bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengaruh Kombinasi Pakan dan Probiotik yang Berbeda Terhadap Kinerja Pertumbuhan Udang Galah (Macrobrachium rosenbergii) dan Penurunan Limbah N yang Dipelihara Dalam Wadah Terkontrol
Authors: Samadan Gamal Mustik , Muchdar Fatma , Sriwati Sriwati , Findra Muhammad Nur 
Published: June 2023

ABSTRAK Udang Galah (Macrobranchium rosenbergii) merupakan salah satu jenis crustacea, yang mempunyai ukuran terbesar dibandingkan dengan udang air tawar lainnya. Penggunaan probiotik terhadap perbaikan mutu kualitas air kinerja pertumbuhan hewan budidaya sudah lazim digunakan. Namun demikian, pada budidaya udang galah masih sangat terbatas informasinya. Penelitian ini bertujuan mengetahui jenis probiotik yang berpengaruh dalam budidaya udang galah (M. rosenbergii), yang dipelihara dalam wadah terkontrol. Penelitian dilaksanakan selama 60 hari di UPT Laboratorium Terpadu Universitas Khairun, Kota Ternate Provinsi Maluku Utara. Penelitian dirancang dengan rancangan acak lengkap (RAL) yang terdiri dari empat perlakuan dan tiga kali ulangan. Perlakuan yang dicobakan adalah probiotik yang berbeda, yaitu probiotik boster planktop (10 ml), probiotik boster sel multi (10 ml), probiotik boster bio lakto (10 ml), dan tanpa. Hasil analisis menunjukkan pemberian probiotik boster planktop signifikan berpengaruh pada kelangsungan hidup udang galah, sedangkan pertumbuhan mutlak, laju pertumbuhan spesifik dan rasio konversi pakan tidak signifikan. Disamping itu, pemberian probiotik yang berbeda menurunkan konsentrasi amoniak dan nitrit, dan juga mempertahankan kualitas air media pemeliharaan.Kata kunci: Probiotik, pertumbuhan, limbah N, udang galahABSTRACTGiant Shrimp (Macrobranchium rosenbergii) is one type of crustacean, which has the largest size compared to freshwater shrimp. The use of probiotics to improve water quality, growth performance of cultivated animals is commonly used. However, the information on giant prawn cultivation is still very limited. This study aims to determine the types of probiotics that have an effect on the cultivation of giant prawns (M. rosenbergii), which are kept in controlled containers. The research was carried out for 60 days at the UPT Integrated Laboratory of Khairun University, Ternate City, North Maluku Province. The study was designed with a completely randomized design (CRD) consisting of four treatments and three replications. The treatments tested were different probiotics, namely planktop booster probiotics (10 ml), multi cell booster probiotics (10 ml), bio lacto booster probiotics (10 ml), and without. The results of the analysis showed that different probiotics had an effect on the survival of giant prawns, while absolute growth, specific growth rate and feed conversion ratio were not significant. In addition, the administration of different probiotics reduces the concentration of ammonia and nitrite, and also maintains the water quality of the maintenance media.Keywords: Probiotics, growth, waste removal, giant prawns
PENGAMBILAN KEPUTUSAN RESELLER FROZEN FOOD SEBAGAI PILIHAN BERWIRAUSAHA DI MASA PANDEMI
Authors: Irawati Rina 
Published: June 2023

ABSTRAKSaat ini banyak peluang bisnis yang dapat dilakukan di rumah dengan modal yang minim namun dapat memperoleh laba yang besar, salah satunya usaha reseller frozen food. Penelitian ini dilakukan untuk menguji pengaruh antara lingkungan internal dan eksternal secara parsial terhadap keputusan berwirausaha menjadi reseller frozen food di masa pandemi. Sampel penelitian sejumlah 44 orang dengan simple random sampling menggunakan rumus Slovin. Seluruh item penelitian telah teruji valid dan reliabel, serta sudah memenuhi kriteria uji asumsi klasik. Uji hipotesis membuktikan bahwa lingkungan internal dan eksternal berpengaruh terhadap keputusan berwirausaha sebagai reseller frozen food. Variabel yang dominan berpengaruh adalah faktor internal dengan nilai beta terbesar yaitu 0.784.Kata kunci: Pengambilan Keputusan, Kewirausahaan, Reseller Frozen Food ABSTRACTCurrently, there are many business opportunities that can be done at home with minimal capital but can earn large profits, one of which is a frozen food reseller business. This study was conducted to partially examine the effect of the internal and external environment on the entrepreneurial decision to become a frozen food reseller during the pandemic. The research sample was 44 people with simple random sampling using the Slovin formula. All research items have been tested to be valid and reliable, and have met the criteria for the classical assumption test. Hypothesis testing proves that the internal and external environment influence the entrepreneurial decision as a frozen food reseller. The dominant variable influencing is the internal factor with the largest beta value of 0.784. Keywords: Decision Making, Entrepreneurship, Frozen food reseller
Challenges in Simulation of Salt Clogging
Authors: Khosravi Maryam , Xu Yao , Mirazimi Seyedamir , Stenby Erling Halfdan , Yan Wei 
Published: June 2023

Abstract
Carbon sequestration in depleted reservoirs or aquifers is highly demanded but still faced with technical challenges in many aspects. Among them, losing well injectivity during the storage process is a major concern. This can be caused by salt deposited in the reservoir, particularly near the injection well, which may sometimes creep into the injection well. Therefore, it is desirable to estimate the amount and distribution of salt precipitation at the injection conditions for a smooth implementation of CO2 sequestration. In this paper, we investigate how much commercial software CMG-GEM can help the evaluation of salt precipitation. We first review the critical mechanisms involved in salt precipitation and then analyze the challenges in simulating these mechanisms.
According to the literature, water saturation and saturation index are the two most influential parameters that control the amount and pattern of salt precipitation and clogging due to water vaporization. Their values are determined by the complex interplay between viscous force, gravity, the evaporation of water into the CO2 stream, the molecular diffusion of dissolved salt in the brine, and surface phenomena such as the spreading of a thin water film on the rock surface, the Marangoni convection, and disjoining suction. Here we investigate the challenges of simulating the aforementioned mechanisms as well as salt precipitation due to the backflow of brine toward the injection well.
The surface-related phenomena are difficult to account for in simulation. However, the extent of the CO2 plume can be significantly underestimated if they are neglected. Although water vaporization, salt diffusion, and capillary pressure can be formally included in the simulation, it is arguable whether they always describe the actual phenomena adequately. In most cases of CO2 injection into an aquifer, water spreads all over the rock surface, which increases the rate of vaporization and surface-related phenomena, such as the Marangoni effect, dramatically. Marangoni turbulent fluxes originating from the unbalanced shear stresses on the interface can accelerate the mixing effect in homogenizing the ions composition, which results in self-enhanced salt precipitation via the thin brine film spreading on the rock surface. We examine different simulation techniques as remedies to mimic those phenomena.
A Robust General Physics-Informed Machine Learning Framework for Energy Recovery Optimization in Geothermal Reservoirs
Authors: Xu Zhen , Yan Bicheng , Gudala Manojkumar , Tariq Zeeshan 
Published: June 2023

Abstract
Energy extraction from the Enhanced Geothermal System (EGS) relies on hydraulic fractures or natural fractures to migrate fluid and thus extract heat from surrounding rocks. However, due to the heterogeneity and complex multi-physics nature inside of fracture plane, high-fidelity physics-based forward simulation can be computationally intensive, creating a barrier for efficient reservoir management. A robust and fast optimization framework for maximizing the thermal recovery from EGS is needed.
We developed a general reservoir management framework which is combining a low-fidelity forward surrogate model (fl) with gradient-based optimizers to speed up reservoir management process. Thermo-hydro-mechanical (THM) EGS simulation model is developed based on the finite element-based reservoir simulation. We parameterized the fracture aperture and well controls and performed the THM simulation to generate 2500 datasets. Further, we trained two different architectures of deep neural network (DNN) with the datasets to predict the dynamics (pressure and temperature), and this ultimately becomes the forward model to calculate the total net energy. Instead of performing optimization workflow with large amount of simulations from fh, we directly optimize the well control parameters based on geological parameters to the fl. As fl is efficient, accurate and fully differentiable, it is coupled with different gradient-based or gradient-free optimization algorithms to maximize the total net energy by finding the optimum decision parameters.
Based on the simulation datasets, we evaluated the impact of fracture aperture on temperature and pressure evolution, and demonstrated that the spatial fracture aperture distribution dominates the thermal front movement. The fracture aperture variation is highly correlated with temperature change in the fracture, which mainly results from thermal stress changes. Compared to the full-fledged physics simulator, our DNN-based forward surrogate model not only provides a computational speedup of around 1500 times, but also brings high predictive accuracy with R2 value 99%. With the aids of the forward model fl, gradient-based optimizers run optimization 10 to 68 times faster than the derivative-free global optimizers. The proposed reservoir management framework shows both efficiency and scalability, which enables each optimization process to be executed in a real-time fashion.
Well Integrity Safety Factor for Tubular Connections in Petroleum, Geothermal, and Carbon Capture Wells
Authors: Goodman Malcolm Arnold 
Published: June 2023

Abstract
Connection sealability is critical for well integrity and carbon control. With current operating practices, there is no way to know that a connection is about to fail and leak before failure actually occurs. API leak equation is uniaxial. Premium connection ratings are biaxial. To measure, monitor, and maintain connection sealability during drilling and production operations, a triaxial Integrity Safety Factor (ISF) for connections is introduced in this paper. Three leak constants evaluated from simple physical tests determine the connection ISFs. Example connection in the paper is 7-inch 35-ppf N80 LTC. Results from FEA leak predictions for 23 load cases, comprising both internal and external leak, show unexpected leak behavior relative to pipe failure.
A Comparative Analysis of Convolutional Neural Networks for Seismic Noise Attenuation
Authors: Fogat Mrigya , Roy Samiran , Ferreira Viviane , Singh Satyan 
Published: June 2023

Abstract
Seismic data is an essential source of information often contaminated with disturbing, coherent and random noise. Seismic random noise has degenerative impacts on subsequent seismic processing and data interpretation. Thus, seismic noise attenuation is a key step in seismic processing. Convolutional Neural Networks (CNNs) have proven successful for various image processing tasks in multidisciplinary fields and this paper aims to study the impact of three CNN architectures (autoencoders, denoising CNNs (DnCNN) and residual dense networks (RDN)) on improving the signal to noise ratio of seismic data. The work consists of three steps: Data preparation, model training and model testing. In this study we have used real seismic data to prepare the training dataset we have manually added noise. Most studies on seismic noise attenuation, study only a single kind of noise. However this paper suggests making our approach by exposing the model to many kinds of noises and noise levels such as Guassian noise, Poisson noise, Salt and Pepper and Speckle noise. In this paper we have analysed the performance of three models. Autoencoders: This architecture consists of two parts, the encoders and the decoders. The encoder consists of convolutions on the input image to extract all key information and map it to a latent space with loss of unnecessary data(noise) while the decoder reconstructs the image from the latent space to a seismic image while high signal to noise ratio. DnCNNs: This architecture is a combination of residual learning and batch normalization and mainly consists of three kinds of blocks. The model is trained to predict the residual image, that is the difference between the noisy observation and the latent clean image. RDNs: This architecture comprises of shallow feature extraction net, residual dense blocks (RDBs), dense feature fusion, and lastly up-sampling net. The data prepared as mentioned above is trained on all three CNN models across different noise levels and the performance of these models was compared.
The model is finally tested on a batch of unseen noisy seismic sections and the performance is measured by an l2 loss namely mean squared error and the improvement in signal to noise ratio.
The resultant images from all three architectures across different noise levels have drastically improved signal to noise ratio and thus the application of CNN as a denoiser for seismic images proves to be successful. It is important to note that when comparing the difference plots(Noisy image minus the denoised image) we found minimal signal leakage. While the application of CNN for image pre-processing has seen great success in other fields, mathematical denoising techniques such as F-K filter, tao-p filter are still used in oil and gas industry particularly in seismic denoising. After thorough review, this paper studies some of the most successful denoising CNN architectures and its success in seismic denoising.
Optimization of Carbon-Geo Storage into Saline Aquifers: A Coupled Hydro-Mechanics-Chemo Process
Authors: Tariq Zeeshan , Gudala Manojkumar , Yan Bicheng , Sun Shuyu , Rui Zhenghua 
Published: June 2023

Abstract
The potential for large-scale storage of carbon dioxide (CO2) through Geological Carbon Sequestration (GCS) in deep geological formations such as saline aquifers and depleted oil and gas reservoirs is significant. Effectively implementing GCS requires evaluating the risk of plume confinement and storage capacity at each site through a thorough assessment. To assess the stability of the caprock after CO2 injection, efficient tools are needed to evaluate the safe duration of CO2 injection. This study used Particle Swarm Optimization (PSO) evolutionary algorithm to optimize the maximum CO2 storage capacity in saline aquifers without risking the integrity of the caprock. A deep learning (DL) model, fully connected neural networks, was trained to predict the safe injection duration. The movement of CO2 was simulated for 170 years following a 30-year injection period into a deep saline aquifer using a physics-based numerical reservoir simulator. The simulation took into consideration uncertainty variables such as petrophysical properties and reservoir physical parameters, as well as operational decisions like injection rate and perforation depth. Sampling the reservoir model with the Latin-Hypercube approach accounted for a range of parameters. Over 720 reservoir simulations were performed to generate training, testing, and validation datasets, and the best DNN model was selected after multiple executions. The three-layer FCNN model with 30 neurons in each layer showed excellent prediction efficiency with a coefficient of determination factor over 0.98 and an average absolute Percentage Error (AAPE) less than 1%. The trained models showed a good match between simulated and predicted results and were 300 times more computationally efficient. PSO was utilized to optimize the operational parameters in the DL models to achieve maximum CO2 storage with minimum damage to the caprock. The results suggest that the DNN-based model can serve as a reliable alternative to numerical simulation for estimating CO2 performance in the subsurface and monitoring storage potential in GCS projects.
A Control Volume Material Balance Approach and Its Applications to Real-Time Flow Diagnostics
Authors: Zhang Nan , Andersen Pål Østebø , Rong Chunming 
Published: June 2023

Abstract
In this paper, we propose a Control Volume Material Balance (CVMB) approach for proxy reservoir simulation and apply it to real-time flow diagnostics. Instead of utilizing a comprehensive reservoir simulator, it estimates the saturations distributions by mapping the mass difference between injected and produced fluids recorded at wells into 3D grid blocks. On this basis, we perform real-time flow diagnostics to evaluate the dynamic heterogeneity of the instantaneous displacement flow field which can be used for making effective and opportune decisions to improve oil recovery.
CVMB solves the pressure and flow fields implicitly, and the transport equations explicitly. It incorporates 3D heterogenous rock properties. The fundamental idea of the CVMB method is to divide the 3D flow field into a series of 1D well-pair Control Volumes (CVs). A well-pair Control Volume is composed of grid blocks in the intersection of the sweep and drainage regions of the injector and producer. The fluid flow in and out of the 1D CV can only occur at the wells, and the in-situ fluid volumes are determined by the well flow rates and the well allocation factors. In each CV, we assume the displacement in the grid blocks is piston-like and follows the 1D order of ascending forward time-of-flight. The fluid saturation distributions are determined by defining the cut-off time-of-flight for the displacement front.
We show how the CVMB method improves the pattern-based mass balance approaches in the following aspects: 1) enables real-time flow diagnostics in terms of the hydrocarbon dynamic Lorenz coefficient without a comprehensive reservoir simulator; 2) enhances the simplicity and extensibility of the pattern-based mass balance approach without mapping between grid blocks and streamlines; 3) reduces the smearing effects in conventional mass balance approach by defining 1D CVs using forward time-of-flight.
The proposed CVMB method utilizes the historical well flow rates as the input to estimate the swept regions and its average saturation with remarkable efficiency and sufficient accuracy for real-time flow diagnostics.
CO2 Dissolution Trapping in Depleted Gas Reservoirs with Residual-Gas Mixtures and Bottom Water
Authors: Shen Xudong , Liu Huiqing , Lyu Xiaocong , He Chang , You Lei , Zhang Wentong 
Published: June 2023

Abstract
The depleted gas reservoir with bottom water exhibits great potential for long-term CO2 sequestration. Due to the reservoir heterogeneity and technical limitations, a considerable amount of natural gas resides in the reservoir. The residual gas affects the mass transfer within and between phases, further impacting the behavior of CO2 dissolution and convection in the porous media. Quantitatively characterizing the mass transport behavior of CO2-residual gas mixtures is of great significance for CO2 storage capacity evaluations.
In this work, a series of numerical simulations were performed to investigate the effect of residual-gas mixtures on CO2 dissolution trapping. We first adopted the modified cubic equations of state and fugacity-activity model to calculate the phase behavior of CO2 with different compositions. Then, the Sherwood number of the 2D synthetic model with a capillary transition zone (CTZ) was calculated to explore the mass transfer for different gas mixtures. Besides, we compared CO2 dissolution rate at each stage and characterized the onset and decay time of convection as a function of gas composition to quantify the effect of residual-gas mixtures.
The results indicate that the variation trend of the Sherwood number resulting from the synthetic model with CTZ is similar to that from the single-phase model. The presence of CTZ enhances gravity-induced convection and accelerates CO2 dissolution, whereas the residual-gas mixtures have a negative effect on mass transfer. The increasing residual gas reduces the partial pressure and solubility of CO2. As a result, the concentration and density difference between saturated water and fresh water are mitigated, leading to the suppression of the driving force for CO2 diffusion and convection. Accordingly, the rate of CO2 dissolution, dominated by gravity-induced fingering, is significantly decreased, resulting in a delay in the onset and decay time of convection and a significant decrease in the maximum Sherwood number. Considering the negative influence of residual gas on the solubility trapping of CO2, it is necessary to produce natural gas with CO2 injection in the depleted gas reservoir as possible along with carbon sequestration.
This work explores the relationship between the Sherwood number and dissolution time under more realistic reservoir conditions. In addition, the influence of the capillary transition zone and residual gas concentration on the dissolution trapping is well analyzed. It can provide a reference for practical carbon capture and storage (CCS) projects.
Massive Geomodel Compression and Rapid Geomodel Generation Using Advanced Autoencoders and Autoregressive Neural Networks
Authors: Misra Siddharth , Chen Jungang , Falola Yusuf , Churilova Polina , Huang Chung-Kan , Delgado Jose 
Published: June 2023

Abstract
The reduction of computational cost when using large geomodels requires low-dimensional representations (transformation or reparameterization) of large geomodels, which need to be computed using fast and robust dimensionality reduction methods. Additionally, to reduce the uncertainty associated with geomodel-based predictions, the probability distribution/density of the subsurface reservoir needs to be accurately estimated as an explicit, intractable quantity for purposes of rapidly generating all possible variability and heterogeneity of the subsurface reservoir. In this paper, we developed and deployed advanced autoencoder-based deep-neural-network architectures for extracting the extremely low-dimensional representations of field geomodels. To that end, the compression and reconstruction efficiencies of vector-quantized variational autoencoders (VQ-VAE) were tested, compared and benchmarked on the task of multi-attribute geomodel compression. Following that, a deep-learning generative model inspired by pixel recurrent network, referred as PixelSNAIL Autoregression, learns not only to estimate the probability density distribution of the low-dimensional representations of large geomodels, but also to make up new latent space samples from the learned prior distributions. To better preserve and reproduce fluvial channels of geomodels, perceptual loss is introduced into the VQ-VAE model as the loss function.
The best performing VQ-VAE achieved an excellent reconstruction from the low-dimensional representations, which exhibited structural similarity index measure (SSIM) of 0.87 at a compression ratio of 155. A hierarchical VQ-VAE model achieved extremely high compression ratio of 667 with SSIM of 0.92, which was further extended to a compression ratio of 1250 with SSIM of 0.85. Finally, using the PixelSNAIL based autoregressive recurrent neural network, we were able to rapidly generate thousands of large-scale geomodel realizations to quantify geological uncertainties to help further decision making. Meanwhile, unconditional generation demonstrated very high data augmentation capability to produce new coherent and realistic geomodels with given training dataset.