bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengoptimalisasian Waktu dan Biaya pada Proyek Pembangunan Gedung Asrama 5 Lantai Lembaga Penjaminan Mutu Pendidikan di Sumatera Barat dengan Menggunakan Aplikasi Primavera P6
Authors: Immanuel Ivander , Setiono Setiono , Rifai Muji 
Published: March 2022

<p align="justify">Proyek Pembangunan Gedung Asrama 5 Lantai LPMP di Sumatera Barat, lembaga ini bertugas untuk melaksanakan penjaminan mutu, pengembangan model dan kemitraan penjaminan mutu pendidikan dasar dan pendidikan menengah di provinsi berdasarkan kebijakan Menteri Pendidikan dan Kebudayaan. Gedung Asrama LPMP ini mempunyai luas lahan 1500 m2 yang terdiri dari 5 Lantai serta satu <em>Roof Top </em>yang mampu menampung 500 orang setiap harinya. Proyek Pembangunan Gedung Asrama 5 Lantai LPMP di Sumatera Barat membutuhkan biaya total sebanyak Rp 10.155.457.429,60, dan durasi total yaitu 145 Hari. Untuk itu, diperlukannya suatu program atau aplikasi perangkat lunak untuk membantu manusia dalam menyusun dan menghitung data-data proyek tersebut secara akurat dan cepat. Program Primavera Project Planner P6 merupakan salah satu aplikasi penjadwalan untuk menghitung serta mengoptimalisasikan proyek tersebut dari segi biaya dan waktu proyek sehingga didapatkan hasil yang seoptimal mungkin. Pengoptimalisasian proyek dihitung menggunakan metode <em>Time Cost Trade Off </em>dengan menambahkan jam kerja lembur dan menambahkan jumlah tenaga kerja pada pekerjaan kritis proyek. Setelah dihitung menggunakan metode tersebut, didapatkan data baru setelah di optimasi, dan dimasukkan kedalam Program Primavera Project Planner P6 untuk didapatkan total biaya dan durasi proyek setelah dioptimasi. Hasil yang didapatkan yaitu dengan menggunakan metode penambahan jam kerja lembur, total biaya dan durasi proyek menjadi Rp 10.354.117.835 dan 133 hari (selisih total biaya proyek dengan sebelum dilakukan pengoptimasian adalah Rp 198.660.406,13 dan selisih waktunya 12 hari). Sementara itu, dengan penambahan jumlah tenaga kerja, total biaya dan durasi proyek menjadi Rp 10.238.473.806,75 dan 124 Hari (selisih total biaya proyek dengan sebelum dilakukan pengoptimasian adalah Rp 83.016.377,15 dan selisih waktunya 21 hari). Dengan metode perhitungan pada 1 pekerjaan kritis saja, didapatkan bahwa durasi proyek berkurang sebanyak 3 hari dan biaya proyek berkurang sebanyak Rp 10.279.030,80. Maka dari itu, metode penambahan jam kerja lembur pada 1 pekerjaan kritis saja dinilai lebih efisien karena mempercepat proyek sebanyak 3 hari dan mengurangi biaya sebanyak Rp 10.279.030,80.</p>
Classification of brain tumours using artificial neural networks
Authors: Subba Rao B. V. , Kondaveti Raja , Prasad R. V. V. S. V. , Rao V. Shanmukha , Sastry K. B. S. , Dasaradharam Bh. 
Published: March 2022

<p>Magnetic Resonance (MR) brain Image is very important for medial analysis and diagnosis. These images are generally measured in radiology department to measure images of anatomy as well as the general physiological process of the human body. In this process magnetic resonance imaging measurement are used with a heavy magnetic field, its gradients along with radio waves to produce the pictures of human organs. MR brain image is also used to identify any blood clots or damaged blood veins in the brain. A counterfeit neural organization is a nonlinear information handling model that have been effectively used preparation models for tackling administered design acknowledgment assignments because of its capacity to sum up this present reality issues. Artificial Neural Networks (ANN) is used to classify the given MR brain image having Benign or Malignant tumour in the brain. Benign tumours are generally not cancerous tumours. These are also not able to grow or spread in the human body. In very rare cases they may grow very slowly. Once it is eliminated, they do not come again. On the other hand, Malignant tumours are cancer tumours. These tumour cells are grown and also easily spread to other parts of the human body. Benign also known as Harmless. These are not destructive. They either can't spread or develop, or they do as such leisurely. On the off chance that a specialist eliminates them, they don't by and large return. Premalignant In these growths, the cells are not yet harmful, however they can possibly become threatening. Malignant also known as threatening. Malignant growths are destructive. The cells can develop and spread to different pieces of the body. In our proposed framework initially, it distinguishes Wavelet Transform to separate the highlights from the picture. Subsequent to separating the highlights it incorporates tumour shape and power attributes just as surface highlights are distinguished. Finally, ANN to group the information highlights set into Benign or Malignant tumour. The main purpose as well as the objective is to identifying the tumours weather it belongs to Benign or Malignant.</p>
Quality of Pineapple Cobs Candy as The Effect of Citric Acid Concentration and Immersion Time
Authors: Putra Achmad Rendika , Hudi Lukman 
Published: March 2022

This study aims to determine the effect of citric acid concentration and immersion time on the characteristics of pineapple cobs candy using factorial randomized block design. The first factor was the concentration of citric acid (P): 0.2% (P1), 0.35% (P2), and 0.5% (P3), the second factor was immersion times (T): 12 hours (T1), 15 hours (T2) and 18 hours (T3). Statistical analysis used was analysis of variance and Tukey’s Honest Significant Difference at 5% level. Results showed that there was significant interaction between the concentration of citric acid and immersion times on the ash content of pineapple cobs candy. Concentration citric acid had significant effect on vitamin C content and lightness. Immersion times had significant effect on moisture content. Citric acid concentration and immersion times has significant affected on hedonic test of color, texture and taste. The best treatment was P2T1 (citric acid concentration 0.35%, immersion times 12 hours) with the characteristics ash content of 0.641%, water content of 10.966%, vitamin C content of 0.469%, lightness of 48.957, and color organoleptic value of 4.200, aroma of 5,433, texture of 5,100 and taste of 5,467.
Sustainable Recovery: Managing Risk, Uncertainty, and Complexity
Authors: Ben-Naceur Kamel 
Published: March 2022

I was invited to chair an executive panel related to best practices in oil megaprojects at the 2021 World Petroleum Congress in Houston in December. There were outstanding presentations by Petrobras, Equinor, and Chevron related to developing very complex projects in challenging surface and subsurface environments with the required quality and in line with initial cost estimates. Technological evolutions in our industry have allowed such an achievement, which makes me proud to belong to this industry.
If I were to characterize the skills that differentiate our oil and gas professionals from most other industries, I would highlight our knowledge of subsurface sciences and the ability to manage large-scale projects that are complex and have significant risks associated with them. The latter refers to a significant deviation from the planned/expected performance of the project due to various factors.
SPE has developed over the years several initiatives related to risk management. They span most of the technical disciplines, in particular the Health, Safety, Environment, and Sustainability, and the Management disciplines. Among the latest resources is the Human Factors Technical Section, which has done an outstanding job to deliver valuable information on how to mitigate risks associated with human factors, especially in the case of complex decisions with short-time response required. With the expanding use of artificial intelligence in our operations, there will be more involvement of the Data Science and Engineering Analytics discipline. Other resources include publications about decision analysis, and in particular, the books Making Good Decisions, Introduction to Petroleum Economics, and the still very useful Decision Analysis in E&P.
A particular type of risk that our industry faces is the challenging economic environment that has resulted in shorter industry cycles associated with an increased impact on activity (hence employment) in the oil and gas sectors. The activity drop in 2014–2016 led to a decrease of more than 25% in upstream investments, followed by another even larger (30%) decline in 2020. In fewer than 5 years, the upstream sector has seen investments nearly halved.
I highlighted in a previous column that the 2022 recovery would depend on the emergence of new variants of COVID-19. The emergence of the Omicron variant and the speed at which it is spreading around the world, with limitations imposed on international travel, has led to new daily cases reaching a (conservative) figure of 3 million daily worldwide. Thankfully, it seems that the severity of the cases is lower than with previous variants such as Delta.
Integrated Hydraulics-Modeling Platform Automates Managed-Pressure Drilling
Authors: Carpenter Chris 
Published: March 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19671, “Integrated Hydraulics Modeling for Managed-Pressure Drilling,” by Sayamik A. Rostami, Jose Brana, and Thomas Koithan, SPE, Weatherford. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission.
Technologies aimed at better performance and lower cost are more important to the oil and gas industry than ever. This emphasis drives hardware manufacturers to innovate quickly and create devices with more capabilities so that what was impossible a few short years ago is reality today. In the complete paper, the authors describe a platform that integrates advanced data analytics and hydraulic modeling in real time, coupled with cybersecurity features applied through an open-platform communications unified-architecture protocol directed at automation controllers for managed-pressure-drilling (MPD) applications.

MPD Control System
An intelligent MPD system automatically controls all the components of an MPD system, including a rotating control device, annular isolation device, flow spool, buffer manifold, choke manifold, and pressure-relief valves. Aside from communicating with and controlling this equipment, such a system is able to communicate with the overarching rig-control system. The system uses the control capabilities of a centralized programmable automation controller with an integrated human/machine-interface screen. Another advantage of the intelligent MPD system is the built-in functionality to track the performance of the system in terms of performance and health through component monitoring to enable condition-based maintenance (CBM).
Finite-Volume Method Enables Simulation of Induced Seismicity
Authors: Carpenter Chris 
Published: March 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203903, “A Collocated Finite-Volume Scheme for High-Performance Simulation of Induced Seismicity in Geoenergy Applications,” by A. Novikov, D.V. Voskov, SPE, and M. Khait, SPE, Delft University of Technology, et al. The paper has not been peer reviewed.
In the complete paper, the authors develop a collocated finite-volume method (FVM) to study induced seismicity as a result of pore-pressure fluctuations. A discrete system is obtained based on a fully implicit coupled description of flow, elastic deformation, and contact mechanics at fault surfaces on a fully unstructured mesh. The cell-centered collocated scheme leads to convenient integration of the different physical equations because the unknowns share the same discrete locations on the mesh. Additionally, a multipoint flux approximation is formulated in a general procedure to treat heterogeneity, anisotropy, and cross-derivative terms for both flow and mechanics equations.

Introduction
The FVM has become an essential tool for flow and transport simulation because of its local conservation property. For mechanical deformation, however, the conservation property does not have the same importance. Nonetheless, the FVM is still an attractive choice because it represents conservation laws in integral form more naturally.
Some authors in the literature use fixed-stress splitting algorithms to decouple mechanics and flow equations. These are a form of sequential implicit solution schemes and often lead to more-efficient simulations than fully implicit (FI) simulation. However, sequential schemes introduce certain restrictions on timestep sizes. On the other hand, FI schemes provide unconditionally convergent solutions and are more-robust and -convenient approaches for investigation of complex multiphysical problems. Although the FI approach does not imply any restriction on timestep size, it requires an efficient linear equation solution strategy for high-resolution models.
In this study, the authors develop a collocated FI multipoint FVM scheme for poromechanics simulation of faulted reservoirs. The scheme can be used to solve poromechanics problems on unstructured polyhedral grids with minimal degrees of freedom per cell. It is also capable of considering material heterogeneity while preserving mass and momentum balances. This scheme is extended to consider discontinuities in displacements at faults.
The developed algorithms have been embedded into the open-source Delft Advanced Research Terra Simulator (DARTS).
Technique Improves Neural Network Training To Quantify Rock Properties From Seismic
Authors: Carpenter Chris 
Published: March 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202685, “Rock-Physics-Guided Data-Science Technique To Improve Neural Network Training To Accurately Quantify Rock Properties From Seismic Data,” by Shraddha Chatterjee, Tanya Colwell, and Jon Downton, CGG, et al. The paper has not been peer reviewed.
The success or failure of neural networks in discerning rock properties through a quantitative approach from seismic data and associated seismic attributes is dependent on the amount of available data. Past researchers developed a hybrid theory-guided-data-science (TGDS) technique that augmented the amount of data used for training. This paper shows the application of this method of TGDS-based synthetic seismic catalog generation followed by the application of a deep feed-forward neural network (DFNN) to a real seismic data set for a commercially oil-producing field in the North Sea.
Introduction
Machine-learning algorithms have been used for several applications in the field of reservoir characterization. Using neural network techniques for supervised learning applications to predict log properties from seismic and seismic attributes is an established technique. However, a known limitation of neural networks is that they depend greatly on the amounts of training data, also called labeled data. Generally, the source of the labeled data during geophysical applications is well-log data. Hence, a need to increase the amount of labeled data was required to increase the accuracy of predictions. The accuracy of neural networks suffers because of the lack of labeled data, especially in exploration study areas where well data are scarce.
The hybrid TGDS model augments the amount of data used to train the neural network. During the process, theory-based generated outputs are used as inputs to the data-science component of this work flow. The theory-based components are generated models of rock physics that help in estimating the elastic parameters of a rock caused by changes in fluid and other petrophysical rock properties; this approach then is used to generate a large set of pseudowells. In the next step, synthetic seismic gathers are generated based on the large set of generated pseudowells that are used for training a DFNN. The parameters that are derived for training the neural network are further applied on a real data set. In the complete paper, the authors show an example of this work flow in which it was applied to a North Sea field that produces commercial volumes of oil. The reservoir of interest is of the Paeocene age and is a remobilized injectite sand that cross-cuts the stratigraphy at high angles. The injectite sands can be excellent pay zones with good porosity and permeability, even though their imaging can be challenging.
Simulation of Scale Inhibitor Squeeze Treatments in a Polymer Flooded Reservoir
Authors: Beteta Alan , Vazquez Oscar , Al Kalbani Mandhr , Eze Faith 
Published: March 2022

Summary
This study aims to demonstrate the changes to scale inhibitor squeeze lifetimes in a polymer flooded reservoir vs. a waterflooded reservoir. A squeeze campaign was designed for the base waterflood system, then injection was switched to polymer flooding (PF) at early and late field life. The squeeze design strategy was adapted to maintain full scale protection under the new system.
During the field life, the production of water is a constant challenge. Both in terms of water handling, but also the associated risk of mineral scale deposition. Squeeze treatment is a common technique, where a scale inhibitor is injected to prevent the formation of scale. The squeeze lifetime is dictated by the adsorption/desorption properties of the inhibitor chemical, along with the water rate at the production well. The impact on the adsorption properties and changes to water rate on squeeze lifetime during PF are studied using reservoir simulation.
A 2D 5-spot model was used in this study, which is considered a reasonable representation of a field reservoir under waterflooding (WF)/PF. It was observed that when applying polymer (HPAM) flooding, with either a constant viscosity or with polymer degradation. The study concludes that the number of squeeze treatments was significantly reduced as compared to the waterflood case. This is due to the significant delay in water production induced by the polymer flood. When the polymer flood was initiated later in field life, after 0.5 PV (reservoir PVs) water injection, resulting in 70% water cut approximately, the number of squeeze treatments required was still lower than the waterflood base case. However, it was also observed that in all cases, at later stages of field life the positive impact of PF on squeeze lifetime begin to diminish, due in part to the polymer breakthrough, which results in higher water viscosity in the production near-wellbore region. Preventing the overflush to be as effective transporting the scale inhibitor.
This study represents the first coupled reservoir simulation/squeeze treatment design for a polymer flooded reservoir. It has been demonstrated that in over the course of a field lifetime, PF will in fact reduce the number of squeeze treatments required even with a potential reduction in inhibitor adsorption. This highlights an opportunity for further optimization and a key benefit of PF in terms of scale management, aside from the EOR.
Severe Casing Failure in Multistage Hydraulic Fracturing Using Dual-Scale Modeling Approach
Authors: Yu Hao , Dahi Taleghani Arash , Lian Zhanghua , Mou Yisheng 
Published: March 2022

Summary
Field evidence of production logs after fracturing have documented the existence of abundant natural fractures in Weiyuan shale plays, which is widely acknowledged to have a positive impact on fracture network complexity. On the contrary, cases of severe casing failures have been frequently reported in this field during multistage fracturing practice. Stress interference between two adjacent stages may increase nonuniform loading on the casing string and accommodate failure. To better understand this problem, we establish a coupled 3D reservoir-scale model with complex well trajectory and tie it to a single well-scale model consisting of casing and the surrounding cement sheath. Using this model, we investigate the potential impacts of cement deficiency, clustered perforations, fracture geometry, and spacing strategy on casing integrity. Our simulation results indicate that cement deficiency could intensify the load nonuniformity around the borehole, which poses potential threats to casing failure. When cement deficiency reaches 45° along the minimum horizontal stress, it has the largest influence on the stress level of casing. In addition, perforations could lower the casing strength, but the reduction may not change further when the perforation diameter reaches a certain value. Also, impacts of fracture geometry and spacing on casing deformation are explored. We can conclude that a lower ratio of fracture length to width and reasonable spacing strategy could help reduce the load nonuniformity on casing and thus avoid the casing deformation. The described workflow may be adopted in other areas to predict the possible casing failure problems induced by multistage hydraulic fracturing with cheap computational costs and to anticipate the challenges and address them by revising pumping schedule or spacing strategy.
An Enhanced Ad hoc On-Demand Multipath Distance Vector Routing using Hybrid Bat Algorithm
Authors: Prabha R. , A Senthil G 
Published: March 2022

Abstract
Mobile Ad-hoc Networks (MANETs) are multi-hop ad hoc networks of mobile battery-charged devices designed for emergency use. There is source preparation with regards to a potential disconnection whilst occurrence of a path breakage. The source will commence with discovery of the path and evade complete disconnection. When there is a decrease in the availability of links, there may be path breakage. Breakages of links because of energy drain and node mobility are managed by reactive MANET routing protocols. Also, multi-hop communication can be more efficient in terms of the routing protocols for large area networks. The Ad hoc On-Demand Multipath Distance Vector protocol (AOMDV) is modified by this study in a way that link availability estimates lead to route handoff. An optimal AOMDV route can be detected by the novel Bat Algorithm hybridization with Harmony Search (BAT-HS) algorithm depending on rate of bit error, velocity, neighbouring node’s queuing delay, and link quality. In this work, the proposed algorithm Hybrid Bat (HBAT) to enhanced the AOMDV with Hybrid Bat is optimizes the routing outperforming the Packet Delivery Ratio (PDR), End to End delay reduction and in order to reduce routing control overheads and route discovery that are proved by simulation results.