bluuebunny/crossref_metadata_2025_split huggingface dataset:
SABERES COMO FONTE DE PRODUÇÃO DE CONHECIMENTOS E INTERLOCUÇÃO PARA O ENSINO
Authors: MAFRA JOSE 
Published: May 2023

Este trabalho fornece uma discussão relacionada ao desenvolvimento de práticas educacionais articuladas com saberes tradicionais e conhecimentos produzidos, a partir da produção de cuias decoradas e incisas, por mulheres artesãs, região de Aritapera, estado do Pará. Possui pressupostos associados a uma pesquisas de campo etnográfica, em estreito vinculo de uma abordagem participante, pois contou com um planejamento conjunto de atividades locais, com professores da região, para implementação posterior, em sala de aula. Com essa investigação, espera-se contribuir para o campo de pesquisa em etnomatemática, ao trazer reflexões empíricas visando a efetivação de fontes de produção de conhecimentos, através de prática curriculares locais e de boas práticas educacionais, que sirvam de interlocução para o ensino.
Melatih Otot Bagi Lansia untuk Meningkatkan Pengetahuan dan Keterampilan Latihan Beban secara Mandiri di Rumah
Authors: Ellyas Intan Suraya , Sunardi Sunardi , Mariyanto Muhammad , Lelono Sarjoko , Margono Agus , Kristiyanto Agus 
Published: May 2023

<p>Pertambahan usia akan memberikan banyak perubahan pada tubuh manusia. Salah satunya dalah penurunan massa otot yang dialami lansia. Penurunan massa otot pada lansia dapat menimbulkan gangguan otot rangka yang progresif dan terjadi secara menyeluruh. Pada kelompok lansia terjadi percepatan hilangnya massa otot dan fungsi gerak sehingga menyebabkan peningkatan risiko jatuh, penurunan fungsional, kelemahan, dan kematian. Sesuai rekomendasi WHO bahwa usia lansia yaitu diatas 65 tahun selain melakukan latihan fisik bersifat aerobik juga melakukan latihan penguatan otot berupa latihan beban yang dilakukan 2 hari dalam satu minggu. Namun, belum banyak kelompok lansia yang mengetahui dan mampu melakukan latihan beban. Kegiatan ini dilakukan dengan memberikan penyuluhan dan pelatihan bagi kelompok senam Prolanis Sehat Bugar Ceria di Kabupaten Karanganyar. Sehingga tujuan dari pengabdian ini adalah memberikan pengetahuan dan pengenalan berbagai gerakan latihan beban yang dapat dilakukan secara mudah dan dapat dilakukan secara mandiri di rumah. Dalam pengabdian masyarakat ini, didapatkan hasil peningkatan pengetahuan para lansia tentang manfaat dan cara latihan beban serta dapat mempraktekkan gerakan latihan beban yang baik dan benar.</p><p> </p><p><strong>Kata kunci</strong> : latihan beban; lansia; sarkopenia<em> </em></p>
Smart Drilling Fluids Formulations for Sensitive Shale Formations Using Surfactants and Nanoparticles
Authors: Kamal Muhammad Shahzad , Ahmad Hafiz Mudaser , Murtaza Mobeen , Rana Azeem , Hussain Syed Muhammad Shakil , Patil Shirish , Mahmoud Mohamed , Shehri Dhafer Al 
Published: May 2023

Abstract
The demand for oil and gas is continuing to rise with a growing population and worldwide industrialization. Surfactants are considered excellent additives for drilling formulations because of their unique properties and chemical structure. The surface-modified nanoparticles in the drilling fluids (DFs) help to improve the rheological and filtration properties of water-sensitive shale formations. The water-sensitive shale formations in the wellbore often result in swelling after interacting with water-based DFs. The swelling of shale formation impacts the rheological and filtration properties of DFs.
The aim of this study is to formulate DFs with cationic surfactants and surface-modified nanoparticles to minimize shale swelling and improve the rheological and filtration properties. Various drilling fluid formulations were prepared with bentonite as a basic constituent of DFs while Gemini surfactant and graphene nanoparticles were added with concentrations of 0.5%. The rheological and filtration properties were determined at room temperature. The shale inhibition tests were performed to analyze the swelling inhibition properties of DFs.
The surface-modified nanoparticles along with the cationic surfactant make a stable dispersion of DFs. The presence of nanoparticles in the DFs enhances the rheological and filtration properties. The filtrate loss has been significantly reduced by incorporating graphene nanoparticles and Gemini surfactant-modified graphene nanoparticles. The rheological properties such as plastic viscosity, yield stress, and gel strengths have been improved by the combined addition of surfactant-modified nanoparticles. The reduction of filtrate loss was due to the clogging effect of small passages in the filter cake while long alkyl chains of surfactant molecules spread over the filter cake making a hydrophobic film that minimizes the contact of water with the filter cake. Moreover, the swelling inhibition test such as the linear swelling test showed that the presence of nanoparticles and cationic surfactants significantly enhanced the shale swelling inhibition and reduced the percentage of swelling compared to the DI water. The cationic surfactant interacts with the negatively charged clay particles through electrostatic forces and surfactant along alkyl chains wraps around the clay particles which leads to the minimum swelling of shale formations.
This study reveals that the formulations based on surface-modified nanoparticles and surfactants in water-based DFs can inhibit shale swelling and improves the borehole stability for water-sensitive shale formations.
Forecasting Oil Recovery Using Long Short Term Memory Neural Machine Learning Technique
Authors: Asante Jonathan , Ampomah William , Carther Martha 
Published: May 2023

Abstract
This paper focuses on using a timeseries neural network to forecast the oil recovery of a mature oil reservoir undergoing tertiary CO2 water alternating gas (WAG) enhanced oil recovery (EOR). Estimating future oil recovery is a necessity for planning an effective EOR strategy. Because of the high uncertainty associated with numerical modeling input parameters, modeling is not necessarily an accurate predictor of future performance for a specific well or even an entire field. The evolution of machine learning algorithms has shown that data-driven models can make decisions based on trends and pattern recognition to achieve tractable, robust, and cost-effective solutions.
The methodology is validated by analyzing a five-spot pattern from the study field. The one injector well and four producers within the pattern are considered to be mutually connected. The multivariate timeseries (MTS) field data utilized in the model construction include production bottom-hole pressure, injection pressure, WAG cycles, and injection volumes. These MTS input data were preprocessed into a format that is more understandable and useful for the model. A Long-Short-Term Memory (LSTM) neural network model was established to determine patterns and trends, discover relationships from MTS data, and subsequently predict oil recovery through model-fitting. During the model construction, the preprocessed dataset was split into training and testing sets based on production time periods. The largest portion of the data set is apportioned to train the model, and it also corresponds to the earliest part of the production. The model is tested on the remaining data set chronologically.
Analysis of field history calibration through loss iteration of the training dataset shows a low mean squared error of 7.16 and a relatively high R-squared value of 0.92. The developed model was validated using a test set, and results showed high-level model predictability of an R-squared value of 0.88. Additional model validation was performed using other wells’ information within the pattern as a blind test dataset. An average R-squared of 0.88 was observed for the other producing wells. The validated model was used to forecast oil recovery into the future with a reasonable outcome. From the forecast, uncertainty increased with the increasing length of time in the future, and the alteration of the WAG cycle significantly impacted the oil recovery. The LSTM model can predict oil recovery with a high level of accuracy. The successful predictions and reasonable forecasting of the oil recovery prove the effectiveness and usefulness of data-driven models.
The workflow presented in this paper predicts the oil recovery without a detailed geological model and/or numerical simulation; it only considers time-changing parameters. Analyzing the LSTM model's results provides robust guidance to adjust real-time field development plans.
Hybrid Multiphase Rate Forecasting Model in Liquid Wells for Unconventional Reservoirs
Authors: Sinha Utkarsh , Zalavadia Hardikkumar , Chauhan Prithvi Singh , Sankaran Sathish 
Published: May 2023

Abstract
The development of shale plays requires accurate forecasting of production rates and expected ultimate recoveries, which is challenging due to the complexities associated with production from hydraulically fractured horizontal wells in unconventional reservoirs. Traditional empirical models like Arps decline are inadequate in capturing these complexities, and long-term forecasting is hindered by the challenges posed by 3 phase flow. In response, a new physics-augmented, data-driven forecasting method has been proposed that efficiently captures these complexities.
The proposed PI-based forecasting (PIBF) method combines data-driven techniques with the physics of propagation of dynamic drainage volume under transient flow conditions observed by unconventional wells for a prolonged period. The model requires only routinely measured inputs such as production rates and wellhead pressure, and efficiently captures the trend shift in gas-to-oil ratio caused by free gas liberation in the near-wellbore region. By using material balance and productivity index models, the proposed approach can forecast well performance and handle changing operational conditions during the well's lifecycle.
Compared to existing empirical or analytical methods like Arps decline and RTA, the proposed method yields more accurate forecasting results, while still using easily available inputs. Empirical methods like Arps decline have low input requirements but lack physical insights, leading to inaccuracies and inability to handle changing operational conditions. Pure physics-based methods like RTA and reservoir simulation require more input properties that are often difficult to obtain, resulting in a low range of applicability.
Overall, the proposed method offers a promising alternative to existing methods, effectively combining data-driven techniques with physics-based insights to accurately forecast well performance and handle changing operational conditions in unconventional reservoirs.
A Novel Approach to Determining Carrying Capacity Index Through Incorporation of Hole Size and Pipe Rotation
Authors: Rathgeber David , Johnson Erick , Lucon Peter , Anderson Ryan , Todd Burt , Downey Jerome , Richards Lee 
Published: May 2023

Abstract
Current API RP13D guidelines outline 3 methods for determining hole-cleaning efficiency based on wellbore angle. Method 1, used in low-angle wellbores (&lt;30°) compares cuttings slip velocity with annular velocity to determine a transport ratio and cuttings concentration. Method 2, also used for low-angle wellbores (&lt;30°) derives a carrying capacity index (CCI) based on bulk annular velocity, fluid density and power-law rheology. Method 3, used in high-angle wellbores (&lt;30°) derives a transport index (TI) based on fluid rheology, density, and flow rate. TI is then plotted on an empirically derived chart (Luo et al., 1992, 1994) to determine maximum allowable rate of penetration (ROP) that should ensure efficient hole cleaning.
Although these methods are considered recommended practices by API, Method 3 (TI) is based on an outdated study (Luo et al., 1992) with limited scope (one flow loop, one field test). Additionally, this method neglects the importance of drill pipe rotation and pipe eccentricity in cuttings transport efficiency, which has been proven to be a factor in other studies (Akhshik et al., 2015; Sanchez et al., 1997b).
This paper highlights the shortcomings of current API standards and identifies what effects contributing factors such as pipe eccentricity and drill pipe rotation rates may have on cuttings transport efficiency. Further, this paper discusses the impact pipe-to-hole area ratio and wellbore flow area have on the effects of drill pipe rotation and flow channeling.
Five horizontal wellbores were modeled using Siemens Star CCM+ Computational Fluid Dynamics (CFD) software, with bottom-eccentric 4 ½″ drill pipe placement, in annular diameters of 6¾″, 7 ⅞″, 8 ⅜″ 8 ½″ and 8 ⅝″. Additionally, one bottom-eccentric 5″ drill pipe in an 8 ¾" wellbore was modeled to compare identical pipe-to-hole area ratios with different flow areas. Simulations were run with drill pipe rotation speeds increasing from 0 to 180 RPM, in 30 RPM increments. In order to determine the impact fluid rheology has on flow channel development, both medium density oil-based muds and light density water-based muds were modeled and compared. Bulk annular flow velocity was set to 100 ft/min, to maximize the observable effects of drill pipe rotation. Bulk average velocity was calculated from cross sectional area, determining both annular velocity (velocity parallel to wellbore) and absolute velocity (fluid velocity magnitude regardless of direction). The resultant velocity profiles were used as the annular velocity component in API CCI and TI calculations and compared to bulk annular velocity.
In addition to observing fluid velocity for CCI and TI calculations, changes in effective viscosity from the onset of pipe rotation was also analyzed to determine changes in wellbore parameters that may affect cuttings transport.
Fracture Diagnostic Revolution: New Advancements in Integrated Studies to Accelerate Your Learning Curve Ahead of Capital Spending
Authors: Williams-Kovacs Jesse D. , Deryushkin Dmitry 
Published: May 2023

Abstract
With increasing complexity and capital cost of developing unconventional reservoirs, it has become common practice for operators to undertake hydraulic fracture optimization prior to initiating field pumping operations. However, one of the biggest challenges that has plagued the industry is a lack of effective and cost-efficient methods to diagnose the efficacy of these fracturing treatments prior to executing their next pad, leading to perpetual cycle of knowledge lagging a full drilling program behind capital spending.
In this paper a new integrated workflow for hydraulic fracture optimization will be demonstrated, with a focus on the use of quantitative flowback analysis (FBA) to quickly and effectively diagnose completion efficacy within a few days of opening the well to flow after pumping using typically gathered field data. FBA is a low-cost method that consists of analyzing post-frac flow test data to diagnose completions and estimate key fracture and SRV/matrix properties using less than two weeks of flowing data.
Incorporating FBA diagnostic in hydraulic fracture optimizations workflows allows operators to quickly assess how effective their fracture treatments were shortly after opening the well to flow, allowing these learnings to be applied on the next well or next pad, which acts to remove the majority of uncertainty commonly experienced during a drilling season. When combined with hydraulic fracturing modelling, FBA delivers results 6-12 months earlier than other conventional techniques like rate-transient analysis and numerical flow modelling on long-term production data and at a fraction of the cost of other early-time completions diagnostics. A case study from a prolific North American unconventional reservoir will be utilized to demonstrate how FBA was used to allow the operator to diagnose the efficacy of key changes to stimulation design developed using hydraulic fracture modelling to mitigate overlaying zone risk, while maximizing reservoir productivity and capital efficiency. Through the application of this new workflow, early-time hydrocarbon recovery was improved by over 30%, while reducing completion costs by 20%, all without incurring any additional field costs. As a result, the operator was also able to satisfy the upper management and confidently accelerate their development program within the same drilling season.
With the application of this new workflow, operators are able to, for the first time, fully leverage the benefits of hydraulic fracture optimization without relying on cost-prohibitive field diagnostics to assess stimulation outcomes. Inclusion of FBA diagnostic in these workflows allows the operator to utilize commonly gathered data to quickly and effectively diagnose fracture efficacy shortly after pumping and finally accelerate their learning curve ahead of their capital spending.
Assessment of the Retrofit Potential of Existing Wellbores for Geologic CO2 Sequestration Applications
Authors: Heimerl Joseph , Ma Zhiwei , Chen Bailian , Mehana Mohamed , Van Wijk Jolante 
Published: May 2023

Abstract
Addressing the existential threat posed by climate change requires the widespread adoption of carbon dioxide (CO2) emission-limiting and atmospheric reduction methods. Point source capture of CO2 at power generation and manufacturing facilities, as well as direct air capture from the atmosphere, can reduce atmospheric CO2. After capture, CO2 will need to be stored long-term. One option is the implementation of geologic sequestration (GS) of CO2. Within the United States, oversight authority of CO2 sequestration wells is conducted by the Environmental Protection Agency (EPA), which designates GS wells as "Class VI wells". So far, only a few Class VI wells have been made operational, with a few dozen more in the permitting process. This volume will not be sufficient to meet the need for full-scale CO2 sequestration, and additional wells will need to be delivered in an economic and timely fashion to allow for large-scale CO2 sequestration. A promising option to boost storage capacity would be to retrofit some of the nation's millions of legacy oil and gas wells for use as sequestration wells. We present an analysis of wellbore retrofit, the process of augmenting an old well to meet Class VI requirements, conducted by looking at the EPA regulations, carbon dioxide compatible well design, and a pilot review of some United States wells. This study does not include a review of the geological considerations associated with sequestration but focuses on what will be necessary on a well-by-well basis for successful retrofit. The findings of this analysis illustrate that wellbore retrofits for long-term carbon dioxide sequestration is only suitable for wells that possess the correct combination of well factors and will inject a clean CO2 stream. For ideal candidates, a retrofit would allow for significant time and cost savings compared to a newly- drilled wellbore. The document concludes with considerations for publicly available wellbore data that could facilitate a more robust search for future wellbore retrofits through state databases with the collaboration of operators.
Comparative Machine Learning Frameworks for Forecasting CO2/CH4 Competitive Adsorption Ratios in Shale
Authors: Ma Haoming , Yang Yun , Xue Zhenqian , Chen Zhangxin 
Published: May 2023

Abstract
Accurate modeling of CO2/CH4 competitive adsorption behavior is a critical aspect of enhanced gas recovery associated with CO2 sequestration in organic-rich shales (CO2-ESGR). It not only improves the ultimate recovery of shale gas reservoirs that satisfies the increasing energy demand but also provides permanent geologic storage of atmospheric CO2 that contributes to the net-zero energy future. Determining a CO2/CH4 adsorption ratio is essential for the performance prediction of shale gas reservoirs and the evaluation of CO2 storage potential. However, experimental adsorption measurements are expensive and time-consuming that may not always be available for shale reservoirs of interest or at the investigated geologic conditions, and as a result, a sorption ratio cannot be assessed appropriately. Traditional models such as a Langmuir model are highly dependent on extensive experiments and cannot be widely applied. Therefore, a unified adsorption model must be developed to predict the CO2/CH4 competitive adsorption ratios, which is essential for CO2 sequestration and exploitation of natural gas from shale reservoirs.
In recent years, the development of machine learning algorithms has significantly improved the accuracy and computational speed of prediction. In this work, we conducted a comparative machine learning algorithm study to effectively forecast the maximum CO2 adsorption capacity and CO2/CH4 competitive adsorption ratios. Four sensitive input parameters (i.e., temperature, total organic carbon, moisture content, and maximum adsorption capacity of CH4) were selected, along with their 50 data points collected from the existing literature.
The artificial neural network (ANN), XGBoost, and Random Forest (RF) algorithms were investigated. By comparing the mean absolute errors (MAE) and coefficients of determination (R2), it was found that the ANN models can successfully forecast the required outputs within a 10% accuracy level. Furthermore, the descriptive statistics demonstrated that the CO2/CH4 competitive adsorption ratios were generally from 1.7 to 5.6. The proposed machine learning algorithm framework will provide insights beyond the isothermal conditions of classical adsorption models and the solid support to CO2-ESGR processes into which competitive adsorption can be a driven mechanism.