bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interwell Connectivity Study
Authors: Shan Youngbin , Wu Yaoguang , Qin Minjun , Liu Dongming , Yao Bin 
Published: March 2022

Abstract
Understanding interwell connectivity is crucial for EOR decision making.
In 1990, K.N Wood et al proposed a method to evaluate the interwell Residual Oil using a reactive tracer and a non-partition tracer. A decade later in 2001 (Joseph Tang, 2001), Joseph Tang et al proposed a method to identify the single well near bore residual oil saturation by puff and huff approach in a single well carbonate reservoir. Today the interwell connectivity is still under research.
The objective of this paper is to propose latest study to evaluate interwell connectivity through two or more partitioning tracers to estimate the breakthrough, pore volume, sweeping channel geometry, high permeability channel, residual oil saturation, etc
Thanks to the new development in tracer technologies, today we can use two distinctive tracers to pump through injection well and collect tracers produced in all production wells. The different partition coefficients for two tracers can reveal the lag factor for the sweeping channel and further derive the statistical channel breakthrough time, pore volume, geometry, tortuosity and residual oil saturation. The theory, derivation and applications of the concepts are described in this paper.
Based on the analysis, sweeping channels statistical information can be calculated by a simple mathematical expression of the ratio of two distinctive tracer mass produced from production wells, the ratio of two tracer dynamic partitioning coefficients and the ratio of two injected tracer mass. With this information, operator can investigate a compartmentalization in the field to optimize flooding plan.
One 9-piont injection well grid were analyzed, and results are shown in this paper. Those results are important input to operators' reservoir model. It revealed the major sweeping channels and azimuths, the major residual oil channel and their azimuths, the possible tortuous channels and their azimuths which gives operator a direction of where the residual oil resides and how easy or difficult it can be recovered in tertiary oil production.
This new theory analyzes sweeping channel statistical information from produced masses of two distinctively partitioning tracers, which follows a rigorous mathematical derivation and setup a volume factor equation relating to produced masses of two partitioning tracers. The partitioning coefficient is also modified by a dynamic factor to better simulate the moving partition in channel rather than the static partitioning between brine and oil.
Automated Corrosion Log Quality Control and Interpretation Using Machine-Learning
Authors: Larbi Zeghlache Mohamed , Rourke Marvin , Liu Xiaotian 
Published: March 2022

Abstract
With the increasing focus on data mining and machine learning (ML) applications in the oil and gas industry, the substantial number of well integrity logs and variety of data types represent a suitable candidate for the implementation of automated corrosion log processing. Convolutional neural networks (CNNs) are used in many fields, especially for image processing and features recognition. On the other hand, genetic algorithms (GA) add a valuable benefit to data processing in terms of global search and optimization. This paper demonstrates the integration of ML techniques with legacy well integrity log data, improving the results and leading to a tangible time and cost savings.
A downhole well integrity evaluation triangle comprises three important services for comprehensive diagnosis: 1) cement evaluation, 2) corrosion inspection, and 3) leak detection. These services produce multiple datasets from a variety of logging tools. The types and sources of these datasets include synthetic data from simulation and modeling, tool calibration and lab testing, as well as raw, processed, and interpreted data. This paper describes the use of advanced ML techniques to scrutinize and improve well integrity evaluation. The new process resolves the recurrent challenges of well integrity evaluation in complex completion and downhole environments. It also maximizes value from existing well and field data.
Image features recognition enables major improvements in the data analysis, such as the identification of concentric casings and tubing as well as their respective collar depths and types. In addition, input parameters and well schematics promote quality control of recorded data versus the model data. The new process helps to identify casing and completion accessories and provides a reliable benchmark. Another major element is the qualitative and quantitative evaluation of corrosion using deep learning algorithms combined with the GA. This evaluation is achieved using feature extraction from the forward model (FM) data in an analogous way to collar identification in an electromagnetic decay image. The integration of big data and advanced ML enables an improved data analysis with automated data quality control (QC) and interpretation. A more pro-active well integrity management system will result.
Testing and validation using field examples demonstrate the benefits of this new methodology. The outcome is a better-quality answer product that helps depict various aspects of the acquired and interpreted data. Savings in time and cost are complemented with an improved and automated quality control.
Automated Seismic-Well Tie Using Dynamic Time Warping and Optimal Interpolation
Authors: Promrak Wisit , Uttareun Ratchadaporn 
Published: March 2022

Abstract
Seismic-well tie is a crucial process to correlate subsurface information from well logs and acquired seismic data. Traditionally, a manual seismic-well tie is conducted based on the interpreter's visual pattern recognition, which is subjective, time-consuming, and may lead to unrealistic velocity distortion. This paper presents a new method to automatically tie seismic to well using Dynamic Time Warping (DTW) and Optimal Interpolation (OI), to save man-hour and to obtain a more reliable time-depth relationship. To produce a better tie, we use DTW to seek the appropriate amounts of time stretching and squeezing to match the synthetic and actual seismic. Then, we balance the rigid pattern matching of DTW by using OI to smooth DTW results and constrain changed rock velocity to be within physical bound. The invented technique has been used to tie seismic to six exploration wells in the Gulf of Thailand. The results from the automated method are then compared with the manual method. For all wells, resulting synthetic-seismic correlations from the automated well tie are higher than the manual method by 1.6%-14.9%. Applied time shifts from the automated and manual methods are then compared. Notably, time adjustment correlations between the automated and manual well tie are considerably high, around 72%-85%, suggesting that both methods yield similar outcomes, yet the automated well tie gives a slightly higher match between tied synthetic and observed seismic traces.
A Technical Limits Weight Control Tool for Integrity Management of Aging Offshore Structures
Authors: Ng Sok Mooi , Khan Riaz , Lee Luong Ann , Isnadi Biramarta , Tang Horng Eng , M Salleh Fahima 
Published: March 2022

Abstract
This paper presents a digitized Technical Limits Weight Control (TLWC) tool for integrity management of existing fixed offshore structures. The tool is currently integrated into PETRONAS's Structural Integrity Compliance System (SICS), which is a web-based application for Structural Integrity Management (SIM) of fixed offshore structures.
The main capability of the TLWC tool is to allow a user (engineer/ manager) to determine the fitness for purpose of an offshore facility quickly and efficiently for the inclusion of additional topsides loading at various locations on the platform decks. The tool makes use of global ultimate strength analyses results which considers the acceptance criteria for each operating region in Malaysian Waters. The limits of these criteria are used to create contour boundaries, which depending on Centre of Gravity (CoG) shifts due to topsides loading patterns.
The tool can provide a high-level check on platform suitability for such increase in loading to cater for further development or operational needs. This quick check enables decision making at early engineering stage without the use of further elaborate and costly analyses. The contour plot shown in the results indicates the limiting acceptable Reserved Strength Ratio (RSR) for that platform.
The TLWC decision-making tool is ideal for practicing offshore structural integrity engineers in operations to make a quick decision with regards to addition of topside loading for platforms with marginal reserved strength. Detailed assessment is required to address potential local member overstress for the topside structural members.
Advanced Inspection Technologies for Corrosion Underneath Splash Zone Riser Hangers and Clamps
Authors: Ruangpattanatawee Kamonwan , Laemkhowthong Chatchai , Thepsriha Suthisak , Promsakulchai Sorakom , Thammachart Matina , Limsakul Chanapol , Lertthanasart Athipkiat 
Published: March 2022

Abstract
Corrosion underneath riser hangers and clamps in the splash zone area is historically challenging for inspectors. It is a chronic problem for offshore pipeline operators which could lead to significant failures and loss of primary containment. When the degradation of the protective coating system occurs, it will result in external severe corrosion. The splash zone riser is exposed to intermittent seawater wetting. Especially at crevice areas which can form and accelerate small concentration corrosion cells creating indiscernible localized corrosion or deep grooves.
Close visual inspection (CVI) is a conventional nondestructive examination (NDE) technique to notify a sign of corrosion. This is a very subjective and qualitative measurement. Wall loss, depth, and sizing are unknown. In order to identify the condition underneath the riser clamp without clamp removal, the company has studied the principle of advanced NDE techniques, mockup tests, and field trials. The main objective is to identify, quantify, and prioritize the severity of corrosion anomalies underneath the clamp for further maintenance and repair plans to prevent pipeline failure. The selected techniques are Computed Radiography Testing (CRT), Medium-Range Ultrasonic testing (MRUT), and Long-Range Ultrasonic testing (LRUT). The result shows that LRUT can be further developed to suit the company's purposes.
Successful Development and Deployment of a Global ROP Optimization Machine Learning Model
Authors: Robinson Timothy S. , Batruny Peter , Gomes Dalila , Hashim M. Meor Hakeem Meor , Yusoff M. Hazwan , Arriffin M. Faris , Mohamad Azlan 
Published: March 2022

AbstractDrilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application.A drilling dataset was gathered from exploration and development wells in both onshore and offshore operations from a variety of fields and regions. The wells were curated to have different water depths, down hole drive such as Rotary Steerable System (RSS), PDM, Standard Rotary, bit types (Mill Tooth, TCI, PDC) and inclinations (vertical or deviated). A deep neural network was used for modelling the relationship between ROP and inputs taken from real-time surface data, such as Torque, Weight-on-Bit (WOB), rotary speed (RPM), flow and pressure measurements. The performance of the ROP model was analyzed using historical data via summary statistics such as Mean Absolute Percentage Error, as well as graphical results such as residuals distributions, cumulative distribution functions of errors, and plots of ROP vs depth for independent holdout testing wells not included in the model fitting process. Analysis was done both in aggregate, and for each specific well.The ROP model was demonstrated to generalize effectively in all cases, with only minor increases in error metrics for the holdout test wells, where the Mean Absolute Percentage Error averaged across wells was ~20%, compared to 17.5% averaged across training wells. Furthermore, residuals distributions were centered close to zero, indicating low systematic error. This work proves the case for a "global" ROP prediction model applicable "out-of-the-box" to a broad set of drilling operations.A global ROP model has the potential to eliminate learning curves, reducing time and costs associated with having to develop a new model for every field. Furthermore, a model that effectively captures the relationships between parameters controllable by drillers and ROP can be used for automatically identifying drilling parameters that improve ROP. Preliminary field-testing of the ROP optimization system yielded positive results, with many examples of increased ROP realized after following drilling parameter recommendations provided by the software.
Sustainability Aspects for the Offshore Sector - Bridging Operations, Carbon Accounting and Esg Principles
Authors: Soultanias Ilias , Sarvaiya Jatin , Bose Aditya , Koutsourakis Panos , Plevrakis Georgios 
Published: March 2022

Abstract
With climate change as a transformative catalyst, the Offshore sector, which has long been a main pillar of the energy industry, is expected to undergo significant transformation in the coming years. Unique challenges associated with the fossil fuel products and their environmental impact are expected. Despite the limited and localized regulatory framework, the main drive towards sustainability is coming from the financing sector and other key external stakeholders.
Embracing sustainability pertains to a series of initiatives and actions around: Benchmarking environmental footprint of the assets and operationsDesign, Engineering, Procurement & Construction and subsequent Operations following certain sustainability criteriaIntroducing Environmental, Social and Governance reporting
This article elaborates on the structured approach of Sustainability principles and initiatives on the highly carbon intense offshore oil & gas industry.
Application of Machine Learning to Augment Wellbore Geometry-Related Stuck Pipe Risk Identification in Real Time
Authors: Othman Eswadi Bin , Gomes Dalila , Tengku Bidin Tengku Ezharuddin Bin , Meor Hashim Meor M. Hakeem , Yusoff M. Hazwan , Arriffin M. Faris , Ghazali Rohaizat 
Published: March 2022

Abstract
Wellbore geometry stuck pipe mechanism occurs when the string and the well are incompatible with each other. This issue is commonly related to changes in hole diameter, angle, and direction associated with symptoms such as mobile/swelling formation, undergauged hole, key seating, ledges, and high doglegs. An internal study identified that many stuck pipe incidents were associated with mechanical sticking, specifically wellbore geometry sticking with high-cost impact, which warrants proactive prevention. Throughout this paper, we provide and demonstrate how machine learning solutions can foresee the potential stuck pipe related to wellbore geometry issues based on two signs: hookload signature and dogleg severity. The application is based on the Artificial Neural Network (ANN) approach that reads the surface parameters sequence of hookload real-time data and learns with historical wells data. Machine learning (ML) then determines how the hookload behaves for each type of activity (tripping and drilling). The machine learning predictions can then be streamed on a web-based application accessible to the operations and project team. The neural network design for hookload prediction while tripping in/out considers a drag when the string moves towards a region with doglegs severity higher than the threshold chosen based on engineering judgment. This paper also discusses applications beyond real-time estimation, such as predicting the trend of the few subsequent expected hook loads up to 6 to 10 stands ahead based on case studies from previous live wells obtained from the real-time monitoring center where the product is used. The output from the machine learning solution provided a basis for risk identification and further analysis by the monitoring specialist in a proactive intervention effort to prevent stuck pipe incidents. The implementation of applications described in this paper could detect an early symptom of wellbore geometry issue; hence proactive action can be taken to avoid a potential stuck pipe event.
An Exact Method for a Class of Self-Similar M/Ga/1/K Queueing System of Internet Stream
Authors: Popoola Jumoke , Popoola Olusogo J. , Olaniran Oyebayo R. 
Published: March 2022

In Internet stream monitoring, computing the expected parameters and distributions for the stream process under self-similarity and long-range dependency requires appropriate and exact methods queueing analyses to obtain performance of the Internet. Our method avoids fitting a non-heavy tailed distribution to Internet stream data by specifically fitting Gamma distribution to its service time process, which enhances the features of correlated events that explain self-similarity and long-range dependency compared to the classical memoryless assumptions of standard Poisson arrival and Exponential service. We propose the specific expectations of the parameters for a class of M/Ga/1/k queueing system (‘M’ represents a Markovian arrival process that follows Poisson distribution, ‘Ga’ represents a Gamma distribution service process, ‘1’ represents a single server, ‘k’ represents the buffer size) in an exact closed-form derivations using the Pollaczek Khintchine formulae alongside the Laplace transform to obtain the performance of queue. The adequacy of the proposed model was confirmed using simulated data of Internet traffic. Keywords- Gamma distribution, Internet stream, M/Ga/1/K model, Self-similarity, heavy-tail distribution
Heavy Metal Pollution Levels of Hand-dug Wells in Close Proximity to Septic Tanks
Authors: Ojo Ochuko M. 
Published: March 2022

The aim of this study is to evaluate the levels of selected heavy metal contamination in well water located in close proximity to septic tanks in the South gate area of the Federal University of Technology, Akure (FUTA). Water samples were collected from five (5) randomly selected hand-dug wells in the study area within 50ft (15.4m) distance to septic tanks. The physical attributes of the wells revealed that the wells were well constructed and all the wells sampled were lined with precast concrete pipe (PCP) rings with covers. Five heavy metals namely Cadmium (Cd), Chromium (Cr), Iron (Fe), Manganese (Mn) and Zinc (Zn) were analyzed in the laboratory using Atomic Absorption Spectroscopy. Cd, Cr and Mn were present in high concentration in some of the water samples evaluated exceeding the WHO permissible limit. However, Fe and Zn contents of the water fell within WHO standard for potable water. Secondary water treatment is recommended before human consumption. Keywords— heavy metals, potable, septic tanks, treatment, water samples.
Gaya Kepemimpinan Direktur Rumah Sakit Umum Daerah di Provinsi Gorontalo
Authors: Yantu Irwan 
Published: March 2022

Penelitian mengambil lokasi di Rumah Sakit yang ada di Provinsi Gorontalo yakni; RSUD Aloei Saboe, RS Islam, RS Multazam, RS Bunda, RS Otanaha, RS Toto, RS Ainun Habibie, RS Tani Dan Nelayan, dan RS ZUS. Penelitian dilaksanakan selama rentang waktu April – Oktober 2020. Penelitian ini menggunakan pendekatan kualitatif deskriptif. Informan dipilih dengan menggunakan teknik snowball yakni proses penentuan informan berdasarkan informan sebelumnya tanpa menentukan jumlahnya secara pasti dengan menggali informasi terkait topik penelitian yang diperlukan. Informan dalam penelitian ini, yaitu seluruh pegawai yang ada mencakup kepala tata usaha, kepala seksi penunjang dan pelayanan, para dokter umum, perawat, bagian perencanaan, staf kepegawaian, dan para koordinator bagian. Data dianalisis mengikuti konsep yang yang diberikan Miles dan Huberman yang meliputi pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan/verifikasi. Hasil penelitian menunjukkan bahwa Direktur RS di lingkungan Provinsi Gorontalo menjalankan ketiga gaya kepemimpinan namun lebih cenderung pada gaya kepemimpinan Demokratis yakni kepemimpinan dimana pemimpin selalu bersedia menerima dan menghargai saran-saran, pendapat dan nasehat dari staff dan bawahan, melalui forum musyawarah untuk mencapai kata sepakat. Kata Kunci: Otoriter; Demokratis; Laissez Faire
Peningkatan Kemampuan Pemecahan Masalah Matematika Siswa Melalui Pendekatan Matematika Realistik
Authors: Saragih Risna Mira Bella , Simamora Yumira 
Published: March 2022

Masalah dalam penelitian ini adalah kemampuan pemecahan masalah siswa rendah dan siswa tidak terbiasa melakukan penyelesaian masalah dengan berbagai prosedur. Penelitian ini bertujuan untuk untuk mengetahui perbedaan peningkatan pemecahan masalah siswa. Penelitian ini merupakan penelitian kuasi eksperimen. Populasi penelitian ini siswa kelas VII SMP Budi Satrya. Instrumen yang digunakan tes pemecahan masalah matematika. Instrumen tersebut dinyatakan telah memenuhi syarat validitas isi, serta koefisien reliabilitas sebesar 0,95. Analisis data dilakukan dengan uji t dan Mann Withney. Hasil utama dari penelitian ini adalah terdapat perbedaan peningkatan kemampuan pemecahan masalah siswa yang memperoleh pembelajaran dengan pendekatan matematika realistik dengan siswa yang memperoleh pembelajaran dengan pendekatan biasa.
Penerapan Pendekatan Open Ended dalam Pembelajaran Matematika Realistik
Authors: Matondang Khoiruddin , Matondang Ade Rahman 
Published: March 2022

Tujuan penelitian ini melihat penerapan pendekatan open ended dalam pembelajaran matematika pada sub pokok bahasan segi empat di kelas VII SMP Swasta Dwitunggal Tanjung Morawa. Jenis penelitian ini adalah penelitian tindakan kelas. Teknik analisa data pada penelitian ini adalah menggunakan metode analisis deskriptif kuantitatif kualitatif, dengan tujuan untuk mengetahui apakah hasil belajar siswa meningkat akibat di terapkannya pendekatan Open Ended. Hasil penelitian ini adalah pada siklus I hasil belajar siswa dengan nilai rata-rata yaitu 64,72, di mana hanya 10 siswa atau 27,78% yang tuntas, sedangkan 26 siswa atau 72,22% tidak tuntas. Pada siklus II nilai rata-rata yaitu 70,41, di mana hanya 16 siswa atau 44,45% yang tuntas, sedangkan 20 siswa atau 55,55% tidak tuntas. Pada sisklus III nilai rata-rata yaitu 82,91, di mana ketuntasan siswa meningkast yaitu jumlah siswa yang tuntas belajar bertambah menjadi 32 siswa atau 88,89 %, sedangkan 4 siswa atau 11,11% tidak tuntas.