bluuebunny/crossref_metadata_2025 huggingface dataset:
Contextual teaching learnıng in human digestive system: The contribution of critical thinking skills
Authors: Bustami Yakobus , Leliavia L , Elisabeth Nyeria , Gandasari Adriana , Ratnasari Desi
Published: June 2020

Critical thinking skills and students' understanding are the supporting factors in students’ learning success. This research aimed to determine the contrıbutıon of critical thinking skills for the understanding on human digestive system concepts when contextual teaching and learning (CTL) was implemented. The samples were 45 students consisting of public junior high schools 2 and 5 class VIII A at Sungai Tebelian. The instrument of critical thinking skills were essay test totaling of 6 items and to measure the understanding on human digestive system concepts, 20 items of multiple choices test were used. Research data were analyzed using a simple regression correlation analysis. The results showed a significant correlation of critical thinking skills to the understanding of human digestive system concepts. The regression equation of the correlation between the two variables was Y = 0.736X + 22.861. The correlation coefficient was 0.640, with the contribution the critical thinking skills to human digestive system concepts of 41.00%. These results showed that there was a significant correlation between critical thinking skills and human digestive system concepts when CTL was implemented in learning. Thus, biology teachers need to develop students’ critical thinking skills through the implementation of CTL  in learning.
5E learning cycle combined with mind mapping in excretory system: effectiveness on curiosity
Authors: Utami Dhani Nugrahaningtyas , Subali Bambang
Published: June 2020

Curiosity is one of the important characters developed in the 21st century. This study aimed to determine the effectiveness of the 5E (engage, explore, explain, elaborate, dan evaluate) learning cycle model combined with mind mapping on the curiosity of XI classes students in the excretion system material. This research was an experimental study with a non-equivalent pretest-posttest control group design. The study population included all students of XI science classes in Yogyakarta, Indonesia that amounted to 96 people. Samples were 32 for experimental as well as control group, with a total of 64 people. The instrument used was a non-test instrument in the form of a questionnaire consisting of 20 items. The validity of the instruments was good, and the reliability was very high, so the instruments were suitable to be used in data collection. Based on the results of data analysis using the Mann Whitney test, it was found that there were significant differences in the use of the 5E learning cycle model combined with effective mind mapping on student curiosity (p: 0.007 <0.05). It is recommended for Biology teachers to use the 5E learning cycle model with mind mapping to improve student curiosity in excretory material.
EFFECTIVENESS INSTRUMENT TEST ON PLAY-BASED METHOD OF FOOTBALL‘S WARM-UP MODEL
Authors: Qorry Armen Gemael , Deden Akbar Izzuddin , Rolly Afrinaldi
Published: June 2020

This study aims to determine the value of the validity and reliability of the effectiveness instruments of football warm-ups with a play-based method involving students of the University of Singapore in Karawang who took a football course as a research sample consisting of 20 students. The research method used is descriptive research and data collection techniques using test and measurement methods. The instrument used in this research and development was a warm-up instrument used to collect subject data, the results of the sit and reach instrument validity test was 0.979, the Static Flexibility Test - Ankle was 0.913, the Standing balance test was 0.942 while the results of the sit and reach reliability test , Static Flexibility Test - Ankle, and Standing balance of 0.940. From the results of the validity and reliability test, the instrument warm-up can be used. Researchers suggest that warm-up instruments be used by coaches to measure muscle, joint and body balance in soccer athletes, especially in the Karawang area of ​​West Java.
Efficacy of Transcartilaginous Electrodes for Intraoperative Neural Monitoring During Thyroid Surgery
Authors: Jung Seon Min , Tae Kyung , Song Chang Myeon , Lee Seung Hwan , Jeong Jin Hyeok , Ji Yong Bae
Published: June 2020

Objectives. This study was conducted evaluate the efficacy of electromyography (EMG) using transcartilaginous (TC) electrodes through the thyroid cartilage and perichondrium.Methods. We prospectively collected EMG data from intraoperative neuromonitoring (IONM) of 54 nerves at risk in 38 patients during thyroidectomy. We followed standardized IONM procedures in all operations. EMG signals from both endotracheal tube (ET) electrodes and TC needle electrodes were recorded simultaneously. We compared the characteristics of the EMG signals and the efficacy of both methods.Results. Significantly higher mean EMG amplitudes were recorded by TC electrodes than by ET electrodes in all four-step procedures (V1-R1-R2-V2, P<0.001). Loss of signal (LOS) occurred in five patients in ET electrodes, but in only two patients in TC electrodes. Postoperative laryngoscopy revealed recurrent laryngeal nerve palsy in the two patients who showed LOS from both the ET and TC electrodes, and vocal cord movement was intact in the other three patients. Therefore, the positive predictive values of LOS in ET and TC electrodes were 40% and 100%, respectively.Conclusion. EMG recording using TC needle electrodes is feasible and effective, making it a good alternative technique for IONM.
Avoiding Limb-Length Discrepancy with Reconstruction of a Massive Tibial Defect Using a Bone Allograft and a Minimally Invasive Lengthening System in a Pediatric Patient
Authors: Alder Kareme D. , Morris Montana T. , Hao Zichen , Li Lu , Yu Kristin E. , Lee Francis Y.
Published: June 2020

Case:

A 7-year-old boy was found to have Ewing sarcoma of the left tibia. The sarcoma was resected, and the defect was reconstructed using a humeral head allograft and intramedullary limb-lengthening nail.

Conclusions:

Limb-salvage reconstruction in children can be complicated by the sacrifice of epiphyseal plates and limb-length discrepancies and thus requires techniques tailored to each case.
Team Approach: Same-Day Discharge of Patients Undergoing Total Joint Arthroplasty
Authors: Crawford Alexander M. , Chen Antonia F. , Sabeti Amirmehdi , Jay Jean F. , Shah Vivek M.
Published: June 2020

» Same-day-discharge total joint arthroplasty is becoming increasingly feasible because of advances in anesthesia, physical therapy, surgical techniques, and perioperative protocols.

» In appropriately selected patient populations, same-day-discharge total joint arthroplasty is safe, does not increase readmission rates, and is cost-effective.

» As the push for value-driven health care continues and the incidence of total joint arthroplasty continues to grow, same-day-discharge arthroplasty will likely become more prevalent and increasingly incentivized.

» A team-based approach to each phase of care is vital for effective and safe same-day-discharge total joint arthroplasty.
INSTAGRAMDA MARKA İMAJI YARATMADA ETKİLEYİCİ KİŞİLER VE MÜŞTERİ ETKİLEŞİMİ: SPONSORLU ÜRÜN YORUMLARININ ANALİZİNE YÖNELİK BİR ÇALIŞMA
Authors: EFE Ayşenur , VENTURA Keti
Published: June 2020

Instagram’ın popülerlik kazanmasıyla birlikte, işletmeler ticari kazanç sağlamak için bu mecrayı pazarlama faaliyeti olarak kullanmaya başlamışlardır. Son dönemde özellikle “etkileyici” adı verilen kişilerin, gerek tüketicilerin bireysel satın alma kararlarını gerekse marka imajı yaratmada ve müşterilerle etkileşim yaratmak amacıyla sıklıkla kullanıldıkları bilinmektedir. Instagram üzerinde deneyim ve bilgi paylaşan “etkileyici”lerin paylaşımları tüketicilerin zihninde kaynağa güvenilirlik açısından her zaman olumlu bir marka imajı ve etkileşim yaratamamaktadır. Ancak etkileyici kişilerin sponsorlu ürün olarak yaptıkları paylaşımlarda bu durumun farklılık gösterdiği görülmektedir. Etkileyici kişilerin sıklıkla kullanıldığı alanlardan biri de şüphesiz ki makyaj ve güzellik sektörüdür. Instagram’da bu alanda oldukça yoğun ürün ya da hizmet odaklı paylaşımlar yapıldığı görülmektedir. Bu kapsamda çalışmanın amacı, kozmetik ve yaşam alanında paylaşımlarda bulunan bir etkileyici kişinin sponsorlu ürünleri deneyimlemesi üzerine, yaptığı olumlu ve olumsuz ürün yorumlarının takipçilerinin marka imajı ve etkileşim üzerindeki etkisini incelemektir. Keşifsel bir araştırma niteliği taşıyan bu araştırmada 2011 yılından beri kozmetik ve yaşam alanında deneyim ve önerilerini paylaşan “etkileyici kişi” Görkem Karman’ın, Instagram üzerinden olumlu ve olumsuz sponsorlu ürün deneyimlerinin takipçilerinde yarattığı marka imajı ve etkileşimi değerlendirmek amacıyla olumlu ve olumsuz ürün deneyimi paylaşımlarına yapılan takipçi yorumlarına yönelik içerik analizi uygulanmıştır.
AŞIRI İŞ YÜKÜNÜN İŞ TATMİNİNE ETKİSİNDE İŞYERİ MANEVİYATININ DÜZENLEYİCİ ROLÜ
Authors: PELENK Saadet Ela , ACARAY Ali
Published: June 2020

Çalışma hayatındaki görev ve sorumluluklar, her birey için farklı bir anlam ifade etmektedir. Bazı çalışanlar, aşırı iş yükünün görevlerinin bir parçası olduğunu düşünürken; bu durum, diğerlerini fiziksel ve psikolojik olarak zorlayabilmektedir. Ancak, “insan olarak yaşamanın” her açıdan zorlaştığı dünyada manevi düşünceler, kişiye pozitif bir duygu ve anlam kazandırabilir. Yaşam rotasını bulmaya yardım eden maneviyat, iş yerinde çalışanın tatmin ve paylaşma duygusunu arttırabilir. Bu noktada aşırı iş yükü, stres, yıkıcı eleştiri, haset vb. negatif duygu ve durumların çalışana verdiği zarar da azalabilir. Bu çalışmanın temel amacı, aşırı iş yükünün iş tatmini üzerine etkisi ve bu etkide işyeri maneviyatının düzenleyicilik rolünü incelemektir. Araştırmanın verileri, bankacılık sektöründeki 166 çalışandan anket tekniği ile toplanmıştır. Araştırmada, kolayda örnekleme yöntemi kullanılmıştır. Yapılan analiz sonuçları; aşırı iş yükünün iş tatminini negatif olarak etkilediğini; iş yeri maneviyatının, iş tatminini pozitif olarak etkilediğini ve aşırı iş yükü ile iş tatmini arasındaki ilişkide işyeri maneviyatının düzenleyicilik rolü üstlendiğini ortaya koymuştur. Çalışmanın, İnsan Kaynakları Yöneticilerine en önemli katkısı, iş yeri maneviyatının iş görenlerin tutum ve davranışlarının yönlendirilebilmesinde önemli bir değişken olabileceğidir.
THE KEY ACTORS OF KNOWLEDGE PRODUCTION FUNCTION IN TURKEY: THE ROLE OF UNIVERSITIES AS A KNOWLEDGE PRODUCER
Authors: IŞIK MADEN Selen , SEZGİN Aykut , BAYKUL Ayşegül , UNUTULMAZ Sevim , DULUPÇU Murat
Published: June 2020

By taking the projects which are carried out in the universities in Turkey and are funded by the Scientific and Technological Research Council (TÜBİTAK) as an output, the paper tries to estimate the knowledge production function of Turkey. In the study, an OLS analysis is made with cross-section data by utilizing the datum of state universities operating in Turkey at the NUTS III level. The dependent variable of the knowledge production function that is developed by using Cobb-Douglas production function is the annual R&D fund transferred to universities by TÜBİTAK for the projects. Other variables in the analysis are the annual R&D expenditures of universities, the number of academic staff, number of postgraduate students and the existence of Technology Transfer Office and/or Technopolis. According to the analysis results there is a positive and significant relationship between R&D fund, and postgraduate students and technology transfer offices/technopolis. However, the relationship between R&D expenditures and the R&D funds transferred to the universities is found to be statistically insignificant. A negative and insignificant relationship is found when R&D funds and academic staff are estimated together, but in the models in which academic staff is decoupled, the results differ.
Modelling of Calcite Deposition on Geothermal Well Inflow Performance
Authors: Mousavi Mirkalaei Seyed Mousa , Edmondson Mark Douglas , Kumar Anjani
Published: June 2020

Calcite deposition in formation and wellbore may significantly reduce the inflow performance of geothermal wells where CO2 is present in brine. The key operational and reservoir parameters influencing the magnitude of impairment by calcite deposition were identified through dynamic simulation modelling based on the rate of calcite deposition, effective tubing radius reduction and its effect on flow rate.

Chemical reactions were modeled to generate calcite in the liquid phase. Subsequently, the flowing calcite may convert to the solid phase due to temperature and pressure effects. A method called flexible wellbore modelling (FlexWell) was used to implicitly solve wellbore equations on a separate grid, which allows the well to undulate through multiple layers to more accurately follow the real trajectory. The method also allows the chemical reactions and calcite deposition to be modeled both inside the wellbore and in the reservoir. FlexWell performs a dynamic calculation of the wellbore's hydraulic radius as well as permeability reduction in the reservoir. This permits the investigation of calcite deposition processes on well inflow performance for specific reservoir conditions and well operations.

The results of dynamic simulation modelling show that the rate of calcite scaling in the wellbore is directly proportional to the hydraulic diameter of the well. For a given flow rate, the degree of calcite deposition in the wellbore and around the wellbore may be significantly reduced by decreasing the drawdown pressure (or pressure gradient) near the well and/or increasing the hydraulic diameter (effective wellbore radius) through well-stimulation techniques. Generally reducing the flow rate will increase the well production time before flow is significantly impaired by deposited calcite.
Evaluating the Effects of Acid Fracture Etching Patterns on Conductivity Estimation Using Machine Learning Techniques
Authors: Desouky Mahmoud , Aljawad Murtada Saleh , Alhoori Hamed , Al-Shehri Dhafer
Published: June 2020

The successful design of an acid fracture job requires accurate prediction of fractured well productivity. Productivity estimation demands knowledge of both the acid penetration length and conductivity distribution for the given reservoir. The literature includes several models developed to predict the conductivity of acid fractured rock. The most popular is empirical and based on measuring the conductivity of 25 acid fracture experiments. The present research provides empirical models utilizing machine learning techniques and incorporating 97 experiments and 563 datapoints.

We conducted an extensive literature review to collect the published data on acid fracture experiments. The objective of such experiments is to measure conductivity at different formation closure stresses while considering field conditions. We used several data preprocessing techniques to clean the data, fill in missing values, exclude outliers and failed experiments, and standardize the dataset. Regularization was employed to eliminate features that didn't contribute to accurate prediction. Feature engineering was used to construct new features from our dataset. We began by measuring the correlations between features to better understand the data. We then built various machine learning models to predict acid fracture conductivity.

It has been observed that developing one universal empirical correlation often results in significant errors in conductivity estimation because different rock types result in different etching patterns that cannot be explained by a single correlation. For instance, the channeling etching pattern is mostly observed in limestone formations, while a roughness pattern is seen in dolomite and chalk rock. Moreover, the conductivities of etching patterns formed in chalk, dolomite, and limestone formations behave differently. We built machine learning classification techniques to determine the most likely etching patterns (e.g., channeling, roughness). A linear regression-based model was then developed as a baseline for comparison with other models generated through ridge regression. We evaluated the performances of our models using well-known metrics such as accuracy, precision, recall, mean squared error, and correlation coefficients. We also employed cross-validation to avoid over-fitting, finding that certain features were the most important in predicting acid fracture conductivity.

Detailed empirical conductivity correlations and models were developed in this work for three carbonate rock types. Previously, a single empirical model has often been employed to estimate acid fracture conductivity or, at best, a model has been developed for a particular rock type. Most models have not considered the impact of etching patterns on conductivity, which was found to be significant in limestone.
Performance Evaluation of Long-Horizontal-Well Solvent-Assisted SAGD in Rising Stage
Authors: Liu Hao , Wang Yuan , Cheng Linsong , Huang Shijun , Chen Xiao
Published: June 2020

Solvent-Assisted Steam-Assisted Gravity Drainage (SA-SAGD) is invented by adding solvent into the injection stream. However, the high cost of the solvent makes it necessary to ensure a high efficiency of solvent transportation from the long-horizontal wellbore to the chamber edge. In this study, a novel model considering the wellbore/chamber coupling effects is established, which can accurately evaluate the performance of SA-SAGD.

First, governing equations for mass flow in the wellbore are built on the basis of the mass and momentum balance determined by coupling the wellbore and steam chamber. Besides, implicit equations for phase changes were derived according to the heat-mass transfer between wellbore and chamber. Second, the model for oil flow rate in the chamber rising stage is derived by the chamber height identified by the variation in thermo-physical properties of solvent and steam in the chamber along the wellbore. Third, by applying Levenberg-Marquardt Algorithm (LMA), the mathematical model is solved

The results show that: the oil rate of classic SAGD is significantly improved by the solvent of C5, while the improvement effect of C7 is quite small. As for C3-SAGD, the oil rate is even lower than that of traditional SAGD. This is mainly because C3 decreases the chamber temperature and the solubility of C3 in heavy oil is low, which results in a relatively higher oil viscosity at the chamber edge compared to other solvents. The differentials between the cases, which consider the wellbore or not, is different for different solvent injection. Specifically, oil rate differential for C3-SAGD is the highest and it is twice that for conventional SAGD which has the least differential. C5-SAGD has the second-largest differential which is followed by C7-SAGD.

Currently, applying SA-SAGD in a field encounters many challenges, especially in understanding its mechanisms and improving economic efficiencies such as a conflict between the production increase and solvent cost. The newly proposed model not only sheds light on the key mechanism of wellbore/chamber coupling effects, which is usually neglected in the literature but also brings greater benefits by better designing the future SA-SAGD heavy oil recovery projects.
Carbon Dioxide Sequestration in Unconventional Shale Reservoirs Via Physical Adsorption: An Experimental Investigation
Authors: Fakher Sherif , El-Tonbary Ahmed , Abdelaal Hesham , Elgahawy Youssef , Imqam Abdulmohsin
Published: June 2020

Carbon dioxide (CO2) is the main greenhouse gas contributing to environmental damage and global warming. It is emitted as a result of many processes, part of which is combustion of oil and gas. One of the methods by which CO2 emissions can be controlled or reduced is through CO2 sequestration processes. This research investigates the ability to store CO2 in shale reservoirs through adsorption and some of the factors impacting the adsorption capacity. CO2 adsorption was measured using the volumetric adsorption method using pulverized shale particles of uniform size. Initially, the void space in the shale-bearing cell was measured using helium. The void space is used in the CO2 adsorption calculations in order to account for the extra volume created when the shale core was pulverized. The effect of varying the CO2 pressure, temperature, and shale volume on the CO2 adsorption capacity was studied. Results showed that both pressure and temperature had a strong effect of CO2 adsorption, with an increase in pressure resulting in an increase in adsorption and an increase in temperature resulting in a decrease in adsorption. Altering the volume of the shale resulted in a change in adsorption as well due to an increase in error as the shale volume decreased relative to the vessel volume. This research provides insight on the impact of multiple factors on CO2 adsorption to shale particles thus illustrating the potential for CO2 storage in unconventional shale reservoirs.
Numerical Modelling the Stress Dependent Transitional Fluid Flow in Coal Bed Methane Reservoirs
Authors: Nainar Subhashini , Govindarajan Suresh Kumar
Published: June 2020

The effects of the coupled reservoir and fluid properties, in addition to the geomechanical effects in the reservoir, are studied on reservoir pressure and cumulative gas produced. The behavioral variation of reservoir pressure, rock properties, and fluid properties are, numerically, studied both spatially and temporally.

The coal formation, subjected to dewatering and subsequent phases, is analyzed. The study of single-phase water followed by the multiphase flow of gas and water, in the transient region, is carried out. The drained distance dynamically varies with time and expands away from the wellbore. The flow to the wellbore is either a single-phase flow or a combination of single and multiphase flow. The transition between phases modeled to maintain continuity between the flow phases. Cleat and fluid compressibility are varied with time to analyze their individual and coupled effect on flow parameters.

The study details flow pattern at a time and how it changes when the time expands. The multiphase nonlinear advective term affects the flow pattern, which was, initially, diffusive in single-phase. Pressure, at any point in the reservoir, is revised when the flow regime and phases altered. The cumulative gas produced at the end of each phase and the cleat permeability, due to coupled reservoir compaction and matrix shrinkage effects, of the reservoir, in comparison with the original, is estimated. It can be observed, from the results of the study, that the flow pattern remains the same for a phase type with changing time. The flow pattern changes once the phase change from single to multiphase and vice versa.

The study is novel from previous works because the cumulative production is not a function of the constant, but varying, reservoir and fluid property that helps to understand the pressure distribution in the reservoir for every phase at each time step and especially at the transitional zones. The study is beneficial to the industry in analyzing the different fluid phase flow patterns and flow associated parameters, which ultimately, after single-phase gas flow, will give us an idea of the recovery factor of methane gas and the change in reservoir petrophysical parameters from the original state.
Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis
Authors: Clemens Torsten , Viechtbauer-Gruber Margit
Published: June 2020

Digitalization and Artificial Intelligence have impacted the oil and gas industry. Drilling of wells, predictive maintenance and digital fields are examples for the use of these technologies. In hydrocarbon production forecasting, numerical reservoir models and "digital twins" of reservoirs have been used for decades. However, increasing computing power and Artificial Intelligence recently enabled oil and gas companies to generate "digital siblings" of reservoirs (model ensembles) covering the uncertainty range in static data (e.g. petrophysics, geological structure), dynamic data (e.g. oil or gas properties) and economics (Capital Expenditures, Operating Expenditures). Machine Learning and Artificial Intelligence are applied to condition the model ensembles to measured data and improve hydrocarbon production forecasting under uncertainty.

The model ensembles can be used for quantitative decision making under uncertainty. This allows companies to shorten the time for field (re-)development planning and to develop into learning organizations for decision making.

These developments require companies to change the way of working in hydrocarbon production forecasting and decision analysis. Additional skills need to be developed in companies to embrace digitalization. Data science - which is considered a key skill in digitalization - has not been identified as crucial in skills development of oil and gas companies in the past. However, for data driven decision making, advanced data analytics skills and data science skills are a pre-requisite. To overcome this skill gap, staff needs to be trained and graduates with data science and profound physical and chemical skills need to be hired.

Furthermore, skills development has to address the challenge of incorrect use of Machine Learning technologies and the risks of Artificial Intelligence leading to erroneous optimizations. In particular interpretability of AI needs to be covered in skills development.
Compaction Seismicity: What Determines Seismic vs. Non-Seismic Behavior in Dutch Gas Fields?
Authors: de Pater C. J. , Berentsen Cas , Martens Hans
Published: June 2020

Reservoir compaction in depleting gas fields can cause seismicity, as has been observed in a dozen countries (Foulger et al., 2019). So, it is no surprise that some of the Dutch gas fields induce many earthquakes up to magnitude 3.5. Remarkably, the occurrence of Dutch seismicity is strongly clustered in two regions, while most fields induce no recorded seismicity at all. It has been attempted to explain this from differences in reservoir characteristics, but as yet, no reservoir property has been found that can explain the difference in seismicity. A new mechanism is proposed that can explain the regional variation of seismicity.

For a few gas reservoirs, the evolution of potential fault slippage is simulated using the commonly adopted Mohr-Coulomb failure criterion. This shows that fault criticality is expected for reservoirs that showed seismic as well as non-seismic behavior. Apparently, some characteristic property is missing to explain the difference in behavior.

Using published pressure histories for seismically active gas fields, the relation is shown between seismic magnitude and relative depletion. It appears that in many cases, the first induced earthquake is relatively strong which suggests substantial cohesion of the faults. It is plausible from the geological history that in non-seismic regions, fault cohesion is larger, so that slippage is inhibited.
Multi-Dimensional Project Based Learning on Understanding Petrophysical Properties by Utilizing Image Processing and 3D Printing
Authors: Alyafei Nayef , Bautista Jerahmeel , Mari Sahar , Khan Talha , Seers Thomas
Published: June 2020

We present a project-based learning prototype for visual analysis of petrophysical properties using 2D cross-sections and micro-models of porous media. Micro-computed Tomography (CT) scans are used to create the quasi-2D micro-models that are printed using Stereolithography (SLA) 3D printers to study petrophysical properties in porous media. The methodology involves obtaining 8 different cross-sections of rocks either from micro-CT scans or online libraries. 2D cross-sections are segmented into black and white binary images and then skeletonized to create quasi-2D models. The flow of oil and water in initially water saturated pores in the printed 2D models mimics the drainage and imbibition processes, respectively. High definition photography is used to capture still and dynamic photographs of flow processes. The binary images are used to analyze porosity and grain size distribution while the still and dynamic photographs are used to analyze fluid saturation and displacement efficiency. The images are analyzed using open source software where a systematic tutorial is provided. The primary outcome of this project is to improve the understanding of petrophysical concepts and 3D printing by the utilization of imagery to create porous media. This project has been tested in teaching and showed major improvements in students’ understanding of petrophysical concepts when compared to pre-project. The data and tutorials used in this project are made available for the community to use through a link in the paper.
Wavelet Modelling Approach for Reservoir Model Classification
Authors: Alimohammadi Hamzeh , Chen Shengnan , Karimi Hassan , Rahmanifard Hamid
Published: June 2020

In oil and gas industry it is crucial to have reliable information on well, reservoir and boundary types and properties. Detailed information can be extracted from a proper interpretation of pressure and rate transients of well testing data. Though, there are times that even with an in-depth pressure transient analysis, a unique solution on well, boundary and especially the reservoir types cannot be obtained and makes it difficult or even impossible to extract correct information. In this study deep learning (DL) is used to tackle this problem by differentiate possible reservoir models and select the most appropriate model based on pressure derivative response. Accuracy of the classification model on real field data with known models is also explored.

Reservoir models can be identified by measuring the downhole pressure data and analyzing the changes in trends in pressure curves and especially pressure derivative curves. In this study, different DL algorithms are used to identify the basic characteristics of pressure derivative curves to determine reservoir model. Several possible well/reservoir/boundary types are considered to select the best model that can be used for well/reservoir/boundary property estimation. Before feeding the networks, training data curves would be shrunk in size using wavelet transform (WT) which is able to sustain the pressure derivative features in a much-compressed form to accelerate algorithm training and testing.

The technique used in this work is a time-efficient process that learns important signatures of pressure derivative curves to classify reservoir models. Unlike the conventional well testing methods in which models are determined from the visual inspection of the pressure and pressure derivative plots, the technique used in this study was trained with a dataset consists of hundreds of reservoir models generated by solving diffusivity equation under different well, reservoir, and boundary conditions. The procedure was applied to multiple field examples with known reservoir model and reservoir properties and proved the consistency and flexibility of the methodology for true reservoir model selection. DL-based models also shown to be very handy with excellent computational efficiency especially when dealing with the complex patterns on the pressure derivative curves. The study showed that the method has great capability to classify pressure derivative and can also tolerate noise when applied on real pressure data.

Large dataset used in this study can increase the comprehensiveness of the training and test data sets. The big advantage of the DL-based approach was the improvement in the pattern recognition of the pressure derivative curves without the need of any feature handcrafting or any prior knowledge of well, reservoir, and boundary types. ML proved to be a reliable, fast, and accurate technique that can significantly improve the process of well, reservoir, and boundary type detection based on pressure derivative curves.
Multi-Dimensional Approach to Inter-Well Connectivity Analysis
Authors: Wong Lee Jean , Amini Hamed , MacBeth Colin
Published: June 2020

Carbonates are known to be heterogeneous. In this paper, we will focus on characterising inter-well connectivity by applying a multi-dimensional approach to production data analysis along with integration of inter-disciplinary data for a Brazillian carbonate reservoir. Results of the analysis are used for interpretation of a noisy 4D seismic data to locate sweet spots.

This unique integrated approach characterizes inter-well connectivity from four perspectives: (1) determine reservoir quality (2) identify source of water production (3) tracking of injected fluid's flow path (4) verify impact on 4D seismic response. We will show how the quality of a carbonate reservoir and its aquifer strength can be verified with well logs, pressure depletion rates and production behaviour. The use of sensitivity analysis in mechanistic models will also be shared to analyse the impact of heterogeneity on production behaviour. Using Chan's (1995) water-oil-ratio diagnostic plots, the source of water production will be identified as well. Explanation of analysis of well chronology, bubble maps of water cut will also be provided for tracking of injected fluid flow paths. Finally, interaction of production parameters between well-pairs and resistance modelling will be used to evaluate inter-well connectivity, and verified with 4D seismic data.

Findings from all the analysis in the integrated approach are summarized into an inter-well connectivity metric, which is used as a reference for production and seismic history matching and interpretation of the noisy 4D seismic data. The integrated data analysis shows that the sweet spot corresponds with softening on the 4D seismic map, un-swept by injectors as it is located on a structural high southeast of the reservoir. This paper offers a comprehensive analysis to characterize important reservoir characteristics (such as thief zones and tight streaks). It will also emphasize ways to integrate inter-disciplinary data, and showcase various visualization perspectives to fortify and enhance the importance of data integration.
Influence of Total Organic Content on CO2–Water– Sandstone Wettability and CO2 Geo-Storage Capacity
Authors: Fauziah Cut Aja , Al-Khdheeawi Emad A. , Iglauer Stefan , Barifcani Ahmed
Published: June 2020

Wettability of CO2–water– reservoir rock system is a key factor to determine fluid dynamic and storage capacities in CO2 geo-storage process. Despite the past researches on this matter, the parameters that influence the CO2–water–rock wettability variation are still not fully understood. One of these parameters is rock-total organic content (TOC). Thus, here, we investigated the effect of TOC on the CO2–water–sandstone wettability and the implication for CO2 geo-storage at relevant reservoir conditions. The used sandstone samples were retrieved from the South West Hub CO2 capture and storage project (GSWA Harvey 1) in Western Australia. Here, we measured the contact angles for a range of sandstone TOC (i.e. 0.01 wt %, 0.015 wt %, 0.017 wt %, and 0.019 wt % TOC) at various pressures (5 MPa, 10 MPa, 15 MPa, and 20 MPa) and at an isothermal reservoir temperature (334 K). The results indicate that both of the advancing (θa) and receding (θr) contact angles for all tested sandstones increased with an increase in pressure TOC, implying the system turned to be more CO2-wet (e.g. the advancing contact angle increased from 92° to 118° when the sandstone TOC increased from 0.01 wt % to 0.019 wt %, at 20 MPa and reservoir temperature). Furthermore, our results indicate that the sandstone contact angle increase with pressure for all tested TOC values. Thus, we conclude that minute increasing in TOC can increase the sandstone contact angle and thus decrease the residual trapping capacities. Our results suggest that the contact angle measurement of rock retrieved from reservoirs should be treated properly for TOC to have an accurate estimation for the CO2 storage capacity.
Special Connections for Representing Multiscale Heterogeneities in Reservoir Simulation
Authors: Correia Manuel , Schiozer Denis
Published: June 2020

The significant quantities of oil contained in fractured karst reservoirs in Brazilian pre-salt fields adds new challenges to the development of upscaling procedures to reduce time on numerical simulations. This work aims to represent multiscale heterogeneities in reservoir simulators based on special connections between matrix, karst, and fracture mediums, both modeled in different grid domains within a single porosity flow model. The objective of this representation is a good balance between accuracy and simulation time. The principle combines the Embedded Discrete Karst Model (EDKM) and the Special Connection Fracture Model (SCFM), both developed by Correia et al, 2019. Therefore, this work extends the approach of special connections to integrate both karst and fracture mediums modeled in different grid domains and block scales. For a proper validation and numerical verification, we applied the methodology to a complex case study. The reference model is a refined grid model with: (1) an arrangement of large conduits (karsts), which are poorly connected, (2) a well-connected and orthogonal system of fractures and (3) a background medium (matrix). For the simulation model, the matrix grid domain has a grid block size of roughly 25 × 25 × 5 meters. The fracture domain has a grid block size of roughly 50 × 50 × 5 meters and the karst grid domain has the same block size as the reference model. Well-connected fractures are upscaled to the fracture domain by applying the Oda (1985) method. The results show a significant performance improvement regarding a dynamic matching response with the reference model, within a suitable simulation time and maintaining the dynamic resolution according to the representative elementary volume of heterogeneities, without using an unconstructed grid. Therefore, this work introduces a new method that can be applied to commercial flow simulators concerning the use of special connections to represent multiscale heterogeneities in flow simulators, without disregarding the accuracy over upscaling and an adequate simulation time.
Applications of Artificial Neural Network for Seismic Facies Classification: A Case Study from the Mid-Cretaceous Reservoir in Supergiant Oil Field
Authors: Al-Ali Ali , Stephen Karl , Shams Asghar
Published: June 2020

Facies classification is significant for characterization and evaluation of a reservoir because the distribution of facies has an important impact on reservoir modelling which is important for decision making and maximizing return. Facies classification using data from sources such as wells and outcrop cannot capture all reservoir characterization in the inter-well region and therefore as an alternative approach, seismic facies classification schemes have to be applied to reduce the uncertainties in the reservoir model. In this research, a machine learning neural network was introduced to predict the lithology required for building a full field earth model for carbonate reservoirs in Sothern Iraq.

In the present research, multilayer feed forward network (MLFN) and probabilistic neural network (PNN) were undertaken to classify facies and its distribution. The well log that was used for litho-facies classification is based on a porosity log. The spatial distribution of litho-facies was validated carefully using core data. Once successfully trained, final results show that PNN technique classified the carbonate reservoir into four facies, while the MLFN presented two facies. The final results on a blind well, show that PNN technique has the best performance on facies classification. These observations implied this reservoir consists of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.

The work and the methodology provide a significant improvement of the facies classification and revealed the capability of probabilistic neural network technique when tested against the neural network. Therefore, it proved to be very successful as developed for facies classification in carbonate rock types in the Middle East and similar heterogeneous carbonate reservoirs.
Exploration of Correlated Prospects and Possible Joint Development Solutions
Authors: Jafarizadeh Babak
Published: June 2020

Drilling exploration wells depend on the uncertainty we perceive. With some simplification, this uncertainty is about the value of producible hydrocarbons. To drill a prospect, the expected benefits from producing and selling hydrocarbons should outweigh the costs. This principle also applies to clusters of interrelated exploration targets. Here, the benefits of drilling each prospect is information about the neighbouring prospects, proving producible hydrocarbons, or both; these benefits should outweigh the aggregate costs. In practice, estimating the expected benefit could be challenging especially when multiple smaller discoveries are developed under a joint development scheme. With these interrelationships, what is the optimal drilling strategy? In addition, what is the economic value of a group of prospects? This paper discusses the effect of joint development economics on perceived uncertainty and drilling decisions. We suggest a framework for valuation of correlated prospects.
Scaling Analysis to Characterise CO2 Storage in Saline Aquifers
Authors: Ghanbari Saeed , Mackay Eric J. , Pickup Gillian E.
Published: June 2020

This study shows the application of scaling analysis in the context of CO2 storage. Scaling analysis has been used in many flooding processes to characterize the displacement in such systems. The study aims to derive the key dimensionless numbers pertinent to CO2 storage in saline aquifers. This set of dimensionless numbers may be used to characterize important storage characteristics such as injectivity, plume migration and mobility, the pressure response and the ultimate storage capacity in potential saline aquifers.

CO2 storage in a two-dimensional cross-sectional model representing part of a saline aquifer was considered. The model is assumed to be full of brine when CO2 is injected into it. The fundamental equations for the material conservation of each phase, and the transport equations were formulated and derived. All fluids and the formation were considered compressible. These fundamental equations were then converted into the dimensionless domain by applying inspectional analysis to allow the identification of the key dimensionless numbers characterizing the storage process.

The storage process in such a system can be described by twelve dimensionless numbers, each of which characterize a different aspect of the storage process. Some numbers are similar to those already observed in the context of petroleum processes while a few of them are solely relevant to the storage process. Importantly, the pressure response and the injectivity consideration of the storage process can be described by the injectivity number and the ratio of compressibilities.

A numerical model was constructed to test the sensitivity of the storage process with respect to these dimensionless numbers. Results show the same set of dimensionless numbers can describe storage performance in different systems as long as the processes in all of them are described by identical dimensionless numbers. The lateral migration of the plume and its onset arrival at the storage boundary can be described by the combination of the magnitudes of gravity numbers, effective aspect ratio number, mobility ratio between CO2 and brine and finally the ratio of CO2 and formation compressibilities relative to brine compressibility.

For a confined storage system, the storage efficiency was correlated with the magnitudes of the influencing dimensionless numbers. The derived dimensionless numbers may be used as a set of characterization parameters for describing the storage process in potential storage candidates. They can also be used effectively as a preliminary screening criteria for the purpose of site selection amongst potential storage candidates.
Development of a Multi-Solution Framework for Simultaneous Well Placement, Completion, and Control Optimization
Authors: Salehian Mohammad , Haghighat Sefat Morteza , Muradov Khafiz
Published: June 2020

Optimal field development and control aim to maximize the economic profit of oil and gas production. This, however, results in a complex optimization problem with a large number of correlated control variables at different levels (e.g. well locations, completions and controls) and a computationally expensive objective function (i.e. a simulated reservoir model). The typical limitations of the existing optimization frameworks are: (1) single-level optimization at a time (i.e. ignoring correlations among control variables at different levels); and (2) providing a single solution only whereas operational problems often add unexpected constraints likely to reduce the ‘optimal’, inflexible solution to a sub-optimal scenario.

The developed framework in this paper is based on sequential iterative optimization of control variables at different levels. An ensemble of close-to-optimum solutions is selected from each level (e.g. for well location) and transferred to the next level of optimization (e.g. to control settings), and this loop continues until no significant improvement is observed in the objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of solutions, with maximum differences in control variables but close-to-optimum objective values, at each level of optimization. The framework also considers pre-defined constraints such as the minimum well spacing, irregular reservoir boundaries, and production/injection rate limits.

The proposed framework has been tested on a benchmark case study, known as the Brugge field, to find the optimal well placement and control in two development scenarios: with conventional (surface control only) and intelligent wells (with additional zonal control using Interval Control Valves). Multiple solutions are obtained in both development scenarios, with different well locations and control settings but close-to-optimum objective values. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the higher-level optimization, highlighting the value of the here-developed multi-solution framework in exploring the search space as compared to the traditional single-solution approaches. The development scenario with intelligent completion installed at the optimal well location and optimally controlled during the production period achieved the maximum added value. Our results demonstrate the advantage of the developed multi-solution optimization framework in providing the much-needed operational flexibility to field operators.