bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient FDP Optimization for AI Enhanced Decision Making
Authors: De Paola Giorgio , Danger Richar Villarroel
Published: June 2023

The present work introduces an efficient workflow for AI-enhanced decision-making in Field Development Planning Optimization. Despite the clear importance of uncertainty quantification in decision-making, we find that constraints in time, hardware, and costs are often limiting factors during field evaluation, with the drawback of having a biased uncertainty description or a wrong risk perception. The proposed work encompasses history matching, solution analysis, and production optimization with special emphasis on reducing both simulation and processing time, maximizing what we can call the result per core hour.

At the center of our work is an AI-guided optimizer suited to avoid excessive convergence bias and maintain an optimal exploration vs. exploitation performance. The optimizer allows the integration of a multi-objective (MO) formulation in standard history matching and optimization workflows. Despite the flexibility of MO optimization and the vast literature in the energy industry, its usage in real-field cases has always been quite limited due to its formulation availability in commercial software and the increased computation time. This work will show improvement in solution accuracy and formulation flexibility compared to Single Objective (SO) formulations at no increase in runtime.

MO is based on the iterative convergence of an efficient frontier from the results generated by the simulation. This same concept has been brought to a user analysis step to allow the identification of best solutions across multiple evaluation workflows, lowering the expertise level for a solution.
Electric Submersible Generators – Using ESPs in an Unfamiliar Way
Authors: Collins Charles Evan , Frazier Kenneth Joel , Coates Bryan
Published: June 2023

Electrical Submersible Pumps (ESP) have been used to produce fluids for almost a century in many different industries. Since the 1800s, people have been using hydroelectric dams to produce power, demonstrating the viability of harnessing the potential energy from elevated water sources. However, there is a tremendous opportunity to partially recover that same potential during the yearly reinjection of billions of cubic meters of water in the oil, gas, and geothermal industries by combining the principles from both.

The electrical submersible generator (ESG) is a modern interpretation of an old idea. The ESG can generate electricity to meet surface power needs or feed directly into the grid to create a revenue stream under the proper injection conditions. In typical configurations, a standard centrifugal pump is used in combination with an induction motor. Rather than operating the motor in a conventional manner to convert electrical energy to mechanical work, the motor is operated in such a way for the mechanical work of the pump to be converted into electrical energy. Other configurations are optional such as using more conventional turbine pump designs and permanent magnet motors.

After the successful completion of a few installations, several changes have been made to optimize the machinery and enhance the equipment operation. Through these efforts, significant knowledge has been gained about how these machines work. For a successful completion, factors including third-party controls, run-away speeds, fluid / reservoir properties, and starting techniques must all be considered.

This paper explores the ESG’s design considerations, theoretical underpinnings, and potential future applications. This will involve a review of field operations, installation procedures, and lessons learned. The conclusions of CFD analysis and installations in the actual world will be presented to support the assumptions.
An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations
Authors: Tabib Mandar Vasudeo , Nivlet Philippe , Steinar Knut , Skogestad Jan Ole , Nybø Roar , Rasheed Adil
Published: June 2023

The paper aims at demonstrating a novel intrusive hybrid-analytics and modelling (HAM) that combines physics-based model and machine learning (ML) for predicting key variables in the monitoring of hole cleaning during drilling, and more specifically monitoring pressure/Equivalent Circulating Density (ECD) and cuttings volume fraction.

Currently, for predicting the spatial-temporal evolution of circulating mud in real-time in the annulus during drilling and potentially anticipating hole cleaning issues, a low-resolution physics-based 1D model is utilized that solves multi-phase flow equations. This model is computationally efficient but susceptible to discrepancies with actual observations. These errors could be a result of numerical issues, unmodelled physics in the model, or inaccurate input to the model. Here, machine learning is used to learn the pattern in residuals between the low-resolution model and a higher fidelity calculation, as well as measurements. The results show that the inclusion of machine learning models for correcting the low-fidelity cutting transport model has helped to improve the accuracy of low-fidelity model in predicting pressure and cutting volume fractions : which are key variables for monitoring hole cleaning. The machine learning models (ANN and LSTM models) have shown good performance in learning and correcting various errors associated with the 1D model, like (a) the numerical errors, (i.e. the error resulting from coarser and finer time-scales for the cuttings volume fraction along the well), and (b) the error due to physics (i.e. the difference in predictions between hi-fidelity model and low-fidelity model for pressure), and (c) the error between measurements and predictions of low-fidelity model. The conclusion of the work is that the intrusive HAM approach combining deep-learning with physics-based approach has the potential to provide a robust and efficient replacement of unknown parts of complex physics in mathematical models for drilling. Future work may involve using this HAM-in-drilling approach in conjunction with an anomaly detection algorithm to enable real-time decision when an anomaly occurs.
Rapid Inference of Reservoir Permeability From Inversion of Travel Time Data Under a Fast Marching Method Based Deep Learning Framework
Authors: Li Chen , Yan Bicheng , Kou Rui , Gao Shunhua
Published: June 2023

The Fast Marching Method (FMM) is a highly efficient numerical algorithm frequently used to solve the Eikonal equation to obtain the travel time from the source point to spatial locations, which can generate a geometric description of monotonically advancing front in anisotropic and heterogeneous media. In modeling fluid flow in subsurface heterogeneous porous media, application of the FMM makes the characterization of pressure front propagation quite straightforward using the diffusive time of flight (DTOF) as the Eikonal solution from an asymptotic approximation to the diffusivity equation. For the infinite-acting flow that occurs in smoothly varying heterogeneous media, travel time of pressure front from the active production or injection well to the observation well can be directly estimated from the DTOF using the concept of radius of investigation (ROI). Based on the ROI definition, the travel time to a given location in space can be determined from the maximum magnitude of partial derivative of pressure to time. Treating travel time computed at the observation well as the objective function, we propose a FMM based deep learning (DL) framework, namely the Inversion Neural Network (INN), to inversely estimate heterogeneous reservoir permeability fields through training the deep neural network (DNN) with the travel time data directly generated from the FMM. A convolutional neural network (CNN) is adopted to establish the mapping between the heterogeneous permeability field and the sparse observational data. Because of the quasi-linear relationship between the travel time and reservoir properties, CNN inspired by FMM is able to provide a rapid inverse estimate of heterogeneous reservoir properties that show sufficient accuracy compared to the true reference model with a limited number of observation wells. Inverse modeling results of the permeability fields are validated by the asymptotic pressure approximation through history matching of the reservoir models with the multi-well pressure transient data.
Long-Term Integrity of Well Cements for CO2-Storage Through Self-Healing Capabilities - Test Development
Authors: Van Noort Reinier
Published: June 2023

Self-healing wellbore sealants, that (chemically) react with leaking fluids such that when leakage pathways form, they are sealed rather than widened, can be a key technology for ensuring long-term seal integrity in CCS and other geological storage applications. Developing such sealants requires representative and reproducible testing methods, to assess how a leakage pathway through a selected sealant material evolves when exposed to a flow of leaking fluid under in-situ conditions. Here, we will present novel experiments, in which a reproducible simulated leakage pathway through a sealant sample is exposed to a constant flow of supercritical CO2. During exposure, up- and downstream fluid pressures are monitored to assess changes in permeability. Microstructural and mineralogical changes are assessed afterwards, using SEM with EDS.
A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data
Authors: Anifowose Fatai , Mezghani Mokhles , Badawood Saleh , Ismail Javed
Published: June 2023

In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field.

The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively.

Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be deduced that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research.

This study has confirmed the feasibility of predicting porosity at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.
A Comparative Study of Hydrogen/Natural Gas Storage in a Depleted Gas Field in the Netherlands Using Analytical and Numerical Modelling
Authors: Yousefi S. Hamidreza , Gonçalves Machado Cintia , Juez-Larré Joaquim , Ravestein Thomas , Smeenk Sebastiaan , Groenenberg Remco
Published: June 2023

Underground gas storage (UGS) in depleted gas reservoirs plays a vital role today in balancing supply and demand of natural gas and securing energy supply. Similarly, such reservoirs could potentially be (re)used for underground hydrogen storage (UHS). Feasibility studies on hydrogen storage in reservoirs need reliable tools and methodologies to identify potential candidates and evaluate their performance. In this study, we investigate the differences between the results of our in-house developed analytical model and a commercial numerical simulation model for a comparative assessment of storage capacity and flow performance of UGS and UHS sites.

The depleted natural gas field of Roden in the Netherlands is selected for a case study because it is considered a representative candidate for UHS. Our analytical model utilizes nodal analysis, a reservoir engineering method that combines novel inflow and outflow performance curves, assuming average reservoir parameters. Meanwhile, the numerical modelling is based on a detailed geological Petrel (static) model of the Roden field, that serves as input for the dynamic modelling with Eclipse 300 (E300).

Our analytical modelling approach shows that the storage performance (injection and production rates, and storage capacity) of hydrogen in terms of energy is lower than that of natural gas. This is based on the combined effect of different fluid properties for hydrogen and methane (i.e. volumetric energy density, compressibility factor, flow performances associated to viscosity and density in the well and reservoir), and several important potential technical limitations (e.g., flow velocity and bottom-hole pressure drawdown limits). The numerical modelling approach provides a more in-depth understanding of processes taking place and provides more accurate estimates on reservoir performance, as it considers the heterogeneity of the reservoir, multiphase flow and type of cushion gas used. Our results provide relevant quantitative insights on the (dis)advantages and error margins when using each modelling method. We show that with a limited amount of input data the analytical approach can be effectively used as a tool to quickly screen large portfolios of depleted gas fields and to obtain a first-order estimation of their performances. Meanwhile numerical modelling can be applied after the best candidates have been selected for a more detailed analysis in which uncertainties are narrowed down to optimize reservoir and well performance, and to establish a robust design for surface facility units and estimation of associated costs.
Numerical Investigations on Induced Seismicity and Fracture Activation in Fractured Geothermal Reservoirs
Authors: Gudala Manojkumar , Xu Zhen , Tariq Zeeshan , Yan Bicheng , Sun Shuyu
Published: June 2023

In this study we developed mathematical model for thermo-hydro-mechanical process occurs within the geothermal reservoir with variable rock/fracture/fluid parameters. The influence of fracture network on the cold plume movement, pore pressure, changes in the rock/fracture effective stress under the same operating conditions. The injected fluid transport to extraction well from injection well within the interconnected fractures. In the same direction variation of the effective stress, pore pressure both in rock matrix and fractures was observed. Due to the variation of effective stress in the fracture, it will undergo shearing and alter the fracture aperture. This variation of fracture aperture will create a micro-seismic moment in the fractured geothermal reservoir. The magnitude of micro-seismic moment and hyper center were changing with time and highly sensitive to the fracture connectivity of each fracture set. The developed mathematical model was observed these variations efficiently. Thus, the developed model can be utilized to address the variations occurred throughout the heat extraction in the fractured geothermal reservoir in conjunction with the activation of fracture and location of hyper center of each seismic moment.
Flow-Based Upscaling of Fractured Porous Media Using a Discrete Fracture and Matrix Model and a Fracture Merging Algorithm
Authors: Maier Christine , Oliveira Gonçalo , Schiozer Denis José
Published: June 2023

Many geo-energy related applications involve predicting the behavior of fluid flow in fractured subsurface reservoirs. Naturally fractured carbonate reservoirs are particularly important for being a major source of the world's hydrocarbon production. These reservoirs are also currently being considered as potential CO2 storage sites that will support net zero emissions goal. Simulation of flow in fractured reservoirs is a challenging task that typically involves upscaling the effective permeability of the fracture network and matrix into continuum models that consider the reservoir scale. The most accurate way to obtain such upscaled permeability for fracture networks is to perform single-phase flow simulations in statistical realizations of the fracture network using three-dimensional unstructured grids and explicit modelling of fractures. This step can be computationally challenging for highly dense fracture networks due to the difficulty in meshing the fractures and the rock matrix. Here, we present a method to reduce the complexity of the fracture network while still preserving the behavior of its effective permeability. Our approach involves a fracture merging algorithm that reduces the number of fractures allowing for faster meshing and upscaling. The fracture merging algorithm uses three different similarity metrics: fracture orientation, fracture area and distance between fractures. These metrics are used to identify similar fractures that can be merged into one single fracture with increased permeability. The upscaling algorithm to obtain the effective permeability of a grid cell containing a fracture network relies on flow simulations in three-dimensional unstructured meshes. We applied our method to different sub-networks extracted from a stochastically generated fracture network of a Brazilian Pre-Salt carbonate reservoir. We found that the average permeability of all fractures of the resulting fracture network increases with merging intensity, i.e., with decreasing the number of fractures, while the resulting upscaled effective permeability for the network remains in the same order of magnitude. This shows that the flow-based upscaling workflow including the merging algorithm leads to a significant reduction of complexity of fracture networks and consequently their 3D unstructured meshes while maintaining the structural and topological features that account for the fracture network effective permeability. Our proposed method is simple to implement and relies only on geometrical properties of the fractures. Other machine-learning based models have been proposed to achieve similar simplification of fracture networks, however, they are not easily incorporated into existing reservoir simulation tools and codes like the method presented in this work. Moreover, such previously published approaches do not consider flow in matrix and thus haven't been tested in scenarios where the matrix also contributes to flow.
Well Performance Metrics Suitable for Automated Monitoring
Authors: Shchipanov Anton , Namazova Gyunay , Muradov Khafiz
Published: June 2023

Automated well operations is a rapidly growing area with recent progress in automated drilling extending now into automated well monitoring and control during production operations. In reservoir engineering, although the industry continues to guide decision making processes mainly based on physics-based models and simulations, the focus of further developments of the industrial workflows has shifted towards hybrid solutions incorporating machine learning and big data analytics. Development of such solutions requires new approaches to integrate the reservoir physics into the workflows suitable for machine learning and big data analytics.

In this paper, we apply and test new metrics for permanent well monitoring developed based on time-lapse pressure transient analysis, called PTA-metrics. These metrics, inheriting reservoir mechanics gained from PTA, remain comparatively simple and suitable for automated workflows. The metrics have been tested on real well data from sandstone and carbonate fields, including slanted injection and horizontal production and injection wells. The testing has confirmed its capabilities in well monitoring separating reservoir from well-reservoir connection contributions to well performance. Application of the metrics enables on-the-fly well monitoring and alarming on well performance issues highlighting the issue origin: either a reservoir or a well-reservoir connection. At the same time, the testing also revealed that reliable application of the metrics depends on the patterns developed by time-lapse pressure transient responses and their Bourdet derivatives. It was shown that the PTA-metrics give reliable results for stable patterns, while change in the pattern may reduce their reliability. The paper concludes with a discussion of ways for application of the metrics in every-day well and reservoir monitoring practice as well as their integration in automated data interpretation workflows developed in the industry.
Potential of Deep Eutectic Solvents in the Upstream Oil and Gas Industry
Authors: Hamdi Zakaria , Koshekov Shaberdi , Bataee Mahmood
Published: June 2023

Oil and gas will be the main part of our future energy sources, despite of emerging and expanding of renewable energies. Enhanced Oil Recovery (EOR) plays an important role in the future oil and gas industry as the conventional oil reserves will shrink. Heavy oil reservoirs will be the main target of EOR methods because of the high number of existing heavy oil reservoirs. Surfactants are the most efficient chemical EOR method for many unconventional reservoirs as previous studies suggest. The problem with this EOR method is that these projects have high costs and raised environmental concerns. Ionic liquids (ILs) are introduced as an alternative material to surfactants, however, the cost of their synthesis and purification processes are high. Besides, some of them are toxic and have non-biodegradable properties which limit their commercial usage. Recently, a new form of ILs produced and called Deep Eutectic Solvents (DESs). The discovered material is more affordable and environmentally friendly and hence, it is considered to be an alternative material for existing conventional ILs. DESs are cheap, easy to produce, non-toxic, reusable, bio-degradable, and environmentally friendly. These materials consist of two or more cheap and safe components which will form a eutectic mixture. The melting point of the final mixture is lower than its components. In this study, the effectiveness of DESs in the EOR is analysed and evaluated to consider it as a new injection material for chemical EOR. This material has specific properties which improve the efficiency of EOR processes. Some of the favourable properties are stable emulsion between phases, interfacial tension (IFT) reduction, wettability change, recovery enhancement, and avoiding formation damage which is discussed and analysed in this paper. Moreover, the cost of the process is hugely reduced compared with other chemical injection methods. As the result, the main factor for the recovery enhancement is wettability alteration and the chance of viscosity. Besides, only malonic and acid-based DESs formed emulsions with oil, and the other types of DESs did not show emulsification properties. The IFT value increased for heavy oil reservoirs, while for reservoirs with light/medium oil IFT was reduced. Furthermore, DESs did not show formation damage which is a bonus point for this method. As the final result, Choline Chloride Glycerol showed the best recovery with an extra 30% to the original production.
The Role of Diffusion on the Reservoir Performance in Underground Hydrogen Storage
Authors: Arekhov Vladislav , Clemens Torsten , Wegner Jonas , Abdelmoula Mohamed , Manai Taoufik
Published: June 2023

For large scale seasonal storage, Underground Hydrogen Storage (UHS) can be used to balance fluctuating sustainable energy generation and energy demand. Similar to underground natural gas storage, depleted gas fields potentially allow for cost-efficient hydrogen storage. One of the major cost factors in UHS is the amount of cushion gas required and the purity of the hydrogen produced during the production cycle.

The hydrocarbon gas remaining in the reservoir can be used as cushion gas to significantly reduce UHS costs. To evaluate the composition of the gas produced during the production cycle of UHS, numerical simulation was applied. One of the important processes in UHS is molecular diffusion within the reservoir. The hydrogen recovery factor and methane to hydrogen production ratio were compared for cases with and without diffusive mass flux. Furthermore, a sensitivity analysis was carried out to identify important factors for UHS. The following parameters were investigated: permeability contrast, vertical to horizontal permeability ratio, reservoir heterogeneity, binary diffusion coefficient, and pressure dependent diffusion. In addition, the effects of numerical dispersion on the results were evaluated and are discussed.

The results of numerical simulation show the importance of diffusion on hydrogen storage in depleted gas reservoirs. Molecular diffusion plays a major role in case of heterogeneous reservoirs and large permeability contrasts. In low permeability zones, the diffusive mass transport becomes dominant over advective flux. Hydrogen propagates further into the low permeable layers of the reservoir when diffusion effects are considered compared with the cases neglecting diffusion. Similar effects are observed during the production cycle. Hydrocarbon gas in low permeability zones becomes more mobile due to diffusive transport. Thus, a larger amount of methane is back-produced with hydrogen for the cases when diffusion is simulated. It is shown that if molecular diffusion is ignored, the hydrogen recovery factor can be overestimated by up to 9% during the first production cycle and the onset of methane contamination can be underestimated by half of the back production cycle. Simulating pressure dependent diffusion might be important for specific configurations and should be covered in a sensitivity.

The results show that molecular diffusion within the reservoir has an impact on the onset of methane contamination when hydrocarbon gas is used as cushion gas in UHS. Also, the total amount of hydrogen produced is overestimated. For UHS operations, both, the composition and amount of hydrogen is important to design facilities and to determine the economics of UHS and hence diffusion should be evaluated in UHS simulation studies.
Fluid Modeling of Underground Hydrogen Storage in a Depleted Natural Gas Field
Authors: Nielsen Markus Hays , Arekhov Vladislav , Whitson Curtis Hays , Clemens Torsten , Zhainakov Timur , Wegner Jonas
Published: June 2023

Underground Hydrogen Storage (UHS) allows the storage of energy that is generated by fluctuating renewable energy sources such as solar and wind. Depleted hydrocarbon fields can be used to store hydrogen. The remaining hydrocarbon gas can be used as cushion gas. To engineer the UHS process, accurate phase, volumetric and transport behavior ("PVT") of hydrogen-hydrocarbon mixtures is required. In this paper, we develop an EOS and viscosity model to describe the operating envelope of a UHS operation in Austria.

Constant Composition Expansion (CCE) experiments were performed using a customized visual HPHT PVT set-up minimizing volume and density errors to ensure high accuracy of the measurements involving hydrogen. Viscosity experiments were performed using a capillary rheometer. Both experimental setups show a total measurement uncertainty of less than 2%. Experiments were performed for various hydrogen- hydrocarbon mixtures to cover the full range of the depleted gas field which is considered. The composition of hydrocarbon-hydrogen mixtures was confirmed using gas chromatography. The results were used to develop an EOS for the hydrogen-hydrocarbon system and to "tune" reduced density corresponding state models to match measured viscosity data.

The measured PVT and viscosity data of hydrogen-hydrocarbon mixtures measured in this study deviate somewhat from the default fluid models used in most commercial simulators. In this paper, a fluid model was developed using the Peng-Robinson EOS with volume shifts, and a reduced density corresponding state LBC viscosity model [1]. The fluid model was matched to (1) hydrogen-hydrocarbon gas laboratory measurements presented in this paper, (2) measured hydrogen-methane binary data (density and viscosity) taken from the literature, and (3) REFPROP (NIST) [2] calculated density and viscosity data for the hydrogen-hydrocarbon gas, hydrogen-methane binary system, and pure components. The required alteration (tuning) of the parameters in the fluid model development is discussed.

The impact of hydrogen content on gas mixture viscosity is studied based on a large number of literature studies for the hydrogen-methane binary system, and the hydrogen-hydrocarbon gas system presented in this paper for relevant operating conditions. Some literature data for hydrogen-methane systems show an anomalous, near-constant gas viscosity behavior at constant pressure and temperature with increasing hydrogen content, until a critical hydrogen content is reached (>50 mole%). Similar behavior is also seen in the hydrogen-hydrocarbon gas mixture presented in this paper.
Modelling of Hydraulic Fracturing Risk on Cement Sheath Integrity
Authors: Cerasi Pierre Rolf , Todorovic Jelena
Published: June 2023

Finite element simulations investigate the effect of cement sheath length and proximity to perforations, varying the spacing of these and their length. The motivation is to assess the well integrity risk in fracturing operations, typical in shale gas or tight sandstone stimulation operations. Other applications are de-risking of injection operations in CO2 or H2 storage. Simplified simulations highlight unwanted associated fracturing in hydraulic fracturing operations, or as additional consequences of unplanned fracturing occurring due to injectivity impairment. Several cases are simulated: 1. Cased, cemented and perforated horizontal well interval; 2. Open hole perforated horizontal well interval; 3. Vertical cased and cemented interval. Results show that under some circumstances, fracturing may be expected in the well's cement sheath. These are locations where simulations show a possible fracturing scenario which could lead to unwanted pressure communication. However, the simulations all agree that there is no induced fracturing at more distant cement sheath sections. The major risk shown here is fracture initiation close to the perforated interval, suggesting that propagation along the cement sheath, although possible, is less likely.
Transient Pressure Interference During CO2 Injection in Saline Aquifers
Authors: Zeidouni Mehdi
Published: June 2023

CO2 injection in subsurface geological formations (e.g. deep saline aquifers) causes pressure perturbations over a large area surrounding the injection well. Observation wells are widely considered in geologic CO2 storage (GCS) projects where the pressure perturbation induced by CO2 injection is measured. In this work, we use analytical and numerical modeling tools along with field data to examine the pressure behavior in GCS projects before and after CO2 arrival at an observation well. Prior to CO2 arrival, a baseline pressure trend is established which corresponds to single-phase brine flow across the observation well (approximated by Theis solution). Therefore, analysis of early-time pressure data is straightforward, provides the single-phase flow characteristics (mobility and storativity), and helps establishing a baseline pressure change that can be extended beyond the single-phase flow period at the observation well.

Upon CO2 arrival, a departure from this baseline trend is expected. For the pressure to detect the CO2 arrival at an observation well, the departure from baseline pressure behavior must be significant and well above the background noise levels. We use existing analytical models to determine the strength of the expected pressure departure signal from the baseline trend upon CO2 arrival. The strength of the expected pressure departure is found to be directly proportional to the mobility ratio. Accordingly, we establish a criterion to determine whether the pressure at an observation well can detect the CO2 arrival. We present an analysis approach through application to synthetic and field data and show the characteristic pressure behavior before and after CO2 arrival. We show that while generally the pressure can be either above or below the expected baseline pressure trend, it would be likely above the baseline upon CO2 arrival. This is because the mobility ratio becomes less than unity after CO2 arrival. We show that depending on the reservoir characteristics, changes in the pressure trend may or may not be sufficient to detect the CO2 arrival.
Ising Meson Spectroscopy on a Noisy Digital Quantum Simulator
Authors: Roy Ananda , Lamb Christopher , Tang Yicheng , Davis Robert
Published: June 2023

Quantum simulation has the potential to be an indispensable technique for the investigation of non-perturbative phenomena in strongly-interacting quantum field theories (QFTs). In the modern quantum era, with Noisy Intermediate Scale Quantum (NISQ) simulators widely available and larger-scale quantum machines on the horizon, it is natural to ask: what non-perturbative QFT problems can be solved with the existing quantum hardware? We show that existing noisy quantum machines can be used to analyze the energy spectrum of a large family of strongly-interacting 1+1D QFTs. The latter exhibit a wide-range of non-perturbative effects like ‘quark confinement’ and ‘false vacuum decay’ which are typically associated with higher-dimensional QFTs of elementary particles. We perform quench experiments on IBM’s ibmq_mumbai quantum simulator to compute the energy spectrum of 1+1D quantum Ising model with a longitudinal field. The latter model is particularly interesting due to the formation of mesonic bound states arising from a confining potential for the Ising domain-walls, reminiscent of t’Hooft’s model of two-dimensional quantum chromodynamics. Our results demonstrate that digital quantum simulation in the NISQ era has the potential to be a viable alternative to numerical techniques such as density matrix renormalization group or the truncated conformal space methods for analyzing QFTs.
Reservoir operation method based on surrogate model and dynamic reservoir capacity
Authors: Zhu Sipeng , Qin Hui , Liu Huaiyuan , Qin Chao , Huang Jingwei , Wu Songlin , Niu Xinqiang
Published: June 2023

In recent years, more and more attention has been paid to the refined operation of reservoirs. Precise operation of reservoirs requires accurate calculation of reservoir capacity. Although hydrodynamic models can accurately calculate dynamic storage capacity, they cannot be used in reservoir operation models due to computational efficiency. This study uses a hydrodynamic model based on ELM to guide the training of PSTA-TCN as a proxy model. The dynamic storage capacity of the reservoir is included in the calculation when generating training data. The experimental results show that the proxy model can be used as a method to accurately calculate the reservoir capacity during the refined reservoir operation.
Voice activity detection for piano online teaching based on digital network system
Authors: Xiao Yayun
Published: June 2023

The purpose of this paper is to build a dedicated network dedicated to a complete digital network. So we investigated the necessity of network construction and digital and communication. After a lot of digital network research and detailed analysis of the system, the use plan of the report and further analysis. The purpose of the piano network system design is to solve traditional face-to-face education problems such as economy, time-consuming, physics, transportation, etc., and to make effective use of rapid development. In particular, it integrates all aspects of society with the network piano itself as learning Platform. By designing the resources and network system platform for effective piano learning, it can not only save social resources, but also provide convenience for students and teachers. This article first explains the basic principles related to voice activity detection, expounds the voice signal model, discusses the higher ratio of parameters used in conventional voice activity detection activities, and promotes simulation-related experiments to verify it. With the advent of the era of big data and the increase in network bandwidth, the previously limited network bandwidth resources have been improved, allowing users to perform a large number of online management. For example, piano online teaching technology based on broadband Internet, remote Internet in the field of information transmission, control technology, and piano, especially the Internet-based piano online teaching system, are all important industries in the era of big data.
Assessing the global impact of targeted conservation actions on species abundance
Authors: Blackburn Tim , Dove Shawn , Geldmann Jonas , Gregory Richard , Hoffmann Michael , McRae Louise , Visconti Piero , Jellesmark Sean
Published: June 2023

Globally, vertebrate abundance has declined, and species’ extinction rates have increased, at unprecedented rates over recent decades 1,2. In response, targeted species interventions, such as conservation breeding programmes, legislative measures or species-specific habitat management, have been used to help address population declines and reduce and reverse the loss of biodiversity 3,4. Until now, assessments of conservation actions have focused on the extent to which they reduce extinction risk or focus on the impact of protected areas 4–9. Here, we extend our knowledge of conservation efficacy by quantifying the impact of targeted conservation actions on vertebrate abundance, analyzing data from 26,904 vertebrate populations across 4,629 species worldwide. Using a counterfactual approach to represent population trends in the absence of conservation, we show that targeted actions have delivered substantial positive effects on the abundance of recipient vertebrate populations worldwide. Positive trends were especially observed in vertebrate populations subject to species or habitat management. Our results provide compelling evidence that, with sufficient and sustained resourcing and commitment, successful implementation of Target 4 of the recently adopted Kunming-Montreal Global Biodiversity Framework can readily help reverse global biodiversity loss and put nature on a path to recovery.
Studies on the Effect of Curcumin and Quercetin in the Liver of Male Albino Rats Exposed to Gamma Irradiation
Authors: Ali Said A. , Azzoz Rady M. , Soliman Saeed M. , Abdelrahman Ibrahim Y. , Khalil Wafaa M. , El-Hady Amr M. Abd
Published: June 2023

Ionizing radiation produces deleterious effects on living organisms. The present investigation has been carried out to study the prophylactic as well as the therapeutic effects of treated rats with quercetin (Quer) and curcumin (Cur) which are two medicinal herbs known for their antioxidant activities against damages induced by whole-body fractionated gamma irradiation. Exposure of rats to whole-body gamma irradiation induced a significant decrease in erythrocyte (RBC), leukocyte (WBCs), platelets count (Plt), hemoglobin concentration (Hb), hematocrit (Hct %), mean erythrocyte hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and mean erythrocyte volume (MCV), high increase in plasma Thiobarbituric acid reactive substances (TBARS), non-significant statistical decrease in the mean value of serum glutathione (GSH), with a significant increase in plasma alanine transferase (ALT), aspartate transferase (AST), alkaline phosphates (ALP), serum total protein, serum total cholesterol levels, total triglycerides levels, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels with marked histological changes and structural changes measured by Fourier Transform Infrared (FTIR). Applying both quercetin and curcumin pre- and post-exposure to gamma-radiation revealed a remarkable improvement in all the studied parameters. The cellular damage by gamma radiation is greatly mitigated by the coadministration of curcumin and quercetin before radiation exposure.
Classification and Interpretation of ECG Arrhythmia through Deep Learning Techniques
Authors: A Shobanadevi , T Veeramakali
Published: June 2023

Digitizing healthcare systems is highly essential to identify diseases at an early stage and thus preventing any catastrophes regarding people’s health. One such critical health issue that requires attention in its initial stages itself is Arrhythmia. The research carried out in this paper mainly focuses on Arrhythmia classification and proposes a model to classify ECG signals into different classes of Arrhythmia based on the AAMI standard along with model interpretation. The benchmark ECG MIT-BIH Arrhythmia dataset has been used for training and testing purposes throughout the research. This research proposes the use of various deep learning and data sampling techniques like CNN, RNN, oversampling and under sampling methods in order to build the model that is to be used for Arrhythmia classification. This proposed CNN model outperforms the existing models in terms of different metrics like accuracy, precision, recall, f1-score and reduced prediction time of a sample from 6.23 seconds to 2.09 seconds.
Development of an SNMP v3 agent for user modelling in LAN environments for thin films by using Dynamic systems modelling
Authors: Mohamad A.A.Hamad , Ahmed Faris Maher
Published: June 2023

This article presents the development of an SNMP v3 agent for user modelling in LAN environments. This agent establishes SNMP communications both with the network managers in charge of configuring the modelling process and with the users from whom it collects information contained in the MIBs (Management Information Base) to find a pattern that characterizes their behaviour. This information will be processed and analyzed by a neural network type SOM (Self Organizing Map), which will allow, after the learning process, the detection of anomalies concerning the normal behaviour of the user. Both the parameters to be configured to define the modelling of each user and the results of the agent's supervision are collected in the modelling MIB contained in the proposed agent. In this way, the developed agent provides a unique tool to model all the users of the same LAN network and constitutes a fully integrated system in the SNMP architecture. Finally, a test scenario is presented for the application of the intrusion detection of the proposed agent.
Control Dynamic System and Qos Manager Agent Over Ipv6 Networks: Intserv and Diffserv Approach in Access Nodes
Authors: Mohamad A.A.Hamad , Hussein Husam Abdulhameed
Published: June 2023

In the last ten years, the growth of IP-based data networks has led to the creation of new applications, which in turn have led to more data being sent. But the bandwidth of intradomain and interdomain transport networks has not grown at the same rate. In the last decade, the growth of IP-based data networks has led to the development of new applications that have increased the amount of data that can be sent. But the bandwidth of both intra-domain and inter-domain transport networks has not grown at the same rate. The present work is to look at how to provide QoS, which is a problem that is currently being looked into and for which there is no clear process or algorithm for a solution. A model that takes advantage of the features of the IPv6 protocol is also being used to find a solution. IPv6 is a very important protocol for the growth and development of Internet services and for supporting new services. But IPv6 doesn't have QoS in and of itself, even though it has different fields. This research aims to solve the QoS problem by trying to find the best way to combine the areas that IPv6 offers. The QoS manager agent was tested with MPLS in a congestion scenario. As a result, the values for jitter and delay for the highest priority flows got a lot better. High-priority flows are those that carry real-time traffic that can't handle delays or packet loss. The fact that the QoS manager agent didn't lose more than 3% of all packets, no matter how much traffic came into the node, shows that priority traffic is protected against network congestion problems. This feature is not present in the MPLS scenario, and as traffic grows, packet loss also grows. The QoS manager agent makes these characteristics better, but it makes them worse in low-level flows. Low-priority flows, on the other hand, are things like web browsing, file transfers, etc., that don't care much about time. The way these flows work is based on connection-oriented protocols like TCP. When packets are lost, the anti-congestion features of this protocol are turned on. The QoS manager agent doesn't have any way to measure incoming flows. This means that high throughput flows can fill up the classes, which goes against management policies, causes packet loss, and changes delay and jitter parameters. To improve the performance of the QoS manager agent in the future, it is suggested that a QoS provisioning protocol be created between a host and a border router. This would allow the agent to have more control over the flows that enter it and would improve the agent's performance.