energy for later use. Underground methanation reactors (UMR) are a promising technology to address this issue. The idea is to create a controlled bio-reactor system in a porous underground formation, where hydrogen obtained from renewable energy sources by electrolysis and carbon dioxide from industrial sources are fed into the reactor and converted into methane. Microorganisms, known as methanogenic archaea, catalyze the chemical reaction by using the two non-organic substrates as nutrients for their growth and for their respiratory metabolism. The generated synthetic methane is renewable and capable to compete with the fossil methane. Mathematical models play an important role in the design and planning of such systems. Usually, a numerical solution of the model is required since complex initial-boundary problems cannot be solved analytically. In this paper, an existing bio-reactive transport model for UMR is simplified to such an extent that an analytical solution of the advection-dispersion-reaction equation can be applied. A second analytical solution is used for the case without dispersion. The analytical solutions are shown for both the educt (hydrogen) and the reaction product (methane). In order to examine the applicability of the analytical models, they are compared with the significantly more complex numerical model for a 1D case and a 3D case. It was shown that there is an acceptable agreement between the two analytical solutions and the numerical solution in different spatial plots of hydrogen and methane concentration and in the methane concentration in the withdrawn gas. The mean absolute error in the mole fraction is well below 0.015 in most cases. The spatial distribution of the hydrogen concentration in the comparison to the 3D case shows a higher deviation with a mean absolute error of approx. 0.023. As expected, the model with dispersion shows a slightly lower error in all cases, as only here the gas mixing resulting in smeared displacement fronts can be represented. It is shown that analytical modeling is a good tool to get a first estimation of the behavior of an UMR. It allows to help in the design of well spacing in combination with the injection rate and injected gas composition. Nevertheless, it is recommended to use more complex models for the later detailed analysis, which require a numerical solution. [ABSTRACT FROM AUTHOR] more...
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- 2024
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19. Phase Behavior and Rational Development Mode of a Fractured Gas Condensate Reservoir with High Pressure and Temperature: A Case Study of the Bozi 3 Block.
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Zhang, Yongling, Tang, Yangang, Shi, Juntai, Dai, Haoxiang, Jia, Xinfeng, Feng, Ge, Yang, Bo, and Li, Wenbin
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GAS injection , *GAS condensate reservoirs , *WATER temperature , *HIGH temperatures , *CRITICAL temperature , *CARBON dioxide - Abstract
The Bozi 3 reservoir is an ultra-deep condensate reservoir (−7800 m) with a high temperature (138.24 °C) and high pressure (104.78 MPa), leading to complex phase behaviors. Few PVT studies could be referred in the literature to meet such high temperature and pressure conditions. Furthermore, it is questionable regarding the applicability of existing condensate production techniques to such a high temperature and pressure reservoir. This study first characterized the phase behavior via PVT experiments and EOS tuning. The operating conditions were then optimized through reservoir numerical simulation. Results showed that: (1) the critical condensate temperature and pressure of Bozi 3 condensate gas were 326.24 °C and 43.83 MPa, respectively; (2) four gases (methane, recycled dry gas, carbon dioxide, and nitrogen) were analyzed, and methane was identified as the optimal injection gas; (3) gas injection started when the production began to fall and achieved higher recovery than gas injection started when the pressure fell below the dew-point pressure; (4) simultaneous injection of methane at both the upper and lower parts of the reservoir can effectively produce condensate oil over the entire block. This scheme achieved 8690.43 m3 more oil production and 2.75% higher recovery factor in comparison with depletion production. [ABSTRACT FROM AUTHOR] more...
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- 2024
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20. An Integrated Approach for 3D Facies Modeling of Kangan and Dalan Reservoirs, South Pars Gas Field, Persian Gulf.
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Sfidari, Ebrahim, Amraie, Javad, Mehrabi, Houshang, and Zamanzadeh, Seyed Mohammad
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This study focuses on the facies modeling and reservoir characterization of the Permian-Triassic age Dalan and Kangan formations, defined as the main reservoirs in the Persian Gulf's South Pars Gas Field. Based on the main characteristics of petrographical observations, 12 facies were identified and classified into four facies associations representing tidal flat (LFAs 1), lagoon (LFAs 2), shoal (LFAs 3), and open marine (LFAs 4) conditions on a carbonate ramp. A neural network approach (self-organizing maps) was employed to predict lithofacies and lithofacies associations (LFAs) in uncored wells. The method demonstrated a high level of accuracy, achieving an 87.5% success rate in predicting lithofacies using GR, DT, NPHI, RHOB, and PEF logs. The predicted LFAs were compared with the core-derived facies and rock types to generate a 2D facies model within the sequence stratigraphy framework for geologic modeling and subsequent reservoir simulation. Finally, geostatistical techniques were employed to prepare a 3D facies distribution and depositional model for the entire field. The stochastic simulation method was applied here to simulate and generate the 3D model of four major LFAs involved in the modeling. Facies modeling of the formations indicates a gentle shallowing from zone K4 to zone K3. The connectivity of LFAs 3 is well observed in zone K4, whereas in zone K3 the connectivity of LFAs 2 is evident. Zone K2 is associated with dominant LFAs 3 and minor LFAs 4. The zone K1 is characterized by the dominance of LFAs 1. [ABSTRACT FROM AUTHOR] more...
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- 2024
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21. Accelerating Numerical Simulations of CO 2 Geological Storage in Deep Saline Aquifers via Machine-Learning-Driven Grid Block Classification.
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Kanakaki, Eirini Maria, Ismail, Ismail, and Gaganis, Vassilis
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CARBON sequestration ,AQUIFERS ,CARBON dioxide ,COMPUTER simulation ,SALT - Abstract
The accurate prediction of pressure and saturation distribution during the simulation of CO
2 injection into saline aquifers is essential for the successful implementation of carbon sequestration projects. Traditional numerical simulations, while reliable, are computationally expensive. Machine learning (ML) has emerged as a promising tool to accelerate these simulations; however, challenges remain in effectively capturing complex reservoir dynamics, particularly in regions experiencing rapid changes in pressure and saturation. This article addresses the challenges by introducing a fully automated, data-driven ML classifier that distinguishes between regions of fast and slow variation within the reservoir. Firstly, we demonstrate the variability in pressure across different reservoir grid blocks using a simple brine injection and production scenario, highlighting the limitations of conventional acceleration approaches. Subsequently, the proposed methodology leverages ML proxies to rapidly and accurately predict the behavior of slow-varying regions in CO2 injection simulations, while traditional iterative methods are reserved for fast-varying areas. The results show that this hybrid approach significantly reduces the computational load without compromising on accuracy. This provides a more efficient and scalable solution for modeling CO2 storage in saline aquifers. [ABSTRACT FROM AUTHOR] more...- Published
- 2024
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22. A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity.
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Guo, Liwen, Kang, Zhihong, Ding, Shuaiwei, Yuan, Xuehao, Yang, Haitong, Zhang, Meng, and Wang, Shuoliang
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KALMAN filtering ,OIL fields ,ARTIFICIAL intelligence ,SERVICE life ,CARBON dioxide ,OIL field flooding - Abstract
Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. This study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates artificial intelligence techniques with dynamic data and geological features to more accurately identify inter-well connectivity and its evolution. A comprehensive sensitivity analysis was performed on single-well pairs and multi-well groups regarding the permeability amplitude, the width of the high permeable channel, change, and lasting period of injection pressure. In addition, the production performance of multi-well groups, especially the development of ineffective circulation channels and their effects on reservoir development, are studied in-depth. The results show that higher permeability, wider permeable channels, and longer injection pressure maintenance can significantly enhance inter-well connectivity coefficients and reduce time-lag coefficients. Inter-well connectivity in multi-well systems is significantly affected by well-group configuration and inter-well interference effects. Based on the simulation results, the evaluation index of ineffective circulation channels is proposed and applied to dozens of well groups. These identified ineffective circulation channel changing patterns provide an important basis for optimizing oil fields' injection and production strategies through data-driven insights and contribute to improving oil recovery. The integration of artificial intelligence enhances the ability to analyze complex datasets, allowing for more precise adjustments in field operations. This paper's research ideas and findings can be confidently extended to other engineering scenarios, such as geothermal development and carbon dioxide storage, where AI-based models can further refine and optimize resource management and operational strategies. [ABSTRACT FROM AUTHOR] more...
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- 2024
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23. Well Pattern optimization as a planning process using a novel developed optimization algorithm
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Seyed Hayan Zaheri, Mahdi Hosseini, and Mohammad Fathinasab
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Genetic algorithm ,Particle swarm optimization ,Reservoir Simulation ,Well Placement Pattern ,Medicine ,Science - Abstract
Abstract Determination of optimum well location and operational settings for existing and new wells is crucial for maximizing production in field development. These optimum conditions depend on geological and petrophysical factors, fluid flow regimes, and economic variables. However, conducting numerous simulations for various parameters can be time-consuming and costly. Also, due to the high dimension of the possible solutions, there is still no general approach to address this problem. The application of searching algorithm as a general approach to solve such problems has received much attention in recent years. In this study, the efficiency, and reliability of genetic algorithm, particle swarm optimization and in particular a newly developed algorithm was analyzed and compared. The novelty of this work is the integrated algorithm, which improves searching performance by leveraging the memorizing characteristics of the particle swarm optimization algorithm to enhance genetic algorithm efficiency. In traditional genetic algorithms, solutions lacking adequate qualifications are deleted from the algorithmic process; however, the new algorithm provides these solutions with additional opportunities to prove themselves by acquiring new velocities from particle swarm optimization. The results indicate that while the genetic algorithm and particle swarm optimization do not guarantee optimal outcomes, the newly developed algorithm outperforms both methods. This performance was tested across various scenarios focused on well pattern optimization, highlighting its innovative contribution to the field development. more...
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- 2024
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24. Estimation of distribution algorithms for well placement optimization in petroleum fields.
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Brum, Artur, Coelho, Guilherme, Santos, Antonio Alberto, and Schiozer, Denis José
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DISTRIBUTION (Probability theory) , *NET present value , *OIL fields , *RANDOM forest algorithms , *ARTIFICIAL intelligence - Abstract
Optimizing well placement is one of the primary challenges in oil field development. The number and positions of wells must be carefully considered, as it is directly related to the infrastructure cost and the profits over the field's life cycle. In this paper, we propose three estimation of distribution algorithms to optimize well placement with the objective of maximizing the net present value. The methods are guided by an elite set of solutions and are able to obtain multiple local optima in a single run. We also present an auxiliary regression model to preemptively discard candidate solutions with poor performance prediction, thus avoiding running computationally expensive simulations for unpromising candidates. The model is trained with the data obtained during the search process and does not require previous training. Our algorithms yielded a significant improvement compared to a state-of-the-art reference method from the literature, as evidenced by computational experiments with two benchmarks. [ABSTRACT FROM AUTHOR] more...
- Published
- 2025
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- View/download PDF
25. Transfer Learning-Based Physics-Informed Convolutional Neural Network for Simulating Flow in Porous Media with Time-Varying Controls.
- Author
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Chen, Jungang, Gildin, Eduardo, and Killough, John E.
- Subjects
- *
CONVOLUTIONAL neural networks , *NEUMANN boundary conditions , *POROUS materials , *MULTIPHASE flow , *TWO-phase flow - Abstract
A physics-informed convolutional neural network (PICNN) is proposed to simulate two-phase flow in porous media with time-varying well controls. While most PICNNs in the existing literature worked on parameter-to-state mapping, our proposed network parameterizes the solutions with time-varying controls to establish a control-to-state regression. Firstly, a finite volume scheme is adopted to discretize flow equations and formulate a loss function that respects mass conservation laws. Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations so no additional loss term is needed. The network architecture comprises two parallel U-Net structures, with network inputs being well controls and outputs being the system states (e.g., oil pressure and water saturation). To capture the time-dependent relationship between inputs and outputs, the network is well designed to mimic discretized state-space equations. We train the network progressively for every time step, enabling it to simultaneously predict oil pressure and water saturation at each timestep. After training the network for one timestep, we leverage transfer learning techniques to expedite the training process for a subsequent time step. The proposed model is used to simulate oil–water porous flow scenarios with varying reservoir model dimensionality, and aspects including computation efficiency and accuracy are compared against corresponding numerical approaches. The comparison with numerical methods demonstrates that a PICNN is highly efficient yet preserves decent accuracy. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
26. New Insights into the Understanding of High-Pressure Air Injection (HPAI): The Role of the Different Chemical Reactions.
- Author
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Gutiérrez, Dubert, Moore, Gord, Mallory, Don, Ursenbach, Matt, Mehta, Raj, and Bernal, Andrea
- Subjects
- *
ENHANCED oil recovery , *FLAMMABLE gases , *PETROLEUM reservoirs , *HIGH temperatures , *IN situ processing (Mining) - Abstract
High-pressure air injection (HPAI) is an enhanced oil recovery process in which compressed air is injected into deep, light oil reservoirs, with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated temperature to produce carbon dioxide. The different chemical reactions taking place can be grouped into oxygen addition, thermal cracking, oxygen-induced cracking, and bond scission reactions. The latter reactions involve the combustion of a flammable vapor as well as the combustion of solid fuel, commonly known as "coke". Since stable peak temperatures observed during HPAI experiments are typically below 300 °C, it has been suggested that thermal cracking and combustion of solid fuel may not be important reaction mechanisms for the process. The objective of this work is to assess the validity of that hypothesis. Therefore, this study makes use of different oxidation and combustion HPAI experiments, which were performed on two different light oil reservoir samples. Modeling of those tests indicate that thermal cracking is not an important reaction mechanism during HPAI and can potentially be ignored. The work also suggests that the main fuel consumed by the process is a flammable vapor generated by the chemical reactions. This represents a shift from the original in situ combustion paradigm, which is based on the combustion of coke. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
27. Simulation Enhancement GAN for Efficient Reservoir Simulation at Fine Scales.
- Author
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Liu, Ye, Yang, Shuopeng, Zhang, Nan, Cao, Jie, and Guo, Chao
- Subjects
- *
GENERATIVE adversarial networks , *FINITE differences , *DEEP learning , *PARTIAL differential equations , *POROUS materials - Abstract
In this paper, an innovative approach for enhancing fluid transport modeling in porous media is presented, which finds application in various fields, including subsurface reservoir modeling. Fluid flow models are typically solved numerically by addressing a system of partial differential equations (PDEs) using methods such as finite difference and finite volume. However, these processes can be computationally demanding, particularly when aiming for high precision on a fine scale. Researchers have increasingly turned to machine learning to explore solutions for PDEs in order to improve simulation efficiency. The proposed method combines an adaptive multi-scale strategy with generative adversarial networks (GAN) to increase simulation efficiency on a fine scale. The devised model, called simulation enhancement GAN (SE-GAN), takes coarse-scale simulation results as input and generates fine-scale results in conjunction with the provided petrophysical properties. With this new approach, a deep learning model is trained to map coarse-scale results to fine-scale outcomes, rather than directly solving the fluid flow model. Case studies reveal that SE-GAN can achieve a significant improvement in accuracy while reducing computational time compared to the original fine-scale simulation solver. A comprehensive evaluation of numerical experiments is conducted to elucidate the benefits and limitations of this method. The potential of SE-GAN in accelerating the numerical solver for reservoir simulations is also demonstrated. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
28. Production optimization in a fractured carbonate reservoir with high producing GOR.
- Author
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Izadpanahi, Amin, Azin, Reza, Osfouri, Shahriar, and Malakooti, Reza
- Subjects
- *
RESPONSE surfaces (Statistics) , *PENG-Robinson equation , *PETROLEUM reservoirs , *ROCK properties , *OIL fields - Abstract
The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
29. Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs.
- Author
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Silva, João Victor Gois, Silveira, Bruno Marco Oliveira, Ferrari, Jean Vicente, and Sampaio, Marcio Augusto
- Subjects
CARBONATE reservoirs ,ENHANCED oil recovery ,NET present value ,INTERFACIAL tension ,GENETIC algorithms - Abstract
In Brazil, pre-salt carbonate reservoirs are largely responsible for the current increase in oil production. However, due to its peculiar characteristics, increasing oil recovery by water injection is not enough. Therefore, we seek to evaluate the recovery potential using chemical methods (cEOR). Among these, the Alkali Surfactant Foam (ASF) method appears with high potential, a variant of Alkali Surfactant Polymers (ASP) without the problems presented by it. Therefore, this work presents an innovative methodology, which seeks to evaluate the potential for recovery with ASF in carbonate reservoirs by integrating experimental characterization and recovery prediction using reservoir simulation. For this, phase behavior and adsorption analyses were carried out. The experimental results provided key parameters for the simulation, such as optimal salinity, surfactant adsorption, foam mobility reduction factors. The results are from two case studies of AS and ASF flooding, using a section of UNISIM-II benchmark, using a one-quarter of five-spot model. Having the modelling for these cEOR methods defined, an optimization process for each method was applied, allowing a reliable comparison among the methods and over a base case of water injection, seeking the maximization of the net present value (NPV). As a result, in the experimental part, a low interfacial tension (IFT) value of 0.003 mN/m was achieved with a surfactant adsorption reduction of 17.9% for an optimal setting among brine (NaCl), alkali (NaBO
2 .4H2 O), and surfactant (BIO-TERGE AS 40). In the reservoir simulation part, using a fast genetic algorithm in the optimization process, a NPV of US$ 14.43 million higher than the base case (water injection) and a 4.5% increase in cumulative oil production for the ASF injection case were obtained. Considering the analyses of production curves (cumulative oil production and oil rate) and oil saturation maps, a considerable oil production anticipation was observed, which was the main reason for NPV improvement, proving the high potential for application of the ASF method in carbonate reservoirs. [ABSTRACT FROM AUTHOR] more...- Published
- 2024
- Full Text
- View/download PDF
30. Investigation of impact of low salinity water flooding on oil recovery for sandstone reservoirs – a simulation approach.
- Author
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Agrawal, Vikas, Patel, Dhwiti, Nalawade, Shubham, Nande, Soumitra, and Patwardhan, Samarth D.
- Subjects
- *
OIL field flooding , *SANDSTONE , *SALINITY , *PETROLEUM , *FLOODS - Abstract
Low salinity Waterflooding (LSWF) is a proven EOR technique which has emerged in the last few decades. In this, the salinity of the injected water is reduced. Studies have unveiled that the LSWF can improve about 5–38% of oil recovery. This work aims to study the effectiveness of LSWF by modeling Sea waterflooding (SWF) and LSWF using a commercial reservoir simulator and justifying the proposed mechanisms responsible for the incremental oil recovery due to LSWF. A critical parametric study has been undertaken, which led us to conclude that LSWF is a cost-effective, and environment-friendly method that results in additional oil recovery for sandstone reservoirs. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
31. 3D seismic forward modeling from the multiphysical inversion at the Ketzin CO2 storage site.
- Author
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Zheng, Yi-kang, Wang, Chong, Liang, Hao-hong, Wang, Yi-bo, and Zeng, Rong-shu
- Subjects
- *
SEISMIC surveys , *SIGNAL-to-noise ratio , *TREND analysis , *DATA modeling , *CARBON dioxide - Abstract
From June 2008 to August 2013, approximately 67 kt of CO2 was injected into a deep saline formation at the Ketzin pilot CO2 storage site. During injection, 3D seismic surveys have been performed to monitor the migration of sequestered CO2. Seismic monitoring results are limited by the acquisition and signal-to-noise ratio of the acquired data. The multiphysical reservoir simulation provides information regarding the CO2 fluid behavior, and the approximated model should be calibrated with the monitoring results. In this work, property models are delivered from the multiphysical model during 3D repeated seismic surveys. The simulated seismic data based on the models are compared with the real data, and the results validate the effectiveness of the multiphysical inversion method. Time-lapse analysis shows the trend of CO2 migration during and after injection. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
32. The Importance of Reservoir Geomechanic Modelling for Carbon Sequestration, Storage, and Utilization: A Case Study from East Natuna.
- Author
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CHERDASA, JERES R., ARIADJI, TUTUKA, SAPIIE, BENYAMIN, and SIAGIAN, UCOK W. R.
- Subjects
- *
POISSON'S ratio , *HYDRAULIC control systems , *FLUID injection , *WORKING fluids , *CARBON sequestration - Abstract
East Natuna is well known for its huge natural gas reserves with a very high CO2 content. The appearance of CO2 content in an oil and gas field is always considered as waste material, and will severely affect the economic value of the field. The higher the content, the more costly the process, both technically and environmentally. In this research, the newly proposed reservoir management approach called CSSU (Carbon Sequestration, Storage, and Utilization) method is trying to be applied to change the paradigm of CO2 from waste material into economic material. The CSSU method is an integration of geological, geophysical, reservoir engineering, and engineering economics with the determination of technical and economic optimization of the use of CO2 produced as the working fluid in a power generation system that has been conditioned through an injection-production system in geological formations. Reservoir simulation modeling is done by three models, namely: Compositional, Compositional + Geomechanical Coupling, and Compositional + Geomechanical Coupling + Thermal. There is a difference in the the total injection between Compositional + Geomechanical Coupling and ordinary Compositional simulations of 1-2 % due to factors such as Modulus Young, Poisson's Ratio, Angle of Internal Friction, and Biot's Coefficient which affect the reservoir pore volume calculations and the total CO2 fluid injection calculation. The changes in geomechanical parameters will affect the CSSU techno-economic analysis where a 30 % change in the rock compressibility and poisson ratio parameters will effect changes in the electrical energy amounts being produced by 0.01 MW or 0.33 %, and in an economic value of 4 MMUS $ or 2.24 %. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
33. Speeding up the reservoir simulation by real time prediction of the initial guess for the Newton-Raphson's iterations.
- Author
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Petrosyants, Musheg, Trifonov, Vladislav, Illarionov, Egor, and Koroteev, Dmitry
- Subjects
- *
NEWTON-Raphson method , *TWO-phase flow , *POROUS materials , *PARAMETER estimation , *PREDICTION models - Abstract
We study linear models for the prediction of the initial guess for the nonlinear Newton-Raphson solver. These models use one or more of the previous simulation steps for prediction, and their parameters are estimated by the ordinary least-squares method. A key feature of the approach is that the parameter estimation is performed using data obtained directly during the simulation and the models are updated in real time. Thus we avoid the expensive process of dataset generation and the need for pre-trained models. We validate the workflow on a standard benchmark Egg dataset of two-phase flow in porous media and compare it to standard approaches for the estimation of initial guess. We demonstrate that the proposed approach leads to reduction in the number of iterations in the Newton-Raphson algorithm and speeds up simulation time. In particular, for the Egg dataset, we obtained a 30% reduction in the number of nonlinear iterations and a 20% reduction in the simulation time. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
34. 高精度油藏数值模拟技术研究进展及应用.
- Author
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赵国忠, 兰玉波, 匡铁, 何鑫, 王青振, 李椋楠, and 石亮
- Subjects
SHALE oils ,PETROLEUM reservoirs ,ARTIFICIAL intelligence ,OIL fields ,SIMULATION methods & models - Abstract
Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
- Published
- 2024
- Full Text
- View/download PDF
35. History Matching Reservoir Models With Many Objective Bayesian Optimization
- Author
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Steven Samoil, Clyde Fare, Kirk E. Jordan, and Zhangxin Chen
- Subjects
Bayesian optimization ,history matching ,reservoir simulation ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
ABSTRACT Reservoir models for predicting subsurface fluid and rock behaviors can now include upwards of billions (and potentially trillions) of grid cells and are pushing the limits of computational resources. History matching, where models are updated to match existing historical data more closely, is conducted to reduce the number of simulation runs and is one of the primary time‐consuming tasks. As models get larger the number of parameters to match increases, and the number of objective functions increases, and traditional methods start to reach their limitations. To solve this, we propose the use of Bayesian optimization (BO) in a hybrid cloud framework. BO iteratively searches for an optimal solution in the simulations campaign through the refinement of a set of priors initialized with a set of simulation results. The current simulation platform implements grid management and a suite of linear solvers to perform the simulation on large scale distributed‐memory systems. Our early results using the hybrid cloud implementation shown here are encouraging on tasks requiring over 100 objective functions, and we propose integrating BO as a built‐in module to efficiently iterate to find an optimal history match of production data in a single package platform. This paper reports on the development of the hybrid cloud BO based history matching framework and the initial results of the application to reservoir history matching. more...
- Published
- 2024
- Full Text
- View/download PDF
36. Assessment of core samples through the analysis of CT measurements and its implications for CO2 sequestration potential in a Hungarian depleted oil field
- Author
-
Gábor Pál Veres, Tamás Földes, and István Szunyog
- Subjects
CO2 storage ,Computed tomography ,Reservoir simulation ,Storage capacity ,Technology - Abstract
This article focuses on potential of refining the CO2 storage capacity and rock parameters of a specific reservoir in Hungary. As part of a comprehensive laboratory measurement program, drilling core samples were analyzed using a human computed tomography (CT). The primary results of these evaluations provide critical insights that will be utilized as input parameters for developing a dynamic reservoir model. This approach aims to achieve a more accurate understanding of the reservoir. These preliminary data will be instrumental in enhancing our knowledge of this storage site, ultimately contributing to more effective CO2 storage solutions and advancing the field of geological carbon sequestration. more...
- Published
- 2024
- Full Text
- View/download PDF
37. A Combined Dimensionless Number in Low Salinity Waterflood Alternating Gas Immiscible CO2 Injection to Predict Oil Recovery Factor in Sandstone Reservoir
- Author
-
Efras, Muhammad Ridho, Dzulkarnain, Iskandar B., Ridha, Syahrir, Merdeka, Mohammad Galang, Rasool, Muhammad Hammad, Hyun, Lee Jang, and Kwon, Sunil
- Published
- 2025
- Full Text
- View/download PDF
38. Application of Adaptive Artificial Viscosity Method to Reduce Grid Orientation Effect in Numerical Simulations for Steam Thermal Recovery
- Author
-
Yue, Meng-Chen, Wang, Xiao-Hong, Liu, Zhi-Feng, Cao, Wei-Dong, Wang, Yong, Hu, Jun, Xiao, Chang-Hao, and Li, Yao-Yong
- Published
- 2024
- Full Text
- View/download PDF
39. Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs
- Author
-
João Victor Gois Silva, Bruno Marco Oliveira Silveira, Jean Vicente Ferrari, and Marcio Augusto Sampaio
- Subjects
Alkali surfactant foam ,Carbonate reservoir ,Chemical enhanced oil recovery ,Experimental investigation ,Reservoir simulation ,Optimization ,Petroleum refining. Petroleum products ,TP690-692.5 ,Petrology ,QE420-499 - Abstract
Abstract In Brazil, pre-salt carbonate reservoirs are largely responsible for the current increase in oil production. However, due to its peculiar characteristics, increasing oil recovery by water injection is not enough. Therefore, we seek to evaluate the recovery potential using chemical methods (cEOR). Among these, the Alkali Surfactant Foam (ASF) method appears with high potential, a variant of Alkali Surfactant Polymers (ASP) without the problems presented by it. Therefore, this work presents an innovative methodology, which seeks to evaluate the potential for recovery with ASF in carbonate reservoirs by integrating experimental characterization and recovery prediction using reservoir simulation. For this, phase behavior and adsorption analyses were carried out. The experimental results provided key parameters for the simulation, such as optimal salinity, surfactant adsorption, foam mobility reduction factors. The results are from two case studies of AS and ASF flooding, using a section of UNISIM-II benchmark, using a one-quarter of five-spot model. Having the modelling for these cEOR methods defined, an optimization process for each method was applied, allowing a reliable comparison among the methods and over a base case of water injection, seeking the maximization of the net present value (NPV). As a result, in the experimental part, a low interfacial tension (IFT) value of 0.003 mN/m was achieved with a surfactant adsorption reduction of 17.9% for an optimal setting among brine (NaCl), alkali (NaBO2.4H2O), and surfactant (BIO-TERGE AS 40). In the reservoir simulation part, using a fast genetic algorithm in the optimization process, a NPV of US$ 14.43 million higher than the base case (water injection) and a 4.5% increase in cumulative oil production for the ASF injection case were obtained. Considering the analyses of production curves (cumulative oil production and oil rate) and oil saturation maps, a considerable oil production anticipation was observed, which was the main reason for NPV improvement, proving the high potential for application of the ASF method in carbonate reservoirs. more...
- Published
- 2024
- Full Text
- View/download PDF
40. Performance analysis of linearization schemes for modelling multi-phase flow in porous media
- Author
-
Abdul Salam Abd, Ali Asif, and Ahmad Abushaikha
- Subjects
Linearization ,Nonlinear solvers ,Reservoir simulation ,Numerical analysis ,Medicine ,Science - Abstract
Abstract Reservoir simulation is crucial for understanding the flow response in underground reservoirs, and it significantly helps reduce uncertainties in geological characterization and optimize methodologies for field development strategies. However, providing efficient and accurate solutions for the strong heterogeneity remains challenging, as most of the discretization methods cannot handle this complexity. In this work, we perform a comprehensive assessment of various numerical linearization techniques employed in reservoir simulation, particularly focusing on the performance of the nonlinear solver for problem dealing with fluid flow in porous media. The primary linearization methods examined are finite difference central (FDC), finite forward difference (FDF), and operator-based linearization (OBL). These methods are rigorously analyzed and compared in terms of their accuracy, computational efficiency, and adaptability to changing reservoir conditions. The results demonstrate that each method has distinct strengths and limitations. The FDC method is more accurate particularly in complex simulations where strong heterogeneity are introduced but is generally slower in convergence. The OBL on the other hand, is more efficient and converges quickly, which makes it suitable for scenarios with limited computational resources and simple physics, while the FDF method provides a balanced combination of precision and computational speed, contingent upon careful step size management of the derivative estimations. This paper aims to guide the selection of appropriate linearization techniques for enhancing nonlinear solvers’ accuracy and efficiency in reservoir simulation . more...
- Published
- 2024
- Full Text
- View/download PDF
41. Simulation of Nahand reservoir water allocation and its performance evaluation under developed scenarios using the water evaluation and planning (WEAP) model
- Author
-
Mohsen Salimi, Mohammad Taghi Sattari, and Javad Parsa
- Subjects
nahand dam ,performance indicators of the reservoir ,water demand ,reservoir simulation ,water evaluation and planning model (weap) ,River, lake, and water-supply engineering (General) ,TC401-506 ,Engineering geology. Rock mechanics. Soil mechanics. Underground construction ,TA703-712 - Abstract
Introduction Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc. Materials and Methods This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances. Results and Discussion The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios. Conclusion To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher. more...
- Published
- 2024
- Full Text
- View/download PDF
42. Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches
- Author
-
Ehsan Hajibolouri, Ali Akbar Roozshenas, Rohaldin Miri, Aboozar Soleymanzadeh, Shahin Kord, and Ali Shafiei
- Subjects
Machine learning ,Random forest ,Permeability modelling ,Heterogeneity ,Reservoir simulation ,Data modelling ,Medicine ,Science - Abstract
Abstract Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system. more...
- Published
- 2024
- Full Text
- View/download PDF
43. Performance analysis of linearization schemes for modelling multi-phase flow in porous media.
- Author
-
Abd, Abdul Salam, Asif, Ali, and Abushaikha, Ahmad
- Subjects
- *
MULTIPHASE flow , *POROUS materials , *FINITE differences , *FLUID flow , *DISCRETIZATION methods - Abstract
Reservoir simulation is crucial for understanding the flow response in underground reservoirs, and it significantly helps reduce uncertainties in geological characterization and optimize methodologies for field development strategies. However, providing efficient and accurate solutions for the strong heterogeneity remains challenging, as most of the discretization methods cannot handle this complexity. In this work, we perform a comprehensive assessment of various numerical linearization techniques employed in reservoir simulation, particularly focusing on the performance of the nonlinear solver for problem dealing with fluid flow in porous media. The primary linearization methods examined are finite difference central (FDC), finite forward difference (FDF), and operator-based linearization (OBL). These methods are rigorously analyzed and compared in terms of their accuracy, computational efficiency, and adaptability to changing reservoir conditions. The results demonstrate that each method has distinct strengths and limitations. The FDC method is more accurate particularly in complex simulations where strong heterogeneity are introduced but is generally slower in convergence. The OBL on the other hand, is more efficient and converges quickly, which makes it suitable for scenarios with limited computational resources and simple physics, while the FDF method provides a balanced combination of precision and computational speed, contingent upon careful step size management of the derivative estimations. This paper aims to guide the selection of appropriate linearization techniques for enhancing nonlinear solvers' accuracy and efficiency in reservoir simulation. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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44. Well Spacing Optimization to Enhance the Performance of Tight Reservoirs.
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Al-Khdheeawi, Emad A., Al-Rubuey, Wisam I., Yuan, Yujie, Fahem, Muntadher M., and Jassim, Jaafar J.
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GAS reservoirs ,PETROLEUM reservoirs ,WATERSHEDS ,HYDRAULIC fracturing ,PETROLEUM industry - Abstract
This paper presents an integrated approach to investigate the impact of well spacing on the performance of tight oil and gas reservoirs depleted by advanced multi-stage hydraulic fracture in horizontal wellbores distributed parallelly in rectangular drainage areas. Thus, an analytical models has been developed considering different reservoir configurations and the associated flow rate, cumulative production, ultimate recovery has been recorded. Also, the pressure behavior for early stages of production conditions and the flow regime dominated by boundary effects has been analyzed. The results indicate that well spacing significantly impacts reservoir performance, particularly at late production stages and that well interference and an ee of well spacing optimization in tight oil and gas reservoirs, offering valuable insights for the strategic planning and development of these vital resources. [ABSTRACT FROM AUTHOR] more...
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- 2024
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45. Integrated Approach to Reservoir Simulations for Evaluating Pilot CO 2 Injection in a Depleted Naturally Fractured Oil Field On-Shore Europe.
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Pagáč, Milan, Opletal, Vladimír, Shchipanov, Anton, Nermoen, Anders, Berenblyum, Roman, Fjelde, Ingebret, and Rez, Jiří
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- *
GEOLOGICAL carbon sequestration , *CARBON sequestration , *GREENHOUSE gases , *OIL fields , *CARBON dioxide , *CARBONATE reservoirs , *GAS fields - Abstract
Carbon dioxide capture and storage (CCS) is a necessary requirement for high-emitting CO2 industries to significantly reduce volumes of greenhouse gases released into the atmosphere and mitigate climate change. Geological CO2 storage into depleted oil and gas fields is the fastest and most accessible option for CCS deployment allowing for re-purposing existing infrastructures and utilizing significant knowledge about the subsurface acquired during field production operations. The location of such depleted fields in the neighborhoods of high-emitting CO2 industries is an additional advantage of matured on-shore European fields. Considering these advantages, oil and gas operators are now evaluating different possibilities for CO2 sequestration projects for the fields approaching end of production. This article describes an integrated approach to reservoir simulations focused on evaluating a CO2 injection pilot at one of these matured fields operated by MND and located in the Czech Republic. The CO2 injection site in focus is a naturally fractured carbonate reservoir. This oil-bearing formation has a gas cap and connection to a limited aquifer and was produced mainly by pressure depletion with limited pressure support from water injection. The article summarizes the results of the efforts made by the multi-disciplinary team. An integrated approach was developed starting from geological modeling of a naturally fractured reservoir, integrating the results of laboratory studies and their interpretations (geomechanics and geochemistry), dynamic field data analysis (pressure transient analysis, including time-lapse) and history matching reservoir model enabling simulation of the pilot CO2 injection. The laboratory studies and field data analysis provided descriptions of stress-sensitive fracture properties and safe injection envelope preventing induced fracturing. The impact of potential salt precipitation in the near wellbore area was also included. These effects are considered in the context of a pilot CO2 injection and addressed in the reservoir simulations of injection scenarios. Single-porosity and permeability reservoir simulations with a dominating fracture flow and black-oil formulation with CO2 simulated as a solvent were performed in this study. The arguments for the choice of the simulation approach for the site in focus are shortly discussed. The reservoir simulations indicated a larger site injection capacity than that required for the pilot injection, and gravity-driven CO2 migration pathway towards the gas cap in the reservoir. The application of the approach to the site in focus also revealed large uncertainties, related to fracture description and geomechanical evaluations, resulting in an uncertain safe injection envelope. These uncertainties should be addressed in further studies in preparation for the pilot. The article concludes with an overview of the outcomes of the integrated approach and its application to the field in focus, including a discussion of the issues and uncertainties revealed. [ABSTRACT FROM AUTHOR] more...
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- 2024
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46. شبیهسازی تخصیص آب مخزن سد نهند و ارزیابی عملکرد آن تحت سناریوهای تدوین شده در مدل ارزیابی و برنامهریزی آب )WEAP).
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محسن سلیمی, محمدتقی ستاری, and جواد پارسا
- Abstract
Introduction Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc. Materials and Methods This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances. Results and Discussion The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios. Conclusion To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher. [ABSTRACT FROM AUTHOR] more...
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- 2024
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47. Methods Used to Estimate Reservoir Pressure Performance: A Review.
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Amer, Manar M. and Al-Obaidi, Dahlia A.
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ENHANCED oil recovery ,PETROLEUM reservoirs ,PRODUCTION engineering ,ARTIFICIAL intelligence - Abstract
Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
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- 2024
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48. Cushion gas effects on hydrogen storage in porous rocks: Insights from reservoir simulation and deep learning.
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Mao, Shaowen, Chen, Bailian, Morales, Misael, Malki, Mohamed, and Mehana, Mohamed
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GAS condensate reservoirs , *DEEP learning , *ARTIFICIAL neural networks , *HYDROGEN storage , *ENERGY futures , *GAS reservoirs , *UNDERGROUND storage - Abstract
Underground hydrogen (H 2) storage (UHS) is crucial for the H 2 economy, offering scalable and long-term solutions vital for reliable and sustainable H 2 -based energy systems. Cushion gas is a common component in UHS designs, but its impact on H 2 storage performance is not yet fully understood. This study bridges this research gap by systematically investigating the effects of various cushion gas scenarios on UHS performance in porous rocks, specifically saline aquifers and depleted gas reservoirs, using reservoir simulations and deep learning. Firstly, we conducted 8,000 multiphase compositional reservoir simulations for UHS operations in two types of storage formations, considering four cushion gas scenarios: no cushion gas, methane (CH 4), nitrogen (N 2), and carbon dioxide (CO 2). These simulations cover a wide range of geological and operational parameters relevant to practical UHS projects. From these simulations, we derived key insights into the physical mechanisms governing UHS processes and proposed critical storage performance metrics, including H 2 withdrawal efficiency, produced H 2 purity, produced gas–water ratio, and well injectivity. Then, we developed a unified reduced-order model (ROM) using a deep neural network (DNN) based on the comprehensive simulation results. The DNN architecture, with an input layer, five hidden layers, and an output layer, takes 12 geological and operational parameters as inputs, and forecasts the four performance metrics. The ROM accurately predicts the cyclic evolution of performance metrics and is over 5,000,000 times faster than traditional physics-based simulations, allowing for thorough uncertainty quantification of UHS performance prediction under various geological and operational conditions. Key findings of this study include: (a) UHS in porous rocks is technically promising, with improving storage performance over cycles; (b) UHS in saline aquifers has higher withdrawal efficiency and purity but lower gas–water ratio and injectivity compared to depleted gas reservoirs; (c) In saline aquifers, cushion gas reduces withdrawal efficiency and purity but significantly enhances gas–water ratio and injectivity, making it particularly beneficial in scenarios with high water production risks; (d) In depleted gas reservoirs, cushion gas is less important under the current operational conditions, as it barely affects withdrawal efficiency and purity, and these reservoirs inherently exhibit high gas–water ratio and injectivity due to initial/leftover native gas (mostly CH 4); (e) The cushion gas type slightly affects storage performance, with N 2 slightly outperforming CH 4 and CO 2 in withdrawal efficiency and purity, CO 2 being better for gas–water ratio, and CH 4 for injectivity. The findings of this study support cushion gas designs in future UHS projects in both saline aquifers and depleted gas reservoirs. Moreover, the developed ROM offers a highly efficient tool for conducting extensive sensitivity analysis and uncertainty quantification, optimizing operational conditions, and facilitating the screening of potential field sites for future UHS projects. • Performed extensive reservoir simulations for underground hydrogen storage (UHS). • Analyzed the physical mechanisms affecting UHS performance. • Developed a unified reduced-order model (ROM) leveraging deep learning. • Conducted thorough uncertainty quantification to evaluate cushion gas effects. [ABSTRACT FROM AUTHOR] more...
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- 2024
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49. ADAPTIVE SPACE-TIME DOMAIN DECOMPOSITION FOR MULTIPHASE FLOW IN POROUS MEDIA WITH BOUND CONSTRAINTS.
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TIANPEI CHENG, HAIJIAN YANG, JIZU HUANG, and CHAO YANG
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- *
DOMAIN decomposition methods , *MULTIPHASE flow , *POROUS materials , *SPACETIME , *FINITE element method , *NEWTON-Raphson method , *PARALLEL algorithms - Abstract
This paper proposes an adaptive space-time algorithm based on domain decomposition for the large-scale simulation of a recently developed thermodynamically consistent reservoir problem. In the approach, the bound constraints are represented by means of a minimum-type complementarity function to enforce the positivity of the reservoir model, and a space-time mixed finite element method is applied for the parallel-in-time monolithic discretization. In particular, we propose a time-adaptive strategy using the improved backward differencing formula of second order, to take full advantage of the high degree of space-time parallelism. Moreover, the complicated dynamics with higher nonlinearity of space-time discretization require some innovative nonlinear and linear solution strategies. Therefore, we present a class of modified semismooth Newton algorithms to enhance the convergence rate of nonlinear iterations. Multilevel space-time restricted additive Schwarz algorithms, whose subdomains cover both space and time variables, are also studied for domain decomposition-based preconditioning. Numerical experiments demonstrate the robustness and parallel scalability of the proposed adaptive space-time algorithm on a supercomputer with tens of thousands of processor cores. [ABSTRACT FROM AUTHOR] more...
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- 2024
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50. Mathematical Model of Asphaltene Deposition During Oil Production.
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Nikiforov, A. I. and Nikiforov, G. A.
- Abstract
Asphaltenes are heavy hydrocarbon molecules that exist naturally in petroleum reservoir fluids. Asphaltene precipitation may occur during pressure depletion or during gas injection processes to improve oil recovery. Inside the reservoir, the precipitated asphaltene can deposit onto the rock surface or remain as a suspended solid in the oil phase. Precipitated asphaltenes are one of the main causes of decreased permeability. A new approach to modeling asphaltene deposition during oil production has been developed. In the model, oil is represented by two hydrocarbon components ("oil" and "asphaltene") that do not dissolve in water and the theory of an ideal solution of a binary mixture is used. Closing relations for the mass and momentum conservation equations describing porosity, permeability and mass transfer are constructed using the pore size distribution function and a model of an ideal porous medium consisting of a bundle of capillaries. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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