bluuebunny/crossref_metadata_2025 huggingface dataset:
New Method to Simulate Digital Petrophysical Properties in Heterogeneous Carbonates Using Multiscale Micro Computed Tomography Imaging and Customized Laboratory Experiments
Authors: Drexler Santiago , Bastos Alves Rafael , Silos Vitor , Ferreira De Siqueira Milena , Toelke Jonas
Published: February 2022

Offshore Pre-salt carbonates are the most relevant assets in Brazil. Since they have only been in production for the last two decades, their petrophysical characterization is of utmost importance to build reservoir models that can effectively estimate reserves and forecast production. The rock and fluid complexity of these carbonates increase the timeline and uncertainty of special core analysis programs. In this context, innovative technologies, such as digital rock analysis, can support and strengthen the petrophysical characterization of this challenging carbonates. In this work, we propose a methodology based on petrophysical trends to improve the definition of representative digital volumes of a reworked carbonate sample from the Pre-salt. In addition, digital static and dynamic properties were simulated at the pore scale and then upscaled for the whole plug. The porosity, permeability, formation factor and capillary pressure results showed a positive match with laboratory mercury injection capillary pressure and routine core analysis. The upscaling procedure was key to capture the dominant features at both the micro (pore) and macro (plug) scales commonly found in complex carbonate rocks. This workflow optimized the use of rock and fluid samples, reduced the timeline of the special core analysis program to 60% and supported minimizing the uncertainty of the traditional laboratory programs by improving quality control.
Combined Use of Nearfield Hydrophones and Multiple Migration for Shallow Imaging
Authors: Damianus Fritz , Poole Gordon , Khakimov Oleg , Cambois Guillaume , Mahgoub Mohamed , Tansini Alessandro
Published: February 2022

Modern acquisition approaches are moving towards ocean bottom node (OBN) designs. By acquiring hydrophone and geophone data, wavefield separation of these data provides access to up-going and down-going wavefields, and powerful techniques such as up/down deconvolution can be employed to attenuate surface related multiples and the source wavelet. In shallow water regions, however, very shallow reflections are not recorded. This paper will illustrate how we can derive an image of the shallow subsurface jointly using near field hydrophone (NFH) data and multiple imaging.

Traditionally used for source firing QC, NFH data are gaining popularity for shallow imaging. Typically recorded with a sample interval of 1 ms or less, NFH recordings are higher frequency than OBN data, which are normally limited to 250 Hz. In very shallow water regions, NFHs mounted on the inactive source array may only record beyond-critical arrivals which have limited use for shallow imaging. In this paper, we will demonstrate the use of NFH recordings mounted on the firing array to overcome this problem. However, NFH data on the firing array often suffer difficulties when recovering low frequency reflections due to the extremely strong direct arrival. In addition, processes such as source deghosting and demultiple may be challenging due to the single-fold nature of the data.

Another method that is gaining popularity for shallow imaging is multiple migration. Compared to imaging with primaries, multiple imaging provides a larger illumination area with smaller incident angles. We propose to combine the low frequencies from multiple imaging with the high frequencies from NFH imaging to provide a high-resolution image of the shallow section.

To illustrate this, we use a shallow water OBN dataset acquired offshore Abu Dhabi in water depths of 3 m to 50 m, where complementary information provided by NFH imaging and multiple migration, and the combination of these images, has resulted in a broadband shallow subsurface section with improved spatial continuity and reduced sidelobes.
Artificial Intelligence and Machine Learning Used as an Enabler for Dynamic Risk Management
Authors: Hutchins Robin , Holbrough David , Jenkins Mark , Saini Prem
Published: February 2022

Applying big data, data science, business process automation (BPA) and domain expertise to operational and project risk in the upstream O&G space, will create a paradigm when applied to wellbore construction. A scalable, dynamic risk ecosystem enables seamless integration of risk into all aspects of the well construction process, a cornerstone of this approach is interoperability at a system level.

A lot of risk management is subjective, risk registers and mitigations are generated by workshopping with SME's. Risk scoring is often performed in a similar manner. The financial sector now applies data science techniques, for example in the fields of auditing and compliance. The aim of this paper is to discuss how these new techniques are integrated into a well-established existing risk management processes.

The starting point for digitizing the process is data. A ‘Risk Ecosystem’ was developed with a risk engine at its core linked to curated input data and providing outputs through dedicated GUI's and direct links into offline planning and real-time operational software. The risk management process has become dynamic and integrated through the value chain. A major change is the integration with the real-time decision making process.

Risk assessments combine analysis by hazard, risk, probability and barriers. When applying risk dynamically to well construction operations the barriers can be data, analysis, procedural and/or physical. The interplay of these barriers is the mitigation.

In the risk engine barriers can be managed by the probability of effectiveness and the uncertainty of the input and output data. Artificial intelligence (AI) and machine learning (ML) can now enable risk and mitigation identification, supporting probability and uncertainty to be analysed in real-time to provide dynamic solutions.

Data connectivity enables dynamic risk management where the risk updates when new data is available or an analysis is improved. The probability and uncertainty of analysis supports risk ranking. Dynamic risk management in planning leads through to risk management during operations, as risk changes and updates the source data can be revisited efficiently as it is connected through hazard, risk or mitigation. AI is triggered, by a change in the data, and the improved risk information is distributed through a revised mitigation scenario. The integration of ML recommends mitigations trained to global experience and continues to improve as SME's qualify mitigations.

Exploration of the interaction of data management, AI, ML and personnel for risk management is important, achieving the correct balance reduces risk, making risk management more efficient.

We propose that the improvement in data science for hazard, risk and mitigation identification supports the drive towards a risk management standard of a minimum risk analysis for the industry. In time this will lead to operational efficiency and consistency of performance.
An Efficient Approach for Automatic Well-Testing Interpretation Based on Surrogate Model and Deep Reinforcement Learning
Authors: Dong Peng , Liao Xinwei , Chen Zhiming , Zhao Hongyan
Published: February 2022

The artificial well-testing interpretation is a good tool for parameter evaluations, performance predictions, and strategy designs. However, non-unique solutions and computational inefficiencies are obstacles to practical interpretation, especially when artificial fractures are considered. Under this situation, a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on vertically fractured well-testing interpretation.

Based on deep deterministic policy gradient (DDPG) algorithm, the proposed DRL approach is successfully applied to automatic matching of well test curves. In addition, to improve the training efficiency, a surrogate model of the vertically fractured well test model based on LSTM neural network was established. Through episodic training, the agent finally converged to an optimal curve matching policy on vertically fractured well-testing model through interaction with the surrogate model.

The results show that the average relative error of the curve parameter interpretation is less than 6%. Additionally, the results from the case studies show that the proposed DRL approach has a high calculation speed, and the average computing time was 0.44 seconds. The proposed DRL approach also has high accuracy in field cases, and the average relative error was 7.15%, which show the reliability of the proposed DRL method.
A Dynamic Workflow of Well Health Issue Prediction – Gas Leakage
Authors: Haq Bashirul , Al Shehri Dhafer , Hassan Zahid , Ahmed Iftekhar , Isah Abubakar , Afagwul Clement , Sofian Mohamed , Mohammed Nasiru S.
Published: February 2022

Due to the corrosive nature of sour natural gas, productions through tubing and casing are susceptible to corrosion, resulting in gas leaking. This issue causes massive production loss. Subsequently, well health monitoring and early detection of existing and developing gas leakage are essential for profitable gas production. Dynamic material balance technique estimates gas initial in place (GIIP) using wellhead or bottom hole pressures and gas rates data at flowing conditions during the production. This method is commonly utilized for estimating GIIP but does not apply to predict gas leakage issues. This work aims to add a leak factor term by modifying the dynamic material balance equation and build a mathematical platform to detect and validate the problem by applying the modified equation.

The new platform produces expected well behaviour using the revised equation and curve-fitting tool in MATLAB and then compares with the actual behaviour and detect gas leakage. The deviation from the expected and actual behaviour determines the issue. Each component of the model is validated using know values. After that, the total system is tested with known leakage data. Finally, the platform is applied in the active gas well, and the leak detection of the platform is reasonably well. The new workflow can notify the production engineers so they can take corrective measures about the issue.
Automatic Calibration of Complex 3D Discrete Fracture Networks using EDFM-AI
Authors: Liu Chuxi , Gupta Anuj , Ates Harun , Yu Wei , Li Ning , Sepehrnoori Kamy
Published: February 2022

In the realm of shale reservoir development and modelling, the problems of completion evaluations and long-term well performances are particularly important but highly challenging. Without suitable measurement metrics of completion designs and production potential, the operators have limited ability to quantify and optimize the quality of completion. Presence of natural fractures within shale reservoir adds higher level of complexity to an already challenging problem. Ability to integrate natural fractures is critical for reliable history matching and long-term production forecast. These challenges limit the accuracy of the traditional numerical methods and needs to be addressed. The objective of this study is to evaluate a novel numerical method called embedded discrete fracture model (EDFM) to non-intrusively model the contribution of hydraulic fractures and natural fractures. In this study, we tested and incorporated cutting edge artificial intelligence with our EDFM method (EDFM-AI) to perform automatic history matching. These improved numerical methods are utilized to handle the above challenges in a systematic manner. The new EDFM-AI workflow is applied to a naturally fractured shale reservoir. Key untertainty features for hydraulic fractures such as half-length, height, water saturation, conductivity, and cluster efficiency are defined, with various confidence intervals. Highly uncertain parameters for natural fractures, such as total number of fractures and their conductivity are also included. Through EDFM-AI workflow, it is found out that the total effective fracture flowing area is about 2.21 million cubic feet for this Eagle Ford horizontal well case study. Furthermore, the conductivity of natural fractures are 1/100 times that of hydraulic fractures, but these natural fractures can contribute 25% of the 30-year oil EUR. Finally, the visualization shows that the drainage radius increased from 200 ft at end of the history period to roughly 500 ft after 10 years of production, which differentiate the optimal well spacing at different producing time. By implementing this novel workflow, the vast uncertainties can be quantitatively analyzed. The successful application of EDFM-AI workflow demonstrates its benefits compared to other unconventional modelling workflows in the oil/gas industry.
Strategic Well Landing and Risk Mitigation in Heterogeneous Formation With Deterministic Automated Inversion and Remote Monitoring
Authors: Nguyen Viet , Vianna Armando , Steviano Karel , Zulharman Ahmad , Aristya Ramadhana , Lie Hendry
Published: February 2022

The K-W horizontal well was drilled to produce gas reserves from A sand and oil reserves from B sand. Accurate landing of the well in the target B sand is critical for the placement of the 6 1/8" lateral section within the reservoir. The main objective of reservoir navigation in the 8 ½" build section is to detect and map the reservoir top as early as possible to smoothly place the well 10ft TVD below the top of the B sand with an inclination of 85 degrees.

Several operational risks have been identified during pre-well feasibility study, including reservoir resistivity and thickness variation, presence of intra-shale, presence of faults, and depth uncertainty from old seismic data. To mitigate the risks, extra-deep azimuthal resistivity technology was included in the bottom hole assembly. The real-time reservoir navigation was carried out remotely with frequent updates on real-time log analysis and observations provided to the client via a 24/7 virtual meeting. The service provider implemented a newly introduced centralized reservoir navigation system, which provides quicker data handling and seamless job handover. Multiple deterministic inversion workflows with constrained parameters were set-up to process the real-time extra-deep resistivity measurement, maximizing reservoir detection and mapping capability.

As a result, the 8 ½" hole section of K-W well has been landed within the target formation. After the last marker, log signatures indicated good correlation with offset well data, extra-deep resistivity detected the presence of sand from a distance of approximately 34ft TVD. However, the automated inversion multi-layer mapping indicated possible reservoir pinch-out. A decision was made by the operator to further drop inclination and cut down through the entire formation. When the well crossed the expected reservoir top, extra-deep resistivity quickly decreased and log signatures indicated the presence of a thin sand before entering the basal shale formation, which was accurately mapped by inversions. In the post-well phase, deterministic uncertainty and the new statistical uncertainty analysis were run to evaluate the position of the main boundaries defined by extra-deep reading measurements. 1) deterministic approach is instantaneous and based on the linearized dependency between measurements and model parameters; 2) statistical quantification is based on the analysis of set of intermediate inversion models and includes industry-standard P50 visualization.

This paper presents a case history of implementing extra-deep resistivity technology to overcome geological uncertainties during well landing in a complex environment. The novel approach of utilizing multiple deterministic inversion workflows maximized detection capability and accurate mapping of the reservoir structure, allowing early risk mitigation even before entering the target reservoir.
Demonstrating the Performance of a Multi-Tubular Corrosion Inspection Tool in Alloyed Completions
Authors: Fouda Ahmed , Dai Junwen
Published: February 2022

With the increased demand for drilling deeper wells in harsh environments involving corrosive, briny waters and more corrosive crudes, completion engineers increasingly adopt more resilient materials for well casings than conventional carbon steel. These materials include alloyed steels, where ferrous steel is mixed with other non-ferrous materials, such as chromium and nickel, for increased strength and durability. Alloyed steel has a lower magnetic permeability than carbon steel and, therefore, generates weaker electromagnetic signatures when logged with electromagnetic pipe inspection tools. This paper demonstrates the performance of an array multi-frequency electromagnetic pipe inspection tool in scenarios involving alloyed completions using a simulated mockup test with known defects. The types of defects considered are circumferential with different combinations of overlapping and non-overlapping defects on well casings.

The pipe inspection tool uses the eddy current principle and includes two transmitters and eight receivers. It operates in continuous wave mode at multiple frequencies. Optimized transmitter-receiver spacing configurations and multi-frequency operation provide sufficiently diverse information to help assess metal loss in individual pipes for a wide range of configurations, including those with alloyed completions. The tool uses a sophisticated workflow of data-processing and inversion algorithms to decouple individual thickness information from the measured data.

A mockup test was designed to replicate typical alloyed completions used in deep water wells to assess tool performance in different scenarios. The mockup comprises an alloyed tubing and two outer casings, which are standard ferromagnetic steel pipes, with seven combinations of defects on the casings. The tool response is synthetically simulated using a finite element electromagnetic solver and the synthetic data are inverted for metal loss on each one of the pipes. The estimated metal loss for each defect was compared to the actual metal loss to assess the accuracy of the tool. It will be shown that in order to obtain high accuracy of metal loss estimation, the electromagnetic material properties of the pipes, including that of the alloyed tubing, must be estimated with sufficient accuracy. The information provided by this tool will enable regular inspection of deepwater wells for corrosion and other integrity issues with minimal downtime and intervention cost.
Optimization of Nitrogen Lifting Operation in Horizontal Wells of Carbonate Formation
Authors: Hashmi Usman Iftikhar , Osisanya Samuel , Rahman Motiur , Ghosh Bisweswar , Subaihi Maad Hasan , Kuwair Husam F. Bin , Fathy Ahmed , Afagwu Clement , Mahmoud Mohamed
Published: February 2022

Specialized operations such as Nitro-logging require nitrogen injection in the open hole. The operating parameters of a nitrogen lifting operation in the open hole depend upon several factors that need to be considered which are discussed and optimized in this paper. A decision matrix is created to show the optimum injection rate for nitrogen for different reservoir parameters. In addition, the effect of several completion components is also discussed that can aid in operation.

In this project, we examine these factors and deduce the optimum operational conditions for each case based on reservoir and well condition. The controllable operational conditions are gas injection rate and pressure. Different combinations of water cut (WC), reservoir pressure, gas-oil-ratio (GOR), and productivity index (J) are simulated in Wellflo® to predict production in each case. Optimum Economic Factor (OEF), calcultates the efficiency of operation in each case which entails a well's production rate, length of operation, and amount of nitrogen used. The optimum operational conditions are used to populate a decision matrix that incorporates all the examined factors.

The analysis of the decision matrix led to the identification of different trends between the operational conditions and reservoir parameters. The main findings were that a higher gas injection rate was required to lift higher WC wells, especially wells with WC greater than 60%. Moreover, wells with GOR less than 1700 scf/stb or wells with J lower than 1 stb/d/psi or wells with reservoir pressure below 2500 psi also required higher gas injection rates. The effects were direr when the well met more than one of these parameters. Additionally, as coiled tubing size increases from 1.25 to 1.5 to 2", liquid production decreased from 2250 to 1960 to 1280 bbl/d, respectively. Studying the back pressure from facilities showed that as pressure increased from 0 to 400 to 1000psi, liquid production reduced as well from 700 to 450 to 100 bbl/d, respectively.

Considerable economic reduction can be achieved if the operation is optimized as per the results of this project. Overall, the decision matrix eases the operation's planning and execution time while optimizing the quantity of nitrogen gas used.
Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity
Authors: Sarma Pallav , Rafiee Javad , Gutierrez Fernando , Calad Carlos , Hilliard Ryan , Plotno Sebastian , Mamani Eduardo , Angulo Oscar , Quintero Gabriel
Published: February 2022

As the oil and gas industry embarks on the path to energy transition, pressure from government regulators, investors, and the public in general demand that companies have clear and transparent net-zero goals and that their operational initiatives and plans support such transition efforts. Mature fields present an opportunity to increase production through operational optimization, which at the same time, can also lead to greenhouse gas (GHG) emissions efficiency.

This paper presents the application of a novel modeling and optimization technique in a mature waterflood environment. Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity and can even be used to predict performance of new wells without any historical data. The technology was applied to a mature field in the Neuquen basin in Argentina to effectively reduce the amount of water injected into the reservoir with no negative impact on the production. Additionally, a new Carbon Intensity (CI) modeling tool was used to compare the emissions intensity before and after optimization showing a significant improvement in CI achieving three objectives in one single decision: 1) obtain significant water injection reduction with its corresponding impact in injection and water treatment costs; 2) maintaining production compared to the initial decline of the field, improving the top line; and 3) improving the GHG emissions intensity hence the long term benefit to the environment.

The paper deals more with the implementation of the technologies than the technologies themselves, assuming that readers unfamiliar with both Data Physics and Carbon Intensity tools will refer to the references section to gain familiarity with these.
Caprock Velocity Characteristics and Its Significance for CO2 Storage in a Saline Aquifer: A Case Study in Malay Basin
Authors: Cai Zhong , Tiwari Pankaj Kumar , Leite Renato Jordan , Das Debasis Priyadarshan , Chidambaram Prasanna , Patil Parimal Arjun
Published: February 2022

The Malay Basin is located at the southern part of the Gulf of Thailand between Vietnam and Peninsular Malaysia. The produced gases in the basin contain varying amounts of CO2. High CO2 concentrations (>50 mol%) are, in typical of reservoirs, mainly derived from inorganic sources. In order to develop these fields with reduced or zero greenhouse gas emissions for sustainability development, some geological storage sites were screened to store the future CO2 production. One of the important geological storage sites is the saline aquifer. There are many elements to assess the quality of the CO2 storage sites, such as caprock integrity and storage capacity. This study focused on one of the caprock characteristics, i.e., caprock velocity characteristic, and its significance for CO2 storage in a saline aquifer.

The studied saline aquifer is one of a numerous grabens or sub-basins at the southern part of Malay Basin. There is one abandoned exploration well with 2D and 3D seismic surveys and there are several types of velocity data, including sonic log, checkshot and seismic stacking velocity. The formations are composited of multiple thick sandstones and shales. Based on velocity analysis, including average velocity and inter-val velocity analysis. the interval velocity in caprock zone is related to the lithology, normally expressed by the sandstone percentage in the caprock zone. The lithology variation will affect the geomechanics characteristics, so, the velocity in caprock zone presents the caprock quality. The caprock is confined within the sub-basin and sandstone percentage is less than 40%, which shows a good seal capacity, com-bined with the caprock thickness distribution.

The velocity in shale zone (caprock) is higher than overlying and overlain sandstones. There is strong positive reflection (peak) at the top of shale zone (caprock) and negative reflection (trough) at the bottom of caprock, i.e., top of sandstone aquifer. There would be strong class III AVO feature at the top of sand-stone aquifer on time-lapse 4D seismic reflection once CO2 is injected. These features (negative reflection with bright spots) and class III AVO at the top of aquifer) are useful for MMV to monitor the CO2 plume migration.
A Novel Chemical Treatment and Well Completion Strategy to Prevent Scale and Production Losses in Shushufindi Aguarico Field
Authors: Paredes Monica , Bravo Marques da Silva Luciano , Egas Lucia , Endara Edison , Escalona Pedro , Maulidani Oki , Pineda Alexander , Estevez Diego , Guaman Juan , Carrion Johana , Freire Carola , Villamar Fausto
Published: February 2022

In this case study, EP Petroecuador and Consorcio Shushufindi evaluate a chemical treatment and completion strategies to reduce the extensive impact of bottomhole scale deposits on oil production, electrical submersible pumps (ESP) run life, and operating costs of wells completed in the high-scaling tendencies reservoir. The positive impact on oil production optimization resulting from these strategies will also be discussed and the advantages, lessons learned, and constraints of this work.

Conventionally, corrosion and scale chemical inhibitors are deployed through capillary lines; this method is effective up to the pump depth but does not prevent deposits at the perforations or at the lower completion and near wellbore. Rapid production decline or complete loss of production is observed, requiring costly well interventions. Laboratory analysis and evidence from the interventions show that lower T-sand fluids present a high-scale tendency at the bottomhole; therefore, a process to identify candidates and deploy chemical treatment in the rathole to prevent scale deposits was defined and proved. The technology selected was encapsulated scale inhibitors (microcaps).

Based on the process, two wells were selected from a portfolio of 12 wells that match the criteria to apply the method to deploy the technology. The following observations were drawn: -Calcium carbonate (CaCO3) is the most common scale-ESP parameters and production surveillance are essential for early detection of problems associated with scale deposits at bottomhole-The action of microcaps and the installation of a pipe tail below the ESP base sensor allowed to deepen the continuous dosage of scale inhibitor and has already doubled the run life of the ESP equipment, with direct savings on operations costs (approximately USD 240,000) in the short time and continue and can continue to yield more.-According to post workover (WO) production tests of the two candidates and the performance of ESP parameters, the application of this strategy made possible to restore the productivity indexes and sustain them over time. This leads to reduction in production losses of 310 BOPD or 60% of the actual production in the similar period before the treatment.-The microcaps can be applied and refilled through rig-less annulus-It is a low-cost solution for scale problems at bottomhole.

This document presents an analysis to reduce operating costs in wells that produce fluids with a high-scaling tendency at bottom hole, through an unconventional and low-cost strategy of chemical treatment from the sand face to the wellhead. This novel process and microcaps application can be used in wells in remote and difficult areas to service on a regular basis.
Combining Physics and Machine Learning for Multimodal Virtual Flow Metering with Confidence
Authors: AL-Qutami Tareq Aziz , Ishak Mohd Azmin , Wollebaek Lars , W. Ahmad W. Adrie
Published: February 2022

This paper introduces a multimodal virtual flow meter (VFM) that merges physics-driven multiphase flow simulations with machine learning models to accurately estimate flow rates in oil and gas wells. The combining algorithm takes advantage of the confidence decay and historical performance factors to assign confidence and contribution weights to the base estimators and then aggregates their estimates to arrive at more accurate flow rate estimates. Furthermore, the proposed multimodal VFM provides an indication of the confidence level for each estimate based on the underlying agreement of the base estimates and the historical performance. The proposed VFM was tested in a 6 months online pilot in two oil wells. The proposed multimodal algorithm resulted in almost 50% improvements in performance compared to individual VFMs. The proposed robust multimodal approach can provide a complimentary benefit as an optimal VFM and reduce the overall system uncertainty. The developed VFM can be used for real-time production monitoring, verification and backup of physical meters, and well-test validation.
Pipeline Leak Detection Combining Machine Learning, Data Assimilation Approaches and Pipeline Fluid Flow Physics Models
Authors: Kyriacou Stylianos , Sarma Pallav , Rafiee Javad , Carlos Calad
Published: February 2022

With growing worldwide consensus about the impacts of climate change, the oil and gas industry faces unprecedented pressure to minimize its carbon footprint. The biggest source of carbon emissions in the industry is the so-called fugitive emissions, accounting for ~57% of the total oil and gas industry emissions, resulting from leaks in oil and gas pipelines and facilities. Fast, accurate and economic prediction of leaks in pipelines would significantly reduce fugitive emissions by reducing the time to respond to a leak.

The proposed leak detection algorithm is a mixture of state-of-the-art machine learning and data assimilation techniques with well-known physical models and correlations of fluid flow in pipeline networks. The algorithm is tasked to continuously oversee pipeline operations by means of pressure and flow measurements. The proposed algorithm can probabilistically detect when and where a leak is taking place at the frequency of data collection (minutes/hours), thus minimizing the time to respond and the total fluid loss (fugitive emissions). The proposed algorithm utilizes a variant of the ensemble Kalman filter for probabilistic data assimilation together with an underlying network physics model. The model is augmented with meta-models and anomaly detection machine learning algorithms for real-time detection of leaks. The effectiveness of the proposed algorithm is demonstrated through a synthetic test case based on a realistic dataset.
Advanced Pore and Rock Type Characterization Using NMR T2 Gamma Inversion in Carbonate Reservoirs
Authors: Haji Abdullah , Fateh Ahmed , Abouzaid Ahmed
Published: February 2022

Carbonate reservoir complexity imposes some challenges in formation evaluation and characterization. Grain, pore, and throat size distributions play major roles in rock typing to understand static and dynamic behaviors of carbonate reservoirs. Special core analysis techniques, such as MICP and digital core imaging, revealed that the presence of different types of pore structures can be classified based on sizes as micro, meso and macro pores. This paper explores a unique inversion technique using nuclear magnetic resonance (NMR) data to deliver fast, accurate, and continuous pore typing across logged intervals.

The traditional NMR data processing technique consists of sequential steps that ultimately convert echoes from time domain into T2 domain using an exponential inversion, also known as Laplace transform. NMR-gamma inversion (NMR-GI) workflow is a mathematical approach to process the NMR data using probabilistic functions. The gamma inversion function has a form of a bell curve with the base in the logarithmic x-axis. Unlike exponential inversion technique, this inversion produces multiple components. Each component is located at a particular time and it is labeled with a specific number, which reflects the T2 time stamp of each component. The individual area under each component is translated into porosity units.

The application of gamma inversion in a T2 spectrum results in subcomponents via deconvolution of the original spectrum. The display of the components makes it easy to visually analyze and interpret the porosimetry for pore size distribution and group the components for pore typing.

A deeper look into the different components of the T2 spectrums enlarges the NMR measurement portfolio by displaying the porosimetry and pore size distribution. The inverted T2 results from both logging while drilling (LWD) and wireline tools in different fields are consistent with the reservoir geological and petrophysical models. The added value of this technique can be tangible for geophysical and geological (G&G) applications as well as completion design optimization. The NMR-GI technique can be further utilized and calibrated to improve reservoir understanding and optimize advanced core analysis by providing a quick continuous carbonate pore and rock type log.
Pressure Transmission Testing Confirms Performance of Aluminum Complex Fluids as Dynamic Borehole Stabilizer
Authors: Buranaj Hoxha Bez , Porter Justin , Everhard Eli
Published: February 2022

Testing the interaction between drilling/completion fluids and the formation is the key critical concept to understand the fundamental mechanism to borehole stability. Unfortunately, most industry tests lack the down-hole conditions to give realistic results. However, there is one advanced testing that can be used to directly and quantitively provide realistic borehole stability interpretations. Specifically, the pore pressure transmission (PPT) test has increasingly gained popularity providing results on how to stabilize troublesome shales by facilitating proper fluid design. In this study, precipitating aluminum chemistry is employed to develop a high-performance water-based mud (HP-WBM) that is tremendously robust and versatile – demonstrating that it can stabilize multiple shale type formations.

PPT evaluations on the alumiumum complex HP-WBM was performed at 250°F with a high simulated overbalanced 1000 psi pressure differential, to fully confirm that the system can withstand high pressure influx and prevent pressure transmission into the shale pore matrix, essentially reducing induced borehole instability. PPT testing was performed on two different types of shales, Pierre Type II and Mancos shale exhibit noteworthy differences in physical, chemical, mineralogical, and mechanical properties, making them ideal shales to study the versatility of the aluminum complex drilling fluid. Because of the pore-plugging capabilities, the fluid can establish, an improved semipermeable membrane, allowing for the counterbalance of hydraulic flow into the shale via osmotic backflow. When compared to the base (water-based mud), a significant delay factor is observed using the aluminum complex fluid, indicating significant reduction in pressure transmission into the shale pore matrix. An invert emulsion system was also tested for comparison and showed the Al-HPWBM's was able to perform similarly at stabilizing these shales. Advantages of precipitating aluminum chemistry over other methods will be further discussed.
Brief Perspective on the Design and Operational System of the H2S Lab at the Dhahran Technology Center, Baker Hughes in Saudi Arabia
Authors: AlSuwaij Ahmed , Jackson Tracey , Hoegerl Manuel
Published: February 2022

40% of the world gas reserves are located in in the Middle East, of which most contain high amounts of corrosive gases H2S and CO2, this with other environmental factors exerts stress on metallic and nonmetallic materials. For Baker Hughes to address these challenges, one answer was to establish local competencies in the Kingdom of Saudi Arabia as a H2S lab and materials science focused team, ultimately targeting the reduction of total expense of corrosion, and to tap into the research capabilities and expertise available in the ecosystem. H2S gas is a major health and safety challenge to deal with, as it is a lethal, flammable, corrosive. The analysis to design the lab was supported by methods of Asset Integrity Risk Management looking on barriers, process, and industry standards. The human factor was considered to ensure competency, mindset & culture. Among others, OSHA standards were followed to develop the Chemical Hygiene Plan (CHP) and respiratory protection program and Emergency Response and Operations Plan (EROP). An unexpected challenge arose during the COVID-19 pandemic where measures were taken to limit infection while maintaining lab operation. The lab started operation with a narrow scope to focus on critical lab operational skill development, successive new competencies and workflows are added following a Management of Change (MOC) process. Current developments leverage the labs growing competence to address arising challenges on ultra-high H2S, CO2 for CCUS, and hydrogen for the energy transaction.
Duygusal Zekâ Psikolojik Beceriler İçin Önemli Mi? Sporcularda Psikolojik Beceriler ile Duygusal Zekâ Arasındaki İlişki
Authors: SARI İhsan , ONBAŞI Salim İbrahim , KARAKAŞ Esmanur
Published: February 2022

Bu araştırmanın amacı sporcuların duygusal zekâsı ile psikolojik becerileri arasındaki ilişkinin incelenmesidir. Bu amaçla 160 erkek (%76.2) 50 kadın (%23.8) olmak üzere toplam 210 sporcu gönüllü olarak araştırmaya katılmıştır. Araştırmaya katılan sporcuların yaşları 21.10±2.68, spor deneyimleri 8.22±4.21 yıl ve haftalık antrenman süresi 10.56±6.06 saat olarak belirlenmiştir. Veri toplama aracı olarak Ottawa Mental Yetenek Değerlendirme Ölçeği ve Sporda Duygusal Zekâ Envanteri kullanılmıştır. Analizler SPSS 22 programında, tanımlayıcı istatistikler, Pearson korelasyon analizi ve regresyon analizi (enter yöntemi) ile değerlendirilmiştir. Pearson korelasyon analizi sonuçlarına göre duygusal zekânın tüm alt boyutları ile psikolojik becerilerin hedef belirleme, özgüven, kararlılık, rahatlama, canlandırma, odaklanma, hayal gücü, zihinde canlandırma ve mücadele planı alt boyutları arasında istatistiksel olarak anlamlı pozitif ilişki olduğu bulunmuştur. Regresyon analizi sonuçlarına göre duyguların kullanımının hedef belirleme, rahatlama ve hayal gücünü; kendi duygularını değerlendirme ve duyguların kullanımı puanlarının canlandırma ve zihinde canlandırmayı; duyguları düzenleme ve duyguların kullanımının özgüveni, sosyal becerilerin odaklanmayı; sosyal beceriler ve duyguların kullanımının ise mücadele planını istatistiksel olarak anlamlı düzeyde açıkladığı belirlenmiştir. Bu bulgular doğrultusunda sporcuların psikolojik becerilerinin duygusal zekâları ile ilişkili olduğu ve duygusal zekânın sporcuların psikolojik becerisine anlamlı katkı sağlayabileceği söylenebilir. Araştırmanın sonuçları genel olarak değerlendirildiğinde, sporcularda duygusal zekânın gelişimine katkı sağlayacak uygulamaların psikolojik becerileri de olumlu olarak etkileyebileceği söylenebilir.
INTEGRATED WATER RESOURCE MANAGEMENT FOR KOVILPATTI – A FUTURISTIC APPROACH
Authors: B Anitha , J Khaja Ihsaanur Rahim , .G Sugirtha Murugalakshmi , R Balaji , M Ramakrishnan
Published: February 2022

Today, sustainable utilisation of water is becoming more complicated due to dwindling water resources. Hence water is so important but now a day there is insufficiency and scarcity of water in some places. Hence to solve the problem we need to conserve water. Water conservation includes all the policies, strategies and activities to sustainably manage the natural resource of fresh water, to protect the hydrosphere and to meet the current and future human demand. This paper focuses on the study of the prevailing water scarcity and demands in the southern part of Tamil Nadu, India by analysis the existing water resources available , population , quality of water and the quantity of water . Finally this paper makes appropriate suggestions to establish sustainable water management without compromising the quality of water. Population, household size, growth and affluence all affect how much water is used. Factors such as climate change have increased pressures on natural water resources especially in manufacturing and agricultural irrigation. Water resource management is the activity of planning, developing, distributing and managing the optimum use of water resources. The field of water resources management will have to continue to adapt to the current and future issues facing the allocation of water. With the growing uncertainties of global climate change and the long term impacts of management actions, the decision-making will be even more difficult. As a result, IWRM would be a better solution with alternative management strategies are sought for in order to avoid setbacks in the allocation of water resources. In this work, we are trying to implement our IWRM technique in our region. Ideally, water resource management planning regard to all the competing demands for water and seeks to allocate water on an equitable basis to satisfy all uses and demands. Integrated Water Resource Management (IWRM) will create a pavement for solving the water scarcity by means of managing water resources. By collecting all water supply data in a particular region (kovilpatti) and we came to know that all 36 wards of kovilpatti town are safe from scarcity but it is known that the scarcity was faced by the kovilpatti panchayat people, Our solution for that people is by treating the water from natural tanks and thus IWRM is performed in kovilpatti region. The characteristic study is performed for these natural tanks.
Antibacterial and antibiofilm activity of Begonia multangula Blume. leaf extract
against Candida albicans
Authors: Hamzah H. , Siregar K.A.A.K. , Suffiana Y. , Yudhawan I. , Nurwijayanto A.
Published: February 2022

Candida albicans is a fungus that is included in the normal microbiota of humans. In
general, C. albicans remains a lifelong commensal microorganism. This fungus has a
hazardous impact. C. albicans can cause infection and is fatal. The spread of this fungus is
one of the factors caused by antibiotic resistance, which causes C. albicans to be resistant
and form strong colonies (biofilms) to survive. Natural ingredients for treatment is an
alternative that is often used by the community because their use is effortless and
practical. One of the plants is the leaves of Begonia multangula Blume. This plant is
known to have potent antifungal activity, but its antibiofilm activity against C. albicans
has not yet been reported in-depth about its antibiofilm. The discovery of new antibiofilm
against C. albicans biofilms is a way to prevent the effects of infections caused by these
biofilms. This research is a form of dedication to the effectiveness of Begonia multangula
Blume leaf extract in inhibiting and eradicating C. albicans biofilm formation. Planktonic
testing, inhibition, and biofilm eradication activity were carried out using the microtiter
broth method. Antibiofilm activity of begonia leaves on C. albicans was measured by
calculating the minimum concentration of biofilm inhibitor (MBIC50). Data were analyzed
using the Statistical Package for the Social Sciences (SPSS) with a 95% confidence level.
The begonia leaves showed inhibitory activity against the tested C. albicans biofilm
formation. Begonia multangula Blume leaf extract 1% gave C. albicans antibacterial
activity of 75±0.01% and mid-phase antibiofilm activity of 65.21±0.01% and maturation
phase of 50.11±0.01. The results also provided evidence that the ethanolic extract of C.
albicans can degrade (eradication) the formation of C. albicans biofilms. Therefore, the
ethanol extract of begonia leaves can be developed as a new antibiofilm agent against C.
albicans.
Educación Virtual como alternativa a la educación presencial un nuevo reto para las Universidades
Authors: Lalama Aguirre José María , Vernaza Arroyo Girard , Tipán Chiguan Imelda Marina , Ramírez Gualoto Fernando
Published: February 2022

Se da Inicio a esta pequeña conversación con el futuro lector de estas páginas cargadas de esperanza, entendimiento, comprensión y reflexión de lo que se ha venido construyendo en el mundo de la Educación Universitaria. De una u otra manera en este diálogo reflexivo se hará una inmersión introspectiva con la sustancia, esencia y apariencia de ser Docente, en este trabajo investigativo intelectivo, se pretende en los momentos actuales escudriñar el alma, en el sentido de cómo, para que y porque somos docentes en tiempos de cambio. Al docente antropológicamente le han asignado tareas y roles en los albores del siglo XXI; se han generado vías, ideas, modos, como ser y cómo hacer para dar respuesta en esta sociedad compleja, complejizadora y en muchas ocasiones sorda, sórdida; donde la notoriedad no existe o es valorada en pocas ocasiones. Entonces, en este momento histórico en las manos del docente está el camino de continuar con la responsabilidad de educar, seguir regando esperanzas, valores y acepciones a la diversidad de pensamiento, sentimientos y actuaciones sin perder el foco de atención que los hace más humanos. A lo largo del texto se encontrarán pista para configurar como es un constructor de saberes, por cuanto se conforman conocimientos epistémicos que siguen el construccionismo social como fuente para una pedagogía constructivista y crítica, donde el docente requiere ser organizador de la misma, garantizando el sentido de los saberes para crear y cocrear situaciones de aprendizajes virtuales en tiempos de cambio; en función de la heterogeneidad que cohabita en un espacio particular con una interconexión con el todo.