bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel System for Large Depth-of-Investigation Pulsed Neutron Measurements and Enhanced Reservoir Saturation Evaluation
Authors: Eltaher Yahia , Schmid Gregory
Published: February 2022

Despite its value and importance to oilfield development and reservoir management, carbon/oxygen (CO) logs are commonly associated with significant challenges that are either related to the wellbore logging environment and/or the physics of the measurement. Shallow depth of investigation is considered the greatest challenge related to the nature of the pulsed-neutron (PN) measurement. It can imply a high degree of uncertainty on the measurement and consequently the calculated water saturation, affecting the true assessment of the reservoir fluids’ saturations, especially in challenging logging environments. In this paper we introduce and prove an innovative approach to increase the depth of investigation of the PN measurement.

Currently, all PN logging tools use an electric pulsed neutron generator (PNG), or "particle accelerator" or Minitron, to probe downhole formations with 14 MeV neutrons and record the returning gamma ray signal at a shallow depth of investigation (DOI), which is generally in the range of 7 inches for C/O measurement and 12 inches for sigma measurement. In this new approach, we introduce the idea of increasing DOI of the measured gamma rays through increasing the energy level of the neutrons emitted by a PNG. To prove the concept, a computer modeling and simulation study was conducted using Monte Carlo N-Particle (MCNP) for a pulsed-neutron logging tool to determine DOI for neutron energies higher than 14 MeV.

The study involved five different combinations of borehole and formation fluids. Each involved a "block" of 24 MCNP calculations. The 24 calculations inside each block represented the 24 possible combinations of 3 neutron energies (14, 20, 40 MeV), two gamma ray spectral types (inelastic, capture), and four detectors. Data simulation shows that the DOI rises substantially with energy for all tested detectors. Where the enhancement in DOI with the increase in neutron energy is more prolific in case of the inelastic measurement compared to the capture measurement. And of course the deeper the detector (further from the source) the better the DOI, although this can compromise the precision of the measurement. Yet with the recent technology advancements mainly in PNG (producing more neutron population) and GR detector technology (higher and faster count rates), this shall enhance the precision of the measurement and enable us to acquire both accurate and precise measurements at deeper detectors.

This patented, innovative approach shall significantly reduce and possibly eliminate one of the main reasons behind the uncertainty of reservoir saturation monitoring using PN logs, which is shallow depth of investigation of the measurement. Having a PNG that can produce neutrons at higher energy levels compared to current industry standard shall allow a deeper, more accurate and a representative evaluation of the reservoir.
Inversion Using Adaptive Physics-Based Neural Network: Application to Magnetotelluric Inversion
Authors: Alyousuf Taqi , Yaoguo Li
Published: February 2022

In order to develop a geophysical earth model that is consistent with the measured geophysical data, two types of inversions are commonly used: a physics-based regularized inversion and a statistical-based machine learning inversion. In nonlinear problems, deterministic regularized inversion usually necessitates a good starting model to prevent possible local minima. The neural networks inversion requires large training data sets, which makes its generalizability limited. To overcome the limitation of physics-based regularized inversion and a statistical-based machine learning inversion and combine the benefits of both one inversion scheme, we developed a new physics-based neural network (PBNN) inversion algorithm. In our PBNN inversion, we include machine learning constraints into the regularized inversion using a coupling model objective function. The coupling objective function aims to minimize the difference between the recovered model through regularized inversion and the network-predicted reference model. We update the reference model using either a fully-trained network or an adaptively-trained network. The fully trained PBNN has the ability to collect all of the connections between data and models through a pseudoinverse operator. However, for geophysical inversion applications, particularly in the exploratory setting, this approach is unlikely to become feasible. Neural networks may struggle to extract complicated correlations from data when given insufficient data observations. The technique is impractical for practical usage due to the quantity of training required. In our novel adaptively PBNN algorithm, there is no need to prepare a training data set. At each iteration, the adaptively-PBNN algorithm retrains using the recovered models from the regularized inversion and their related data. The regularized inversion's recovered resistivity models are sufficient to guide neural network predictions towards the true model. One unique advantage is that the approach’s ability to fully use all intermediate models from the regularized inversion that were commonly discarded and apply them to the network training. When applied to synthetic MT data, we show that our technique is capable of reconstructing high-resolution resistivity models.
Permeability Prediction from Clay Volume Using Artificial Intelligence
Authors: Alhamood Hussain A. , Abdulraheem Abdulazeez
Published: February 2022

The importance of permeability can be observed by the massive number of studies on its prediction as reported in literatures. Common methods of permeability prediction depend mainly on core analysis and pressure test data in the lab. However, there are difficulties associated with predicting permeability from traditional methods. This motivates the search for new methods. In this study, the prediction of reservoir permeability from clay volume by using Artificial Intelligence is proposed as an innovative solution.

One of these AI methods is Artificial Neural Networks. The ANN approach is implemented in this paper in order to predict reservoir permeability. This study used the volume of clay and porosity as input parameters. The model data was mainly obtained from well logs and validated by core data from 21CXRM Palaeozoic project in the North Sea. The training of the ANN model was completed using the Backpropagation Neural Network (BPNN). More than 25,000 data points were used in the development of the ANN model. The data was divided randomly to train and test the ANN model.

The results of this study illustrate excellent correlation between the actual permeability values and the predicted values from the ANN model, i.e., 0.9985 and 0.9991 for the training group and the testing group, respectively. The ANN successfully predicted the permeability for a wide range of reservoir permeability values at different reservoir depths. A mathematical equation was developed to demonstrate the relationship between the used input data and the output results. This proves the capability of AI techniques to estimate reservoir parameters in general.
Advances in Virtual Flow Metering Using Deep Composite Lstm-Autoencoder Network for Gas-Condensate Wells
Authors: Omeke James
Published: February 2022

In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and temperature etc.) without any recursive feedback connection between the network outputs and inputs. This disconnection has impacted their accuracy. Dynamic artificial neural network, for example, the recurrent neural networks (RNN), e.g., LSTM has shown good performance as its architecture allows for the usage of data from the past time step to predict the current time step. Forecast accuracy for RNN are limited to short period of time due to their inherent vanishing gradient issues. While majority of VFM application have been developed for oil and gas systems, little or non is applied to gas condensate system.

In this project, a sequence-to-sequence deep composite LSTM-Autoencoders was explored and used to demonstrate the ability of leveraging on its architecture to accurately predict multiphase flow rate for some wells in a gas condensate reservoir with highly dynamic multiphase flow phenomenon. A more complicated flow system was developed using a 3D compositional simulator to simulate, as close as possible, a realistic case of compositional reservoir. A single well was used to train the model and a blind test was ran on two other wells in same reservoir whose data are not part of the training set in order to predict their flow rate with accuracy.

Based on the actual vs predicted results demonstrated, especially the blind test case, the feature extraction and encoding process of the trained LSTM-autoencoder was actually learning the physics of fluid flow and accurately passing the encoded results to the two decoders with very good output (training and testing mean square error are 0.02 and 0.05 respectively).

The ability to leverage on some advanced artificial intelligence framework such as a composite LSTM-autoencoder has proven that it is possible to achieve the desired accuracy needed in data driven VFM to meet the requirement of low cost, low execution time and high accuracy.

This project has also demonstrated the ability of the data driven model to learn the complex dynamics within the temporal ordering of input sequences of production data, with an internal memory adapted to remember or use information across long input sequences, hence, yield longer and reliable forecast, unlike other networks.
Drilling Parameter Based Geomechanical Log Application: A Fracture Stage Selection Case Study in Tight Gas Sand Development Wells
Authors: Bentosa Elena , Makechnie Glenn , Rabines Jaime , Meridji Yacine
Published: February 2022

In heterogeneous tight sand formations, horizontal wells encounter intervals deposited under varying depositional environments along the lateral portion of the wellbore between landing point and total depth. Horizontal wells in this study were drilled in tight sands deposited in a marine environment where lateral depositional facies changes are common, and hydraulic fracture stimulation is necessary to achieve economic hydrocarbon extraction due to the relatively low permeability of the formation. Without geomechanical logs currently derived from wireline logging, it is not possible to optimize cluster spacing and placement. This step provides necesary information used to optimize completion design, which is crucial to the ultimate productivity of a well. Due to formation heterogeneity, expensive wireline logs must be collected in order to optimize fracture stimulation or else new methods to estimate these logs must be employed. This paper presents a technique to optimize cluster selection for hydraulic fracturing in unconventional tight gas development horizontal wells without wireline logging by leveraging Measure While Drilling (MWD) Gamma Ray logs and surface drilling parameters together with Artificial Intelegence (AI) algorythms to predict density, compressional and shear slowness logs for use in geomechanical evaluation.
Oil in Water Monitoring – Data Resolution
Authors: Oulman Spencer
Published: February 2022

Water processing is a widespread challenge for oil production. In many areas of the world, access to water is becoming scarce and cost of water processing is increasing. With aging reservoirs, larger water cuts are expected. The oil production from wells becomes more challenging as larger percentages of water are recovered from production. Added chemistry and extra mechanical work on the fluids become necessary to recover the desired oil. Further downstream of the oil separation, additional work on produced water becomes necessary to further recover oil. Later in the process as the produced water is prepared for disposal or reuse, the trace oil is monitored for recovery to further increase production gains and avoid added costs for disposal or premature fouling of reservoirs in secondary and tertiary enhanced oil recovery operations.

To control the process, oil in water concentration is monitored. The traditional method of obtaining oil in water concentrations is from manual grab samples. These samples are then analyzed predominantly by gravimetric or photometric methods in a lab. An alternate method for obtaining the process results uses online monitoring that is directly connected to the process for high frequency sampling and measurements. The use of oil in water online monitoring has been commercially available for a few decades. Online monitoring has been gaining traction as the cost of the technology becomes economically viable.

The study will focus strictly on the resolution of data to show detail missed by traditional manual grab samples. This study and results will not focus on comparisons of accuracy of measurement, maintenance, and calibration requirements of online monitoring. An onshore producer in the US studied a production process using waterflood injection. Over the 15 days of study, manual grab samples and online sampling were performed. Process changes will be highlighted across the study, showcasing the importance of resolution.
Don't Lose Your Bha - New Approach to Freeing Stuck Pipe in Obm
Authors: Sindi Rami , Rubiea Mohammed , Anioke Chidieber , Alabdulmohsen Zainab , Fathallah Mohamed
Published: February 2022

Objective

Mesophase is a spotting fluid, along with an organic acid which will serve to remove filter cake matter, equalize differential pressure, and free the pipe.

Methodology

Herein is a case history of a successful application using mesophase technology for stuck pipe incident in lateral reservoir section align with wellbore cleanouts. Stuck pipe is a major operational challenge that causes a significant amount of downtime and high costs during the operation.

Filter cake can significantly cause stuck pipe incidents and reduce the efficiency of the production rate of a well. This paper describes an engineered and cost-effective approach to overcome stuck pipe events. This approach is based on removing emulsions, dissolving acid soluble solids, dispersing non-solute and water wetting all surface.

The mesophase restoration system is a customized, and designed to remove mud damage, in-situ emulsions, oil-based mud (OBM) filter cakes in the well bores.

Results

Selection of an appropriate clean-up procedure is critical in ensuring the productivity of open hole wells, particularly horizontal wells. The mesophase system was pumped ahead after pumping the spacer train. The system contains a proprietary surfactant blend, organic acid, corrosion inhibitor, and brine that are all spotted into the stuck spot in the hole and allowed to soak. During this soaking process, the ultra-low interfacial tension properties of the breaker solution begin to diffuse, solubilizing the oil in the emulsions and dissolving the filter cake while dispersing undissolved solids and allowing the pipe to be freed.

Novelty

The success of freeing a stuck-pipe depends on two main factors – the correct diagnosis of stuck-pipe event either to be differential or mechanical stuck, and the right decision to implement the remedial treatment quickly and effectively. Applying the mesophase fluid technique saved the non- productive time (NPT). This technique is a reservoir friendly and has been successfully implemented in stuck-pipe events in oil-based drilling fluids.
Myhsse – Analytic Transformation of Health, Safety, Security and Environment HSSE in Making Prescriptive Possible Towards Zero Incident; Geared by End Users’ Pain Points
Authors: Badzri Shairizal , Lim Swee Fung , Jamil Nor Eliea Eluziea , Hassan A Hakim
Published: February 2022

HSSE digitalisation and big data analytics in Oil and Gas industry present a multitude of challenges which includes the inconsistency of work processes in data management, questionable data integrity, high operating cost in managing digital systems, lack of visibility and the mindset shift of users. These challenges have led to the digital transformation journey in PETRONAS.

Since PETRONAS embarked on the digitalisation and analytic transformation, numerous value creations have been recorded namely improved HSSE performance, compliance to internal and HSSE regulatory requirements, cost value realization and supporting the United Nations Sustainable Development Goals (UN SDGs).

The objective of this paper is to provide strategy guidance in HSSE system digitalisation and data analytics enhancement. The inclusion of digitalisation as one of the focus areas in PETRONAS HSSE Strategy and its supporting blocks in realising the bigger digitalisation agenda can be emulated and/or referenced by industry peers who face similar challenges and are keen to embark on the transformation journey.
Synergy Between High-Fidelity Dynamic Simulation and AI Surrogate for Robust Design Optimization
Authors: Macri Andrea , Genovese Simone
Published: February 2022

It is common practice in the industry to proceed with validation and optimization of any conceptual design of a process or piece of equipment to ensure that its size, configuration, and operation meet all the requirements in terms of production, safety and more. Design methods are mostly initiated by doing steady-state calculations. However, due to the dynamic nature of certain phenomena, a dynamic simulation is often required for a rigorous design.

The performance of first-principles dynamic models is limited by the high number and frequency of calculations required for an accurate representation of the process behavior. Especially with large simulations we should not expect to accelerate more than 3 or 5 times faster than real time. A transient of one minute will still take 15-20 seconds to be completed. Running thousands of dynamic scenarios to identify the optimal design using only first-principles dynamic models would require a considerable amount of time. So, when time is a constraint, and time is always a constraint in a project, the optimization effort will inevitably be considered completed after a reduced number of runs resulting in a design which satisfies the process requirements but does not guarantee the lowest possible capital expenditures (CAPEX) and operating expenses (OPEX).

Artificial intelligence surrogate models can compute considerably faster, but they require a good amount of data to be trained with and they are less reliable.

This paper demonstrates through a real-life example the advantages of combining in the same engineering workflow a rigorous dynamic representation of the process behavior and AI surrogates to identify the best robust and reliable design for an industrial process.

The proposed workflow starts with the initial investigation of the design space using a dynamic simulation of the process. The initial data extracted from the first-principles model is then used to train an AI surrogate model that can quickly complete the great majority of the scenarios. Only the final robust optimization of the most promising nominal design cases is then performed using again the first-principles simulation.

The design optimization was completed in a fraction of the time that would have been needed by using only first-principles models.
Efficient and Cost-Effective Casing Leak Detection Methodology on Offshore Oil Fields
Authors: Al-Hamdan Mohammad , Al-Shammari Ahmad
Published: February 2022

Maintaining well integrity is one of the critical factors in the oil and gas industry. It requires close monitoring during the life cycle of the well, especially in offshore fields, to maximize the well life cycle and avoid catastrophic failure.

Casing and bonded cement are major components of well completion that secure oil and gas production paths from different overburden formations. However, casing leaks are a common issue that might lead to serious losses in oil and gas production, locked reserves due to formation damage, personnel injuries, and severe environmental impact. Thus, it is important to detect casing leaks in the early stages to prevent such losses, which might induce a high cost of workover operations and well suspension or abandonment.

Casing leaks occur due to corrosive fluids in the formations and long-term exposure to corrosive gases. During drilling, cement is set between the casing and the different formations or between the two casings for isolation and well protection. A bad cementing job leads to the failure of well barriers, cracks, and microchannels that allow corrosive fluids to migrate, which slowly corrodes casing and tubing over time. The flow direction determines the type of casing leak, either dumping (downward) or taking (upward). However, both types have a dangerous effect depending on leak severity.

The identification of casing leaks, their severity, depth, and flow direction are a crucial task. Well diagnostic using the latest advanced leak detection tools is important in deciding the most appropriate remedial actions.

This paper discusses a case study in a well of the Al-Khafji offshore field, where different methodologies were utilized to identify casing leaks. It involves the use of pressure/temperature profiles through downhole memory gauges, annuli pressure surveys, well-testing operations, geochemical analysis, and conventional production logs. The approach used succeeded in identifying casing leaks, flow direction, and the accurate determination of the leak location/depth.
Effect of Different Nanoparticles Silver, Iron Oxide and Titanium Oxide to Control Corrosion by Desulfovibrio Sp.Isolated from Oil Fields
Authors: Alsakkaf Sarah , Al-Dosary Sahar , El-Komy Hesham , Al. Ahmadi Mona
Published: February 2022

Microbiologically influenced corrosion (MIC), or biocorrosion is a corrosion of metals in environment containing microorganism. The microorganisms can initiate corrosion or accelerate by their metabolic activities. Recently, MIC problems common in oil recovery which depend on seawater injection. Many of researchers have been investigated that Sulfate-reducing bacteria (SRB) is the major groups of microorganisms that cause corrosion. Pitting corrosion and sulfide generation in oil and gas fields are correlated to presence of SRB. Last decade, nanotechnology is alternative biocide of chlorine-free. Metal nanoparticles improve the antimicrobial activity of the metals. The biocidal activity of AgNPs, Fe2O3NPs and TiO2NPs against Desulfovibrio sp. (St.7) at 1 ppm, 50 ppm and 100 ppm concentrations as estimated by cell growth and H2S production was examined. AgNPs and Fe2O3NPs had no significant effect at a low concentration, 1 ppm, on both bacterial growth and sulfide production. Whereas, increasing NPs concentration to 50 ppm and 100 ppm led to a complete inhibition of both growth and sulfide production. TiO2NPs gave complete inhibitory effect on both growth and H2S production by Desulfovibrio sp. (St.7) at all the tested concentrations (1ppm, 50ppm, 100ppm). The study supports the use of such nanoparticles as a green biocide and safe method to control corrosion problem caused by sulphate reducing bacteria.
Laboratory Evaluation Comparison Study Between the Performance of Fatty Acid Solid Lubricant and Liquid Lubricant
Authors: Bukhawwah Munirah , Alrammah Sarah
Published: February 2022

High torque and friction factors are major challenges while drilling. Providing lubrication helps in reducing torque and drag and increasing rate of penetration (ROP) in water-based fluids and produced water. The lubricants are inert hence, they do not react with other fluid additives or cuttings and will not affect fluid rheology. All Lubricants in the oil and gas industry are in liquid form and usually used to reduce torque and decrease coefficients of friction, they came in different chemical compositions a toxic lubricant mineral oil and non-toxic vegetable lubricants, and they have many papers talk about how they function as lubricants, but with a new generation of solids lubricants, it will be changing the whole industry.

Powdered encapsulated lubricant additive comprises a liquid lubricant blended with an inert solid substrate. The solid lubricant additive compositions thus, obtained are advantageously employed in drilling fluids.

Fatty acid solid lubricant is one of the aforementioned powdered lubricants. It is a dry form encapsulated lubricant composed of micronized capsules containing oil that remains held until sufficient operational pressure, friction, or shear break the encapsulation to release the oil on demand.

In this paper, we present a testing plan, lab results of fatty acid solid lubricant, compare and contrast results with liquid lubricants that had the same component group in three types of salts (brine) to see the range of efficiency between solid and liquid forms.
Taking a New Approach Towards Chelating Agents for Scale Removal
Authors: Solling Theis , Mahmoud Mohamed , Pittelkow Michael
Published: February 2022

Scale presents a significant challenge in any context where flow is involved. In the oil and gas sector scale associated problems are notorious because of the significant volumes of brine that are involved at any stage of production. In addition, the formation brines are usually extremely concentrated when it comes to electrolytes that are prone to form scale. The real problematic scales are usually considered to be barite (BaSO4) and pyrite (FeS2, or mixed iron sulfides). The approach to remove them is usually to drive the solubility equilibrium towards solution by simply stabilizing the ions that are involved. That is usually done by coordination of the ions with a ligand that favors binding to the particular cation in question. For pyrite the challenge is then to identify a ligand that strongly coordinates Fe2+ whereas in the case of barite the focus will be on Ba2+. This is the classical approach to mostly target the cation and it has mostly been dealt with in the form of various chelating agents. We show results firstly, where newly designed cation binders provide a 20 % higher dissolving power than the second best and where binding pockets are designed for anions. This new binding mode will enable the design of a structure that simultaneously binds cations and anions and this is where we are heading with the current results. The efficiency of chelating agents is significantly dependent on pH this topic will be touched upon in a computational study here the calculated structure and the associated calculated binding energy will be discussed and related to dissolving power. The result show that ab initio calculations are nice supplements to experimental endeavors.
Cost Effective Solution for Replacing Amine Columns
Authors: Al Awadhi Ibrahim , Sharma Ashok , Al Zeyoudi Saleimah
Published: February 2022

Objective/Scope

Amine Regenerator column is in operation since 1980 and severe internal corrosion / erosion observed since 2000 and subsequent local shell course replacement in 2015 and 2017 were done. Therefore, to avoid frequent maintenance and repair cost and to ensure long-term measure of integrity and availability of the equipment, it was concluded to replace the existing CS column with enhanced metallurgy

Methods, Procedures, Process

The convectional approach of replacing the full column with CS +SS 3mm clad material which typically take around 12 month schedule for fabrication activities in dedicated shutdown in 2021. In addition, this replacement was planned to include replacement of full internals of amine column, existing foundation, pipe support and platform. Moreover, the execution strategy planned to be through FEED/EPC. However, looking for enhanced metallurgy and studying the local market stock of raw material and manufacturer have a big role to carry out the replacement job in planned shutdown in 2020 and within planned budget.

Results, Observations & Conclusions

The solution in going with solid SS vessel instead of clad will not only improve integrity but also will lead to enhanced schedule into 6.5 months and it is the most commercially attractive option. Moreover, going with solid SS material will reduce the fabrication time and avoid the issues associated with cladded materials and the requirements of the NDT due to welding job. In addition, it was planned to replace partially the column internals after reviewing the inspection history instead of replacing them fully. Further, the column and column internals procurement were accomplish with in in-house resources. The installation job of column, column internals, and foundation carried out within available planed June 2020 shutdown.

The development of execution strategy/in-house engineering led to avoid of 35-days unit 2021 shutdown. Also, achieving direct cost saving of USD 1 million in procurement of Column and Column Internal from the planned budget.

Further, the availability of SS solid material in UAE ex-stock and local manufacturer, were considered in minimizing the transportation and delivery schedule.

Novel/Additive Information

The column replacement was the solution to tackle the repetitive corrosion/ thickness loss failures in the vessel. The way forward of replacing full column with SS solid material will improve integrity, enhanced delivery schedule, commercially it is an attractive option and has direct support to ADNOC ICV strategy. Hence, this job will avoid the dedicated 35 days in 2021 shutdown and accordingly the revenues losses of OPCOs shutdown
Preparation of a Synthetic Geopolymer Cement Utilizing Saudi Arabian Volcanic Ash for a Sustainable Development: Method, Preparation and Applications
Authors: Alanqari Khawlah , Al-Yami Abdullah , Wagle Vikrant
Published: February 2022

The global production of cement is the third largest source of CO2 emissions into the environment, which comes from the decomposition of carbonates. Geopolymer cements can reduce CO2 emissions by 64-80% compared to conventional Ordinary Portland Cement (OPC). In other words, OPC produces around 900 kg of CO2 for every 1000 kg of cement production. Whereas, geo-polymer cements emits zero CO2 emissions while production. In addition, Geo-polymer cements can be prepared utilizing waste materials such as fly ash. In Saudi Arabia, we have enough volcanic ash to sustain the development of this ecofriendly cement. Moreover, geopolymer cements have demonstrated promising mechanical properties in comparison to OPCs. We successfully developed a novel geopolymer cement utilizing a Saudi Arabian volcanic ash for primary cementing applications for the Oil and Gas industry that can replace OPCs completely. This novel cement was prepared by activating the volcanic ash particles with an alkali solution to undergo a geopolymerization reaction. The objective of this paper is to prepare a geopolymer cement with excellent strength and to discuss the synthesis of this cement based on the chemical composition of the volcanic ash (VA). Also, to investigate the effect of the type and concertation of the activating solution on the final geopolymer mechanical properties as well as to detail lab testing.

The volcanic ash particles were activated by a mixture of NaOH, sodium silicate and water to develop a geopolymer cement. In this study, we prepared four different activating solutions by varying the alkali concertation based on the VA chemical composition. The effect of these variations on the setting time and compressive strength of the final geopolymer VA-based cement were investigated. This was done to develop a cement with excellent strength and controlled setting to assure a right cement placement. In addition, the chemical conditions were evaluated to simulate a variety of downhole conditions to prove the effectiveness of this novel geopolymer composition as a cement for primary cementing applications.

The lab testing includes thickening time measurement, compressive strength, and a chemical analysis of the volcanic ash. Test results indicate that the resulting compressive strength and thickening time are strongly affected by the alkali concentration and the addition amounts of the activating solution. Based on this study, we were able to prepare a geopolymer cement with good rheology, setting time and compressive strength for primary cementing application.
Research and Practice on Eor of Three Dimensional Well Pattern in the Thin Interbedded Oil Reservoir in the Middle and Deep Layers of the Sea
Authors: Zhang Zhanhua , Cai Hui , Ge Lizhen , Yang Xiaoyan , Liu Xue
Published: February 2022

In order to solve the problems of thin interbedded deposition, strong heterogeneity and large interlayer interference in early development of offshore K oilfield, this paper proposes a research idea of three-dimensional adjustment of injection production well pattern based on multi-layer flow field control in early development.

Through the combination of reservoir numerical simulation method, reservoir engineering and field test, the expression formula of sweep efficiency of water drive area in different stages of injection production unit is established in plane, the injection production well pattern optimization technology based on plane heterogeneity is formed; The quantitative expression formula of multi factor inter layer interference coefficient is put forward vertically. Combining with Lorentz curve, the evaluation template and technical limit of early subdivision layer system of thin interbedded reservoir are established, and the early subdivision layer system technology based on the dynamic interference representation between layers is formed.

By analyzing the seepage characteristics of thin interbedded multi-layer sandstone reservoir and combining with the field practice, the plane well layout model of offshore medium deep thin interbedded reservoir is established, Based on the quantitative characterization of interlayer interference, the technical limits of early subdivision of thin interbedded reservoirs are as follows: the permeability gradient is controlled within 3, the longitudinal span is controlled within 200m, the number of sand control sections is controlled within 4, and the water cut is less than 40%. The horizontal and vertical combination can effectively reduce the interlayer interference and start the poor reservoir area, forming a three-dimensional well pattern deployment technology suitable for the early development of offshore K oilfield. It can effectively improve the plane injection production relationship, increase the sweep range of water flooding as a whole, and improve the development level of oilfield. It can effectively reduce the more serious interlayer interference in the middle and later stage of water injection development of formation, improve the water absorption capacity of thin interbeds, and form balanced displacement and efficient development. The recovery was increased by 4.7%. The field test results show that the technology is effective.

This paper is the first time to form a three-dimensional well pattern deployment technology suitable for the early development of offshore K oilfield, which provides effective development experience for the efficient development of offshore medium and deep thin interbedded reservoirs.
The Classification and Detection of Malicious URLs Using a Machine Learning Based Approach
Authors: Sendi Razan
Published: February 2022

The world wide web has become the most cardinal and salient source of information, for which in today's technological era, web pages are incessantly being utilized to broadcast pernicious activity over the web. Hence, the perpetual technological advancements have enthralled cybercriminals to easily exploit the web environment by embedding malicious code on these web pages which permits oblivious and innocent users into becoming victims by visiting these harmful pages with a click of a button. Such detrimental and malicious web pages are created by cyber-attackers to promote and host viruses and exploit frauds, attacks, and scams.

Consequently, within an organization, emails containing Uniform Resource Locators (URLs) cause a considerable amount of risk towards an organization as these links become instrumental in terms of giving systematic control to the cyber-attackers. There is a critical need of filtering employees’ incoming emails in accordance with the maliciousness of the URL to protect the web environment users from such threats. Accordingly, the premier objective of this proposed paper is to constructively classify and detect malicious URLs while utilizing a machine learning based approach. This will lead towards sufficiently decreasing the number of URL-based attacks that may target organizations via email, protecting the users of the web from phishing, scam, defacement, and malware web attacks. Hence, the conceptualization behind the proposed AI framework is to construct a multi-classification model to effectively thwart and protect organizations from detrimental and malicious web-based attacks by classifying a URL as either a defacement, benign, malware, spam, or a phishing-based URL. The general framework for the proposed malicious URL detection system is illustratively displayed in Figure 1.
SmartPoint Seismic Data Acquisition System
Authors: Song Zhixiang , Lin Qingfu
Published: February 2022

Seismic acquisition system is the most essential equipment in seismic exploration for oil and gas. Quality, efficiency, and accuracy of seismic data acquisition play the most important role in oil and gas exploration. True wireless seismic exploration is of great significance in "cost decreasing and benefit increasing" in oil and gas industrial. Seismometer and its supporting software are core equipment in seismic acquisition.

With build-in features of eMBB, uRLLC and mMTC, 5G provides a good opportunity in developing wireless real-time seismic acquisition system. This paper is mainly about R&D progress of SmartPoint Seismic Data Acquisition System (SmartPoint system for short). Based on 5G, SmartPoint system consists of seismographs, software system, 5G networking system and auxiliary system.

SmartPoint system takes good use of industry leading technology such as 5G, AI, Big Data and Cloud Computing to solve the difficulties of high economic and labor cost in seismic acquisition. Field test of SmartPoint system has shown some very promising results. From the current test result, we are confident to deduce that with the help of advanced communication and information technology, SmartPoint system will make seismic data acquisition much more efficient, economic, and less costly.
Crise convulsiva febril em crianças: uma revisão narrativa
Authors: Fonseca André Luís Basso , Benavides Valeriane Maia Siravegna
Published: February 2022

Objetivo: Descrever e atualizar sobre as Convulsões Febris, formas de diagnóstico e tratamento em crianças. Revisão bibliográfica: As convulsões febris são convulsões presentes em um momento febril (temperatura acima de 38°C) em crianças, cujo foco febril não seja infecções do sistema nervoso central, meningites e alterações metabólicas. Em quase todos os casos, as crises febris simples têm um prognóstico benigno e não requerem uma investigação diagnóstica extensa. Convulsões febris complexas requerem uma avaliação clínica mais detalhada e exames adicionais podem ser indicados devido a um maior risco de causas detectáveis e um risco ligeiramente aumentado de desenvolver epilepsia posteriormente. Considerações finais: As CF são manifestações epilépticas benignas na infância que ocorrem entre os 6 meses e os 5 anos de idade e afetam cerca de 2–5% das crianças. As convulsões febris geralmente não têm efeitos adversos significativos no desenvolvimento cognitivo e motor. A profilaxia anticonvulsivante não é recomendada de rotina para crises simples e complexas, pois seus efeitos colaterais superam os benefícios dessa prática.
A case of spinal melorheostosis
Authors: Leong Alban Wei Shern , Langdon James , Malhotra Vivek , Mandalia Uday
Published: February 2022

Melorheostosis is a rare bone dysplasia of unknown etiology with an incidence of 0.9 cases per million. It typically affects the upper or lower limbs and can cause severe deformity and functional impairment. Diagnosis is radiological and is often described as a “flowing candle wax” appearance on the radiograph. Treatment is individualized depending on the site and severity of symptoms. We report a rare case of spinal melorheostosis. We demonstrate the imaging features of melorheostosis on CT and MRI. We discuss the classification, genetics, and management of this condition.
Flexibility or Toughness ? – The Design of Thermoset Toughening Agents
Authors: Mülhaupt Rolf
Published: February 2022

Functional toughening agents, incorporated as separate microphases into rigid highly crosslinked polymeric matrices, are the key component of high-performance structural materials which exhibit high impact and fatigue resistance without sacrificing high stiffness, dimensional stability,
strength, and heat distortion temperature. The chemistry and the basic structure/property relationships of various polymeric flexibilizers and toughening agents applied in the aerospace, aircraft, and automotive industries are reviewed. Special emphasis is given to the role of polymer compatibility
in the design of functional liquid rubbers, reactive thermoplastic oligomers, and functional core/shell microparticles. Some of the major synthetic routes to advanced structural materials based on thermoset resin chemistry and functional supermolecular structures are outlined.
The Structure of Poly(1-oxo-2-phenyltrimethylene): A Crystalline Alternating Styrene-Carbon Monoxide Copolymer
Authors: Corradini Paolo , De Rosa Claudio , Panunzi Achille , Petrucci Giorgio , Pinot Piero
Published: February 2022

From the alternating copolymerization of carbon monoxide and styrene with a soluble palladium containing catalytic system a product fraction insoluble in boiling 2-butanone was isolated which is highly crystalline. The polymer has been identified mainly by 1H- and 13C-NMR
spectroscopy and by X-ray diffraction as syndiotactic poly(1-oxo-2-phenyltrimethylene). This polymer completely epimerizes to atactic poly(1-oxo-2-phenyltrimethylene) in the presence of sodium 2-chlorophenolate.
Ternary Complexes in Solution: Intramolecular Stack Formation in Mixed Ligand Complexes Containing CuII, 2,2' - Bipyridyl, and the 5'-Monophosphate of Inosine (IMP), Guanosine (GMP) o
Authors: Massoud Salah S. , Sigel Helmut
Published: February 2022

Stability constants of mixed ligand Cu(bpy)(NMP) complexes (bpy = 2,2'-bipyridyl; NMp2e = 5'_IMP28, 5'-GMp2e, or 5'_AMP28) were determined by potentiometric pH titrations in aqueous solution at 1=O.lM (NaNO) and 25 C. These ternary Cu(bpy)(NMP) complexes are more stable than the corresponding Cu(bpy)(R-MP) complexes, where R_Mp28 represents a phosphate monoester with a group R that is unable to participate in any kind of interaction within the complexes as, e.g., D-ribose 5'-monophosphate. This increased stability is attributed, in agreement with previous results, to intramolecular stack formation within the Cu(bpy)(NMP) complexes between the purine residue of the NMPs and the aromatic rings of bpy. The formation degree of the Cu(bpy)(NMP) species with the intramolecular stacks increases in the series, IMP2? (72 ± 5%) < AMP2? (81 ± 4%) ? GMP2? (85 ± 3%). This order is different from that previously observed for the self-stacking tendency of nucleic base residues.
Herstellung von [(CH₃)₃Si]2NSexN[Si(CH₃)₃]₂ (x = 1,2) und neuen Se-S-N-Heterocyclen
Authors: Haas Alois , Kasprowski Jörg
Published: February 2022

Reaction of Se(NSO)₂ with TiCl₄ yields the four-membered ring SeN₂S (1) which was isolated as the adduct SeSN₂ · TiCI₄ (2). This adduct forms with AsF₅ (molar ratio 1:1) in SO₂ the ionic compound [SeS₃N₅]⊕[AsF₆]⊖ showing a cage structure for the cation (3). The covalent isomer (4b) of the ionic [ClSe₂N₂S]?Cl₂ (4a) is obtained from Se(NSO)₂ and POCl₃. Lithium bis(trimethylsilyl)amid reacted with Se₂Cl₂ (molar ratio 2:1) to give the bis(disilylamido)-derivatives of Sex, x = 1,2. The monoselenium derivative (5a) forms with SeCl₄in good yields explosive Se₄N₄ and with SCl₂ a salt {SeS₂N₂Cl}₂ (6) of unknown structure.
DETERMINATION OF THE THICKNESS OF THE LOAD BEARING METAL PANELS ON THE POST-BUCKLING BEHAVIOR UNDER SHEAR LOADING TAKING INTO ACCOUNT THE RIGID SUPPORT ON THE LONG SIDES AND THE INITIAL DEFLECTION
Authors: МИТРОФАНОВ О.В.
Published: February 2022

На основе решения геометрически нелинейной задачи о закритическом поведении металлической панели предложена методика определения оптимальной толщины панели, которая нагружена сдвиговыми потоками. Решение учитывает жесткое опирание по длинным сторонам и влияние возможной начальной погиби.

Based on the solution of a geometrically nonlinear problem of the post-buckling behavior of a metal panel, a method for determining the optimal thickness of a panel that is loaded with shear fl ows is proposed. The solution takes into account the rigid support on the long sides and the impact of the initial defl ection.
FEATURES OF FLOWERING VIOLACEAE VARIETIES IN THE NORTH CAUCASUS
Authors: Тихонова И.Н. , Лега С.Н.
Published: February 2022

В статье рассматривается вопрос об особенности биологии цветения некоторых видов рода Viola L. на Северном Кавказе, в системе размножения фиалок характерно как вегетативное, так и семенное размножение. Особый интерес представляет семенное размножение в связи с наличием двух типов цветков – хазмогамных и клейстогамных. Авторами в течение нескольких лет проводились фенологические, онтогенетические, сравнительно-морфологические, экологические исследования семенного размножения, осуществлялись репродуктивные эксперименты в климатической камере с контролем продолжительности фотопериода. В результате было зарегистрировано взаимосвязь характера цветения фиалок с экологическими условиями произрастания вида, которые регулируются уровнем освещенности, изменением уровня атмосферной влажности и температурой воздуха. Для северокавказских фиалок установленно двоякое отношение к продолжительности фотопериода: нейтральное при развитии клейстогамных цветков и активное для образования хазмогамных цветков, на тип цветения влияет возраст растения.

The article discusses the features of the flowering biology of some species of the genus Viola L. in the North Caucasus; both vegetative and seed propagation are characteristic of the violet propagation system. Of particular interest is seed propagation in connection with the presence of two types of flowers – chasmogamous (open) and cleistogamous (closed). For several years, the authors carried out phenological, ontogenetic, comparative morphological, ecological studies of seed propagation, carried out reproductive experiments in a climatic chamber with control of the duration of the photoperiod. As a result, the relationship between the nature of the blooming of violets and the environmental conditions for the growth of the species, which are regulated by the level of illumination, a change in the level of atmospheric humidity and air temperature, was recorded. For North Caucasian violets, a two-pronged relationship to the duration of the photoperiod is established: neutral during the development of clematogamous flowers and active for the formation of chasmogamous flowers, the age of the plant affects the type of flowering.