bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life Cycle Assessment as a tool of modern ecological education
Authors: Dybikowska Adrianna , Kazmierczak-Piwko Leszek 
Published: September 2019

The article presents the issues of the Life Cycle Assessment (LCA) use in contemporary ecological education, in relation to the problem of sustainable development. Based on the analysis of the literature, legal norms and strategic documents, the theoretical aspects of LCA as a tool used in numerous areas and environmental management processes were discussed and the significance of its use in the process of shaping ecological awareness of both the whole society and enterprises was demonstrated. The role of using computer programs based on LCA technology (such as SimaPro) in the teaching process at the higher education level was also emphasised.

Keywords: Environmental awareness, ecological education, sustainable development, LCA, life cycle assessment.
Perbanyakan Jabon Merah (Anthocephallus macrophyllus (Roxb.) Havil) Secara Vegetatif dengan Stek Pucuk Muda dan Stek Pucuk Tua (tunas wiwilan) dengan Zat Pengatur Tumbuh
Authors: Agustin Ely Kristiati 
Published: September 2019

Jabon merah(Anthocephallus macrophylus) merupakan salah satu anggota keluarga kopi-kopian (Rubiaceae). Tumbuhan ini tergolong jenis tanaman hutan yang cepat tumbuh (fast growing species). Pada umumnya pengembangan kayu jabon diperuntukan sebagai bahan baku pulp dan kertas, kayu lapis, mebeler, bahan bangunan dan konstruksi ringan. Beberapa tahun belakangan ini laju perkembangan industri perkayuan terhambat terkait dengan berbagai masalah yang dihadapi saat ini, salah satu permasalahan utamanya adalah pasokan kayu. Mengingat lajunya kebutuhan kayu ini kiranya diperlukan salah satu materi genetik perbanyakan vegetatif. Perbanyakan vegetatif mempunyai beberapa keunggulan, diantaranya adalah tidak bergantung pada musim buah, efektif dalam perbanyakan secara masal dan mempunyai keturunan yang sifat genetiknya sama dengan induknya. Rancangan percobaan yang digunakan adalah Rancangan Acak Kelompok (RAK) dengan 2 faktor. Faktor pertama yaitu pengaruh pemberian Rootone-f. Faktor kedua adalah asal bahan stek. Teknik perbanyakan vegetatif merupakan salah satu alternatif untuk memperbanyak jenis tanaman ini. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian zat pengatur tumbuh dan asal sumber stek juvenil muda dan tunas wiwilan jabon merah. Hasil penelitian menunjukkan bahwa Rootone-f memberikan pengaruh signifikan terhadap persen berakar tunas muda (98,67%) dan stek pucuk tua (74,67%). Demikian pula tumbuh akar paling cepat yaitu pada hari ke-4 setelah tanam. Persentase stek hidup adalah 91,58 %.
Efektivitas Metode Discovery dan Media Youtube Terhadap Siswa Kelas V SD N Tlahap Dalam Materi Fotosintesis
Authors: Bargi  
Published: September 2019

Pembelajaran di Sekolah Dasar akan lebih efektif, menarik, dan menyenangkan apabila guru dalam menjelaskan materi pelajaran memanfaatkan berbagai media dan metode secara bervariasi. Penggunaan metode yang tidak bervariasi, menjadikan hasil belajar sangat rendah seperti yang terjadi pada siswa kelas V SDN Tlahap tahun pelajaran 2018/2019. Untuk memecahkan masalah tersebut, peneliti melakukan tindakan dengan mengubah metode dan media pembelajaran, yang semula menggunakan mengunakan metode konvensional diganti metode discovery dan media youtube. Tujuan dari penelitian ini adalah untuk meningkatkan minat dan motivasi belajar pada siswa kelas V SDN Tlahap tahun pelajaran 2018/2019 pada materi fotosintesis yang nilainya masih rendah. Dalam menggunakan metode discovery dan media youtube siswa diajak untuk mengamati dari video yang diambil dari youtube kemudian diarahkan untuk berdiskusi dalam kelompok. Hasil diskusi disampaikan di depan kelas dan ditanggapi oleh kelompok lain, yang kemudian akan disimpulkan bersama guru. Sumber data diambil dari pretest sebelum dilakukan tindakan untuk mengetahui kemampuan awal, dan postest pada akhir siklus kedua. Dalam tiap siklus, penggunaan metode discovery dan media youtube akan meningkatkan hasil belajar karena lebih menarik, menyenangkan, dan pengetahuannya pun diperoleh dari pengalaman belajar sendiri bersama teman-temannya. Hasil belajar terbukti lebih baik ada peningkatan sebesar 60,87%
Applying Decision Trees to Improve Decision Quality in Unconventional Resource Development
Authors: Miller Patrick , Gouveia James 
Published: September 2019

Abstract
Decision trees have been used for many years in conventional oil and gas plays to help managers better understand risk and expected value for a project. Although industry has rapidly shifted over the past decade to developing unconventional resources, application of decision trees to these plays has lagged behind. When building decision trees for unconventional plays, it is often unclear to evaluators how to build the tree, namely, how to estimate the probability of meeting a given commercial threshold, as well as the production profiles and costs to use for each branch of the tree. This paper presents a workflow that can be used to build a decision tree for an unconventional play in the appraisal phase of development, given ranges of uncertainty in production profiles and drilling and completion costs. In applying this workflow, managers will better understand both the drivers of uncertainty in expected value and how they can influence it via appraisal program design and setting commercial thresholds. An example from a North American unconventional play is used as an example to illustrate the steps of the workflow.
Field Trial Results of Ball-Activated Outflow Control Devices for SAGD
Authors: Webb Patrick David , Gauthier Joshua , Skeates Craig 
Published: September 2019

Abstract
A field-trialed ball-activated Outflow Control Device (OCD) is presented that eliminates coiled tubing intervention in SAGD injection wells when converting from steam circulation to SAGD. This paper builds on a previously presented paper by the same authors where the design and tool qualification of the ball-activated OCD was presented (Webb et al, 2017). In this paper, the results of the field trial are shared and compared with analogous wells where coiled tubing shiftable OCDs were used.
The field trial of the ball-activated OCD is compared to coiled tubing shiftable OCDs on three main criteria: job efficiency, confirmation of shift, and environmental, health, and safety (EHS) considerations. Acoustic-based monitoring equipment and pressure signatures were used to confirm successful ball-activation of the ball-activated OCD. A tension response from the coiled tubing was used to confirm shifting of the coiled tubing shiftable OCDs. Steam modelling and observed tubing pressure drop data is also shared for all trials as another indication of successful shift.
In five of six ball-activated OCD shifts, the tools functioned as intended with clear pressure and acoustic signatures confirming a successful shift of the sliding sleeve. One of six was intentionally left closed. In addition, the jobs were completed more efficiently and with less EHS risk than the coiled tubing shiftable OCDs. The coiled tubing shifting tension data indicated that two out of six coiled tubing shiftable OCDs were successfully shifted, with inconclusive shifts occurring on three OCDs, and one left intentionally closed. The observed pressure drop data presented, indicates that shifting was successful in all ball-activated trial wells, and two of three coiled tubing shiftable trial wells.
The ball-activated OCD is a novel tool for use in SAGD injector wells to improve efficiency by reducing operational time and personnel required in shifting OCDs. In addition, the shift confirmation pressure signature and optional acoustic monitoring provides greater confidence of sleeve shift.
Ears On Wells: Reservoir Delineation and Monitoring Using True 3D Imaging Approaches
Authors: Ghazali Ahmad Riza , Abdul Rahim M. Faizal , Mad Zahir M. Hafizal , Muhammad M. Daniel , Mohammad M. Afzan , A. Aziz Khairul Mustaqim 
Published: September 2019

Abstract
The key objectives were to achieve better seismic resolution and spatial delineation in very heterogeneous reservoirs. We decided to supplement simultaneously the surface 3D multi component seismic acquisition by placing additional fiber optic live receivers in the subsurface via a "True-3D" experiment without shutting down the oil production. The most cost-effective method to snapshot this wavefield propagation downhole is by utilizing fiber optic Distributed Acoustic Sensing (DAS). The borehole 3D VSP data were acquired by sharing the surface OBN nodal survey airgun sources. This is an important experiment for the field in the future so that the need to halt insitu field production for 4D time lapse monitoring will not be required if the S/N is acceptable by using this method. This permanent installation of fiber optic cables has become our ears on wells, not only for 3D DAS VSP but for proactive monitoring of the field, ensuring optimum production performance throughout the life of the field.
The Effect of Changes in Hydraulic Fracking Fluid Chemistries on the Dissolution Rate of Dissolvable Magnesium Frack Plug Components
Authors: Murphy Matthew , Turski Mark , Warfield William , Lyon Paul 
Published: September 2019

Abstract
Objectives/Scope: The use of dissolvable metal technologies for use in downhole tools for the hydraulic fracking industry has expanded rapidly over the last few years. The dissolution rate of these alloys is known to be sensitive to the chemistry of the fracking fluids used. Recently, there has been a drive to increase the use of recycled fracking fluid, resulting in unintended changes to the fluid chemistry. This work will review the effect of such changes in frac fluid chemistries on the dissolution rate of commercially available magnesium based dissolvable alloys. These results are compared to the published dissolution rate for the same alloys.
Methods, Procedures, Process: The published rate of dissolution is based on tightly controlled temperatures, utilising KCl solutions typically up to 3 % KCl. In this work, the dissolution rate was measured for a range of simulated frac fluid chemistries. Tests were carried out in tightly controlled laboratory conditions at ambient pressures and at temperatures up to 200 F. A range of brine solutions were investigated, applying systematic changes to the compositions of each solution. This approach provided reproducible conditions, allowing the effect of specific species typically found in downhole frac fluids to be isolated and evaluated.
Results, Observations, Conclusions: The results show clear trends on the dissolution rate of dissolvable magnesium alloys with changes in frac fluid chemistry. In particular, the presence of certain salts have shown to significantly reduce the material dissolution rate.
Novel/Additive Information: While the general trend for increased dissolution rate with increasing brine concentration is well understood it was surprising to find a decrease in corrosion rate where certain salts are present. These results are invaluable in providing the operator a greater understanding on the expected dissolution rate of dissolvable magnesium technologies within the field for a given chemistry of frac fluid. These results are of particular importance given the trend towards reuse of frac fluids in the field.
Image Processing and Machine Learning Applied to Lithology Identification, Classification and Quantification of Thin Section Cutting Samples
Authors: Caja Miguel Ángel , Peña Andrea Carolina , Campos José Rafael , García Diego Laura , Tritlla Jordi , Bover-Arnal Telm , Martín-Martín Juan Diego 
Published: September 2019

Abstract
Cuttings provide the opportunity to precisely look at the rock that has been drilled. A preliminary drill cuttings description is commontly performed by mudloggers and wellsite geologists using conventional binocular microscope at the drilling rig. After this preliminary description, often the bags of cuttings are stored in a warehouse and samples are seldom examined back again. Cuttings give the geologist information about the formation lithology needed for geologic correlation, understanding about reservoir quality, seals and source rocks, and can also be an input for the petrophysicist.
In this study, we are testing a methodology to identify, classify and quantify lithologies present in cutting samples using thin section images. The method includes sample preparation (washing, drying and thin section cuttings preparation), image acquisition (to obtain whole thin section gigapixel high resolution microscopy images), virtual microscopy (to identify lithologies) and automatic image analysis (to perform supervised machine learning lithology clasiffication).
Virtual microscopy allowed the identification of four main lithologies in all the studied thin sections: quartzites (including loose quartz grains), siltstones, claystones and carbonates. Image analysis allowed the classification and quantification of the identified lithologies in 16 drill cutting samples from two tight gas reservoirs.
This innovative methodology allowed the fast identification of lithologies using virtual microscopy and their classification and quantification by image analysis and supervised machine learning. This approach is widely accessible as open source software was used for virtual microscopy and image analysis. Algorithm training and model generation was relativelly fast, and its performance or accuracy was qualititavely evaluated by virtual microscopy with good classification results.
A Hybrid Data and Physics Modeling Approach Towards Unconventional Well Performance Analysis
Authors: Molinari Diego , Sankaran Sathish , Symmons Dave , Perrotte Michael 
Published: September 2019

Abstract
As unconventional reservoirs have become more challenging to develop and predict, understanding well performance has proven to be essential for driving value. Though there have been continuous advancements in well performance analysis and production forecasting for unconventional reservoirs that exhibit prolonged transient flow conditions, there is a still a gaping need for robust and scalable methods which are usable from a practical standpoint, considering data availability and other uncertainties.
Traditional forecasting methods (decline curve analysis and its variants) are often not fully representative and impacted by surface operations such as constrained flow, choke changes etc. or subsurface events such as well interference, frac hits, depletion below saturation pressure, etc. Analytical and numerical modeling methods address this issue by applying first principles and simplified physics for integrating flow diagnostics and time-rate-pressure analysis. However, this is often quite interpretive through manual analyses that are not scalable, requiring additional reservoir inputs that are often not collected or known for all wells. Consequently, these methods are not suitable for large scale, repetitive forecasting purposes.
We desire a reliable, consistent and scalable well performance analysis method that can work with routinely measured data for most unconventional wells (i.e. surface pressures, flow rates and fluid data).
A novel continuous drainage volume estimation method based on the concept of diffusive time of flight is used in conjunction with material balance calculation to estimate pressure depletion and well deliverability from daily flow rates and bottomhole pressure. A data-driven procedure for estimating PVT properties for all wells is also developed and applied to convert surface pressures to bottomhole pressures. A predicted rate profile can be obtained from the productivity and depletion trends. Finally, based on the "learned" well response, a new stochastic method is used for a reliable uncertainty quantification in production forecasting.
The proposed workflow has been applied to more than a thousand wells in a major unconventional field successfully through an automated platform, after applying necessary data processing and cleansing procedures. We present value-driven case studies demonstrating tangible ways in which this method can be successfully implemented, providing additional insights compared to traditional forecasting techniques.
Our proposed method overcomes common practical challenges of traditional production analysis and forecasting methods (decline curve, analytical and numerical models) to provide a consistent and reliable measure to compare well performance at large scale.
Analyzing Fractures Using Time-Lapse Electric Potential Data
Authors: Hu Jason , Horne Roland 
Published: September 2019

Abstract
Characterizing the fractures is an important task to improve the understanding and utilization of hydraulic fracturing. As an approach to augment and improve on the existing methods, time-lapse electric potential measurements could be used to characterize subsurface features. In this study we investigated the characterization of fracture length and fracture density by using time-lapse electric potential data. A new borehole ERT (electric resistivity tomography) method designed specifically for hydraulic fracture characterization is proposed to better capture reservoir dynamics during hydraulic fracturing. This method uses high resolution electric potential data by implementing electrodes in or near boreholes and monitor electric potential distribution near the horizontal fracture zone. The time-lapse electric potential data generated by this tool were simulated and subsequently used to analyze fracture characteristics. Inverse analysis was then performed on the electric potential data to estimate fracture length and fracture density. Last, we performed sensitivity analysis to examine the robustness of the estimates in nonideal environments. The results of this work show that time-lapse electric potential data are capable of capturing flow dynamics during the fracturing process. Using the proposed borehole ERT method we successfully estimated the true fracture length and true fracture density of a constructed fracture model. We were able to determine the best locations in the constructed reservoir to place the electrodes, and through sensitivity analysis we found the maximum noise level of the electric potential data that can still allow the proposed method to make robust fracture length and fracture density estimates.
Our proposed method offers a new approach to make robust estimates of fracture length and fracture density. Electric potential data have been used mostly for well logging in the past. This study demonstrates a novel way of using electric potential data in unconventional development and opens possibilities for more applications such as production monitoring.
Artificial Intelligence Applied in Sucker Rod Pumping Wells: Intelligent Dynamometer Card Generation, Diagnosis, and Failure Detection Using Deep Neural Networks
Authors: Peng Yi 
Published: September 2019

Abstract
For most of the mature fields, the oil well operation and maintenance expenditures continue to put financial pressure on the operators in the low oil price period. Digital oilfields and artificial intelligent technology are the major areas invested to fight for declining oil production and increasing cost. This paper provides a novel artificial intelligent method to monitoring and diagnose the sucker-rod pumping wells using deep learning algorithms.
Traditional method using load and displacement sensors to measure the dynamometer card needs large investment on the equipment installation and maintenance. We build a general model that generates the dynamometer card from electrical parameter using state-of-art deep learning algorithms. The deep learning algorithms can analyze the relationships between the electrical data and corresponding dynamometer card in different conditions, which is very hard for human being to detect. In addition, we build another automated diagnosis deep learning model from thousands of dynamometer cards labeled with different classifications.
We have already tested these newly developed artificial intelligent models on hundreds of sucker rod pumped wells in different oilfields in PetroChina. The field test results show that the dynamometer cards generated from electrical data have above 90% similarity compared to the real dynamometer, which meet the requirement for well diagnosis. The card generation model is stable and prevents the disturbance of hostile environment change and sensor failures. The automated diagnosis model also proved to be a good substitute to the conventional software, with above 95% prediction accuracy. The automated diagnosis model reduces the liability and uncertainty of traditional diagnosis software and can integrated with the former dynamometer card generation model to fulfill well monitoring and diagnosis automatically without any physical model based calculations.
These models developed with artificial intelligent technology will be important components in the "Intelligent Fields". They can also be embedded in the IIoT edge computing machines for automatic diagnosis and control. For ultra-low production wells and the newly producing wells utilized this method, operator can save expenditure and human resources tremendously.
Enhancing Scale Prediction in Pre-Salt Wells Using Numerical Simulation
Authors: Simões Maciel Rodrigo , Ressel Pereira Fábio de , Fieni Fejoli Rômulo , Leibsohn Martins André , Duarte Ferreira Marcus Vinicius 
Published: September 2019

Abstract
Petrobras has faced several challenges concerning inorganic scaling in the Pre-salt cluster. Scale prediction plays an important role on well completion selection and supporting to define better alternatives for chemical injection location. However, predicting scale in wellbores is traditionally performed based on thermodynamical equilibrium of the formation water under static conditions. This strategy leads to conservative results since it neglects hydrodynamics and kinetics of the scaling process. This paper proposes a new approach to predict scaling in downhole conditions. The study seeks to contribute on the comprehension of the effect of fluid flow and equipment geometry variation in the crystal deposition process in intelligent well completion equipment.
Such completion devices act in managing the fluid flow influx from different reservoirs or multiple zones of the same reservoir. Despite the positive aspects of this technology, some authors have been pointing out some problems associated with specific applications of these tools. The most common issues are related to the considerable pressure differential and the occurrence of calcium carbonate (CaCO3) scale. The pressure drop in this tool induces the flash liberation of CO2 from the aqueous solution. Consequently, the chemical equilibrium is displaced towards the direction of precipitation of CaCO3 in the flow stream. This paper proposes a new approach to predict scaling in downhole conditions and aims to quantitatively evaluate the calcium carbonate precipitation on the smart completion element internal surfaces. Computational Fluid Dynamics (CFD) along with discrete phase modeling (DPM) is employed to simulate the transport and adhesion of the calcium carbonate crystals on the device. The valves geometries consider the main features observed on the field according to different suppliers, accounting the different possibilities of completion geometries for Brazilian Pre-Salt environment.
The results showed the tendency of scale deposition pointing out hot spots in several different completion accessories at downhole conditions. A better understanding of the scale potential has influenced the decision-making process on the completion design and workover alternatives in the Pre-salt wellbores.