bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pemanfaatan Ubi Jalar sebagai Alternatif Karbohidrat yang Meningkatkan Ekonomi Warga Banten
Authors: Aisy Rihadatul , Putri Gita Nur Aisyah , Aulia Nabila Nurul , Salsabila Nuriana , Indrawati Sherly , Madani Willy Faisal , Khastini Rida Oktorida 
Published: June 2023

<p>Ubi jalar (<em>Ipomoea batatas </em>L.) merupakan salah satu komoditas yang nilai ekonomisnya tinggi dan mempunyai banyak manfaat dan kandungan nutrisi yang berguna bagi kesehatan tubuh manusia. Tujuan penelitian ini adalah untuk mengetahui persepsi masyarakat terhadap konsumsi ubi jalar sebagai pangan lokal alternatif karbohidrat dan upaya yang dilakukan oleh masyarakat Banten dalam pemanfaatan potensinya untuk meningkatkan sektor ekonomi. Penelitian kuantitatif dilakukan metode penelitian survei deskriptif kepada 57 responden masyarakat Sindangkarya Kabupaten Pandeglang. Hasil penelitian menunjukkan bahwa masyarakat Banten memiliki ragam persepsi terkait ubi jalar, potensi dan pemanfaatannya. Informasi yang diperoleh dapat dijadikan sebagai dasar pengembangan ubi jalar yang berkelanjutan sehingga dapat meningkatkan aspek ekonomi masyarakat lokal Banten.</p><p> </p><p><strong>Kata kunci</strong> : Ubi jalar; karbohidrat; alternatif pangan.</p>
Pendampingan Pengelolaan Keuangan Berbasis Digital Finance Syariah Pada Yayasan Fathu Minal Fityan
Authors: Widyastuti Reni Dwi , Setiawan Aris , Sari Wilda , Ekawati Ekawati , Purwanto Purwanto 
Published: June 2023

<p>Pengelolaan keuangan sangat penting dilakukan oleh berbagai pihak termasuk pondok pesantren. Namum pada kenyataanya masih terdapat banyak pondok pesantren yang belum dapat melakukan pengelolaan keuangan dikarenakan oleh berbagai factor. Diantaranya adalah kurangnya pemahaman pengurus pondok pesantren dalam melakukan pengelolaan keuangan yang baik dan benar. Tujuan dari program pengabdian kepada masyarakat ini adalah untuk memberikan pelatihan dan pendampingan pengelolaan keuangan berbasis akuntansi syariah pada pondok pesantren. Pengabdian dilaksanakan pada Yayasan Fathu Minal Fytian Kalimantan Barat. Pelatihan dan pengelolaan keuangan yang dilakukan berbasis Digital Finance Syariah dengan menggunakan Aplikasi SYIAR. Kegiatan PKM ini diawali dengan proses survei permasalahan, penyuluhan atau penyampaian materi, dan pendampingan dalam praktik administrasi dan pengelolaan keuangan. Setelah program ini selesai diharapkan pengelola pondok pesantren dapat melakukan pengelolaan keuangan dengan baik menggunakan aplikasi digital.</p><p> </p><p><strong>Kata kunci</strong> : Pengelolaan Keuangan; <em>Digital Finance</em> Syariah; SYIAR</p>
Reconstruction of the Distal Humerus in a Post-traumatic Arthritic Elbow with a Frozen Osteochondral Allograft Using CT-based Preoperative 3D Planning
Authors: Russo Raffaele , Guastafierro Antonio , Mortellaro Marco , Minopoli Paolo , Pietroluongo Livia Renata 
Published: June 2023

Case:
We present a young patient with malunion of the Type IV distal humeral coronal shear fracture and post-traumatic arthritis of the elbow joint. He was treated with a frozen osteochondral allograft transplant using CT-based preoperative 3D planning. At 64 months after surgery, both the clinical and radiological results were satisfactory and no complications had been reported.

Conclusion:
The precision of computer-aided surgical planning could assist in preoperative designing and preparation of a personalized elbow osteochondral allograft. Even in a chronic situation, a personalized treatment approach can allow for osseointegration and satisfactory clinical results.
Reservoir Fluid Typing from Standard Mud Gas - A Machine Learning Approach
Authors: Cely Alexandra , Siedlecki Artur , Liashenko Artur , Yang Tao , Donnadieu Sandrine 
Published: June 2023

Abstract
Standard mud gas data is part of the basic mudlogging service and is used mainly for safety monitoring. Although the data is available for all wells, it is not used for reservoir fluid typing due to poor prediction accuracy. We recently developed a new manual method and significantly improved the reservoir fluid typing accuracy from standard mud gas data. However, there is a strong business for an automatic method to enable reservoir fluid interpretation while drilling.
A machine learning method has been developed based on a well-established standard mud gas database. The standard mud gas compositions contain methane, ethane, and propane components with reasonable quality measurements. The butane and pentane compositions in the standard mud gas are low and sometimes close to the detection limit. Therefore, we only use methane to propane compositions in the machine learning algorithm. It is particularly challenging to predict reservoir fluid type accurately based on only three gas components. Therefore, we introduce additional data sources to increase the prediction accuracy: a large in-house reservoir fluid database and petrophysical logs.
The machine learning algorithm extracts critical reservoir fluid information specifically for a known field by utilizing the geospatial location and the existing reservoir fluid database. When combined with the standard mud gas database, the reservoir fluid typing accuracy increased from 50-60% to nearly 80%. Petrophysical logs are the main tool in the industry to identify the reservoir fluid type. When combining the petrophysical logs with the machine learning model already with satisfactory performance, the final reservoir fluid type prediction accuracy is about 80%. Given the difficulties of distinguishing oil or gas for near-critical fluids or volatile oil, the current prediction accuracy is sufficient for industry applications.
The innovation created significant business opportunities based on the standard mud gas, which has been regarded as not applicable data for accurate reservoir fluid typing for many decades. The new method makes accurate reservoir fluid typing possible for real-time well decisions like well placement, completion, and sidetracking. In addition, the new method can add lots of value for well integrity, maturating production targets, and cost-efficient Plug and Abandonment (P&A) in the overburden.
Efficient FDP Optimization for AI Enhanced Decision Making
Authors: De Paola Giorgio , Danger Richar Villarroel 
Published: June 2023

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.