Operational Issues Research Articles

Analyzing the effects of different patterns of diffuser layouts on air distribution and mixing quality in an aeration tank using CFD

A diffused aeration system in an activated sludge process is generally designed based on a limited number of typical diffuser patterns. There are instances where these patterns have shown various operational and process issues, resulting in poor process performance. To address these issues, bubble interactions and the mixing conditions must be factored into the design of alternative patterns. For this purpose, a validated full-scale CFD model was used to simulate five diffuser patterns: lateral, grid, line, stripe, and checkered patterns‎, the first two in operation, and the next three as designed patterns. The patterns were simulated for an aeration tank under identical functional conditions. The population balance method was incorporated into the model to analyze how various patterns affect bubble size distribution. It was found that changing the pattern of diffusers had a significant effect on bubble breakup/coalescence and the resulting bubble size distribution. The results showed a range of 6.1–22.9% for the coarse bubble percentage and 11.9–16% for the fine bubble percentage in the five simulated patterns. Moreover, evaluating flow circulation and velocity based on the average flow velocity data of the simulated patterns showed up to 30% variation, ranging from 0.29 to 0.375 m/s. It highlighted the great impact of diffuser patterns on bubble size and mixing quality as notable factors in oxygen transfer efficiency. Finally, the proposed patterns were compared and ranked based on air distribution, bubble size, and mixing quality criteria using the multi-criteria decision-making (MCDM) approach. It was concluded that the checkered pattern outperformed the proposed and existing ones.

Using rational surfaces to improve pellet fuelling in stellarators

Pellet injection is currently the primary candidate for achieving efficient plasma fuelling, one of the key issues for steady-state operation in large fusion devices. In this paper, pellet injection experiments are performed for several magnetic configurations of the TJ-II stellarator. The aim of this study is to increase the understanding of the role played by rational surfaces in plasmoid drift and deposition profiles in stellarators. The analysis of experimentally observed plasmoid drifts is supported by simulations of such cases made with the HPI2 code. Plasmoid drift is found to be significantly reduced, as in tokamaks, in the vicinity of rational surfaces. This is attributed to the fact that plasmoid external charge reconnection lengths are shorter near rational surfaces, resulting in a more effective damping of the plasmoid drift. Although the effect of plasmoid external currents on the drift is expected to be negligible in stellarators, compared with those caused by plasmoid internal currents, the effect observed in TJ-II is clearly measurable. In addition, simulations show that enhanced drift reductions near rational surfaces lead to significantly different deposition profiles for the magnetic configurations included in this study. This implies that it should be possible to select the magnetic configurations to obtain more efficient pellet fuelling.

Reflective reviews on Japanese high-level waste (HLW) vitrification – Exploring the obstacles encountered in active tests at Rokkasho

Vitrification is currently the most widely used technology for the treatment of high-level waste (HLW) generated from spent nuclear fuel (SNF) reprocessing. Glasses for the solidification of HLW have been developed over half a century at different times in each country. Likewise in Japan, repeated system evaluations of borosilicate waste glass matrices and the liquid-fed joule-heated ceramic-lined melter (LFCM) process have been found to be applicable to a wide range of HLW compositions. The Rokkasho vitrification facility located in northern Japan encounters significant operational issues owing to the presence of sparsely soluble elements including noble metals (NMs) and separated phases, such as the yellow phases (YPs) constituents, in glass melts. These elements cause serious disruptions, jeopardizing the stable functioning of the LFCM. The active vitrification test was suspended because of the formation of YPs in the glass melts and subsequent sedimentation of NMs at the bottom of the melter. First, we analyzed the testing data to identify the fundamental causes behind the unstable LFCM operation. Our investigation revealed that the presence of significant amounts of Mo and S, along with NMs, specifically Ru, Rh, and Pd, in the Rokkasho HLW stream triggers the formation of molybdate and sulfate salt phases (YPs), which subsequently enabled the sedimentation of NMs at the bottom of the melter. Second, we selected eight major tasks related to difficulties of LFCM operation in the active tests and studied the findings in each task considering the preconditions of task success. Moreover, we strengthened our analysis by subjecting each task to 22 hypotheses tests in a systematic manner to minimize the risk of issues associated with generalizability. Third, we combined these findings and classified them into two categories based on the action system theory on management: variable system (Y) and actor’s system (X), assuming that the indirect or mechanism/environment factors of X should influence on the directly observed factors of Y. Our approach involved implementing a reflective actor’s system (X’) to improve the variable system for the controllable system (Y’), thereby mitigating unexpected events. We then summarized the main implications of this method to ensure predictable and reliable outcomes for the associated tasks. Finally, we concluded the potential effectiveness of strategic actions that could leverage organizational capabilities and processes to improve task performances in the context of a controllable system given the implications we identified.

Coppice reintroduction in the Czech Republic: extent, motivation and obstacles

Coppicing is a form of forest management in European broadleaved forests. While it is still practised in south-eastern Europe, in central and western Europe it was almost completely replaced by high forest management. Currently, there are increasing efforts to reintroduce coppice management into former coppice woods. However, little comprehensive knowledge is available about the extent and management of coppice reintroduction and the processes governing it. In this paper, we present an overview of localities in the Czech Republic where coppice reintroduction was taking place in 2020. We identified 8 localities and conducted 10 semi-structured interviews with experts involved in their management in order to understand what motivated them to restart coppicing and what obstacles they have had to face. The main motivation of site managers is nature conservation, while the most important obstacles are operational issues, such as bureaucracy, complicated land ownership and legal responsibilities or lack of workforce. Other motivating factors identified in other European countries (production of a renewable energy source and potential for rural employment) have so far played a minor role in the Czech Republic. We conclude that a major challenge for future forest policies will be to utilise the economic experiences of regions with active coppicing to foster the spread of coppice woods in order to combine renewable energy production with biodiversity conservation.

The clinical implementation of a de-escalation strategy using CYP2C19 genotyping in patients with an acute coronary syndrome: insights from the FORCE-ACS registry

Abstract Background Current guidelines recommend prasugrel and ticagrelor in patients undergoing percutaneous coronary intervention (PCI), however these potent P2Y12-inhibitors are associated with a higher bleeding risk when compared to clopidogrel. The CYP2C19 gene influences the efficacy of clopidogrel, with carriers of loss-of-function alleles having a higher risk for major adverse cardiovascular events. A genotype-guided strategy whereby noncarriers are treated with clopidogrel has been shown to reduce bleeding without increasing the risk of major adverse cardiovascular events and to be cost-effective. Currently, there are no practical guidelines for implementing a genotype-guided strategy and there are still operational issues regarding the usage of buccal swabs or venous blood samples. Purpose The study aimed to assess the feasibility of a CYP2C19 genotype-guided de-escalation strategy in acute coronary syndrome (ACS) patients treated with dual antiplatelet therapy and to evaluate the de-escalation rate. Methods ACS patients receiving genotype-guided antiplatelet therapy from August 2021 onwards were eligible. Genotyping was done using buccal swabs in a point-of-care (POC) device or by venous blood samples in the lab. Genotyping results were entered into the EHR. Attending physicians received automatic notifications to change antiplatelet therapy based on genotyping results, but subsequent management was at their discretion. The primary endpoint is maintenance therapy with P2Y12 inhibitors, with secondary endpoints being therapy changes, and time until genotype results. Therapy changes based on genotyping method were assessed using the Chi-square and Mann-Witney U test. Results In total, 1,530 patients with suspected ACS were included in the FORCE-ACS registry from June 2021 to January 2023. A CYP2C19 genotype test was performed in 738 ticagrelor treated patients and 260 (35%) patients carried a CYP2C19 LoF-allele. The median time to genetic test result was 6.2 hours, with 84.6% of results known within 24 hours for POCT and 15.4% for blood analysis. Of all 478 CYP2C19 LoF-allele noncarriers eligible for de-escalation, 433 (90.4%) patients were successfully de-escalated from ticagrelor to clopidogrel. De-escalation to clopidogrel occurred within 24 hours in 70.9% of patients and within 48 hours in 93.1%. The time to de-escalation was significantly lower in patients analyzed using POCT (25.4 hours) compared to blood analysis (58.9 hours). Conclusion The implementation of routine genotyping is feasible and de-escalation rates to clopidogrel in non-carriers are acceptable. The quicker time-to-results and de-escalation highlights the potential benefits of using POC testing.Overview of genotype-guided strategy

On the anti-rolling performance of a train using a vortex generator array

Vortex generators (VGs) have shown the potential to mitigate the train's operational instability issues caused by strong wind. Numerical simulations are used to predict the flow structures around a train with VGs of different heights. The improved delayed detached eddy simulation (IDDES) hybrid modeling method is adopted to predict the trailing vortices on the leeward field. The numerical method is validated by reproducing wind tunnel test results. The study results reveal that VGs are capable of reducing the rolling moment coefficient around the leeward rail of a train by about 5% ∼ 15% while keeping the drag of the train still lower than its operational drag without crosswind. The control mechanism lies on that the streamwise vortices generated by VGs are attracted to the large-scale trailing vortices, resulting in the pressure on the leeward wall rising. The differences in the domain frequencies between VGs and Baseline cases in POD modes indicate that the VGs changed the periodicity and symmetry of the vorticity fluctuation. This study provides a new method to improve the safety of trains under crosswinds.

A hydrogen-vanadium rebalance cell based on ABPBI membrane operating at low hydrogen concentration to restore the capacity of VRFB

A hydrogen‑vanadium rebalance cell (HVRC) is developed to address the capacity degradation and hydrogen explosion risks in long-term operations of all‑vanadium liquid flow battery (VRFB). Different operating conditions was evaluated in this study to investigate the cell's performance focusing on low hydrogen concentrations (4 %). The HVRC achieved a stable rebalance process with a meager hydrogen stoichiometry ratio of 1.15 at 300 mA cm−2. The use of PFSA membranes was found to be problematic due to the contradictory issues of unstable battery operation without humidification and severe vanadium ion loss with humidification. Therefore, ABPBI membrane was used instead of PFSA membrane to reduce vanadium ion crossover and Pt catalyst corrosion. HVRC based on ABPBI membrane completed a 100 h stability test at 70 mA cm−2 with a stoichiometric ratio of hydrogen of 1.23. The vanadium ion crossover of ABPBI-HVRC was less than 1/6 that of PFSA-HVRC, and no significant platinum dissolution occurred during 100 h operation without humidification. The reliability of the ABPBI-HVRC in intermittent operation mode has also been verified. These results illustrate the HVRC offers a promising measure to eliminate safety issues and restore capacity in VRFB systems. Additionally, incorporating the ABPBI membrane in the HVRC holds greater promise for enhancing its stability.

Digital Forensic Investigation Tools for Cases Related to Social Media and Cybersecurity

The entire world has moved into the digital realm as a result of the quickening pace of technological development. However, this change has also led to an increase in cybercrimes and security breach occurrences, which endanger user security and privacy. As a result, this study sought to examine how digital forensics, a significant advancement in cybersecurity, is used to combat cybercrime. The most recent developments in digital forensics, such as cloud forensics, social media forensics, and IoT forensics, have been examined in this study. With the use of these technologies, cybersecurity experts can protect data while identifying fraudsters by using the digital footprints that data processing and storage leave behind. Technical, operational, and personnel-related issues are among the specific dangers to digital forensics that have been identified by the research. These systems' high level of complexity, volume of data, chain of custody, personnel integrity, and the validity and accuracy of digital forensics are all significant barriers to their widespread adoption. However, the research has also noted the use of artificial intelligence, intrusion detection, and USB forensics as significant prospects for digital forensics that can make the processes simpler, more effective, and safe.

Common DC-Link Capacitor Lifetime Extension in Modular DC/DC Converters for Electric Vehicle Fast Chargers via Variable-Angle Interleaved Operation

The global shift towards the electrified transportation as a means of achieving decarbonized economy has led to an increase in the demand of electric vehicles. In order to enhance the transition, there is a need to develop fast and ultra-fast charging substations. Hence, power converters play an inherently vital role in the mass adoption of electric vehicles. However, the reliability and active maintenance scheduling of these converters are becoming prominent issues for manufacturers and operators. As a result, optimization of the operation of an interleaved dc/dc converter present in the charging poles plays a significant role. This paper proposes an alternative modulation technique for interleaved dc/dc power converters of fast chargers aiming to improve the harmonic performance of the common dc-link capacitor current. This technique leads to an extension of lifetime of the capacitor, which is one of the most critical component of the power system. Experimental results are shown in order to validate the effectiveness of the proposed method.

Grey wolf optimization based optimal isolated microgrid with battery and pumped hydro as double storage to limit excess energy

This paper proposes a 100 % renewable fraction based isolated microgrid with wind turbine, solar photovoltaic, battery, and pumped hydro storage to minimize the levelized cost of energy (LCOE) considering a) Loss of power supply probability (LOPSP) to enhance reliability and b) Percentage of excess generation (PEG) to limit excess energy (Pex) generation. The reliability assessment is performed considering the outage rate of generating units. Isolated Microgrids with a 100 % renewable fraction generate Pex, a short-duration and infrequent event, adding to system uncertainty. The presence of Pex causes operational and reliability issues, increasing the system cost. Diesel generators are a popular choice to overcome the Pex; however, they cause greenhouse gas (GHG) emissions. The proposed microgrid is simulated on MATLAB for a year using Grey wolf optimization (GWO), Moth flame optimization (MFO), and the Dragonfly algorithm (DA). The GWO algorithm outperforms the other algorithms in terms of accuracy and execution time. The results show that the proposed microgrid restricts the Pex to 4.91 % (Case-I) and 4.95 % (Case-II) of total generation. The LCOE is 0.3588 $/kWh and 0.2491 $/kWh for Case-I and Case-II, respectively, using GWO. With the hybridization of energy storage, GHG emissions are reduced by 82.37 % and 93.15 % for Case-I and Case-II, respectively, compared to grid-supplied. The sensitivity analysis shows that CO2 emissions vary linearly with generation. However, LCOE variation is non-linear due to its significant correlation with energy storage. Thus, the isolated microgrids operate at 100 % renewable fraction with minimum LCOE, Pex, and low GHG emissions.

Proposed Knowledge Management Strategy by Leveraging Current Technology to Increase Productivity Operation in PT. Trimata Benua

As coal industries is still attractive in the world, and a significant contributor for energy production which account approximately 37% for electricity generation. This type of fossil fuel is still being used by many countries despite it negative side effect to environment for example a greenhouse gas emission. Coal attractiveness is due to the fact that it is cheaper compared to other fossil fuel, resulting coal industries is still growing and predicted for many years to come. In Asia Pacific there are many countries dubbed as largest producers of coal, with China, India, and Indonesia being on the top three countries in Asia Pacific. With expected growth of 3.5% on yearly basis this makes Asia Pacific a significant contributor of the worlds coal industry. With that as well Asia Pacific contributes around 52% of the worlds carbon emissions in year 2020. Indonesia as the third largest coal producers, have approximately 8.9% growth in 2021. With projected coal production of 2.6% Year on Year. PT. Trimata Benua is one of the key players in Indonesia’s coal production. However, the company experience lack of productivity in operations, causing their production target missed. Is the absence of knowledge management system in the company causing that issue to arise. With that the company need to roll out and plan effective and structured knowledge management system to address their business issue. This research is based on three research questions. First one what is the best fit knowledge management strategy, second is to identify issues in operations, and the last one is to have suggestion for the company on the implementation plan based on their needs. This research will answer those research questions by referencing to the knowledge management theories on the People, Process, and Technology (PPT) as well as adopting Nonaka’s SECI model in knowledge transformation cycle and Jann model knowledge management framework (Jann). Since this research is the first stage of implementing knowledge management system, it will be a qualitative method used with data collection gathered through interviews. Data will be analyse using thematic analysis. There are several issues identified in relation of People, Process, and Technology. Ranging of the lack of professionalism, incomplete SOP, awareness and training program, and the minimum technology that is supporting operation team causing the inability of easy access to knowledge. By applying and utilize PPT, SECI, and as well as Jann framework, author manage to compile and design knowledge management system that is best fit for the company. Identifying the challenges, addressing capability gaps on the organization as well as identification of organization critical knowledge to achieve the objectives of implementation. This will be compiled into a plan in designing knowledge management system. With the implementation of the system, it will result enhancement for operation team to increase their productivity, enhance decision-making process that will benefit the company in the future and raised their competitiveness with their competitors. The implementation plan will begin in February 2024, and it will cover three enablers of KM people, process, and technology. Strengthen the implementation the organization will also create KM steering committee that will oversee, review, and manages the initiatives. The committee will also do the evaluation on the system in making sure that it is stay updated, measuring the impact, and identify necessary changes.

Developing the Protocol Infrastructure for DNA Sequencing Natural History Collections.

Intentionally preserved biological material in natural history collections represents a vast repository of biodiversity. Advances in laboratory and sequencing technologies have made these specimens increasingly accessible for genomic analyses, offering a window into the genetic past of species and often permitting access to information that can no longer be sampled in the wild. Due to their age, preparation and storage conditions, DNA retrieved from museum and herbarium specimens is often poor in yield, heavily fragmented and biochemically modified. This not only poses methodological challenges in recovering nucleotide sequences, but also makes such investigations susceptible to environmental and laboratory contamination. In this paper, we review the practical challenges associated with making the recovery of DNA sequence data from museum collections more routine. We first review key operational principles and issues to address, to guide the decision-making process and dialogue between researchers and curators about when and how to sample museum specimens for genomic analyses. We then outline the range of steps that can be taken to reduce the likelihood of contamination including laboratory set-ups, workflows and working practices. We finish by presenting a series of case studies, each focusing on protocol practicalities for the application of different mainstream methodologies to museum specimens including: (i) shotgun sequencing of insect mitogenomes, (ii) whole genome sequencing of insects, (iii) genome skimming to recover plant plastid genomes from herbarium specimens, (iv) target capture of multi-locus nuclear sequences from herbarium specimens, (v) RAD-sequencing of bird specimens and (vi) shotgun sequencing of ancient bovid bone samples.

Supply Chain Simulation of Manufacturing Shirts Using System Dynamics for Sustainability

In supply chain management (SCM), goods and services flow from the raw materials stage to the end user with complexities and uncertainty at each stage. Computer modeling and simulation is a particularly useful method to examine supply chain operational issues because it can solve operational complexities that are challenging and time consuming to analyze. Manufacturing companies fear losing valuable time and assets during the manufacturing process; the inaccurate estimation of raw materials, human capital, or physical infrastructure not only leads to monetary loss for the manufacturing unit, but also has a detrimental effect on the environment. The purpose of this paper is to demonstrate that system dynamics modeling (SDM) in sustainable supply chain management (SSCM) can be applied to apparel manufacturing to optimize materials, labor, and equipment usage. Utilizing system dynamics (SD), the manufacturing unit can improve sustainability by reducing materials, labor, and equipment usage, which in turn reduces energy use. In our literature review, we did not identify any study addressing supply chain simulation of the manufacturing of shirts using SDM. We chose shirt manufacturing to demonstrate the model because of its relatively simple manufacturing process. In our study, we conclude that SDM simulation is an efficient way to optimize materials, labor, and equipment in apparel manufacturing. This leads to a more sustainable manufacturing process, as the model simulates different manufacturing supply chain scenarios in a risk-free environment, thereby minimizing waste and resources. Further, the outputs from the STELLA® model can be used as inputs into a subsequent life cycle assessment (LCA) model to determine the quantitative environmental impacts.

The Actual Situation and Issues of System Operation for the Development of Children's Playgrounds in The Metropolitan Suburbs

The Actual Situation and Issues of System Operation for the Development of Children's Playgrounds in The Metropolitan Suburbs

Minding the source: toward an integrative theory of human–machine communication

Abstract According to the computers are social actors (CASA) paradigm, a dominant theoretical framework for research on human–computer interaction, people treat computers as if they were people. Recent studies on human–machine communication (HMC) and human–artificial intelligence (AI) interaction, however, appear to focus on when and how people respond to machines differently than to human agents. To reconcile this apparent contradiction, this study reviews critically the two overarching theoretical explanations proposed and tested in each respective tradition, the mindlessness account and the machine heuristic. After elaborating on several conceptual and operational issues with each explanatory mechanism, an alternative theoretical model of HMC is proposed that integrates both research traditions and generates predictions that potentially deviate from the dual-process models. Lastly, it is discussed how recent developments in AI technology invite modifications to the current understanding of HMC and beyond.

Sustainable Technology Strategies for Transportation and Logistics Challenges: An Implementation Feasibility Study

Transportation and logistics are the basic building blocks in the socio-economic development of a country. The pandemic altered the landscape of the transportation and logistics sector where organizations had to look for new technology-based solutions. Block chain and digital trucking are emerging concepts, which were further accelerated by COVID-19, to manage the challenges in the transportation and supply chain industry. This study, therefore, investigates the challenges faced by the transportation industry during and post COVID-19 and, consequently, identifies relevant sustainable strategies to combat these challenges in a developing-economies context. Data were collected through interviews from 20 key personnel working in managerial positions in the transportation industry of Pakistan, a developing economy. The findings of this study indicate that the challenges faced by the transportation industry are reduced import–export, local market orders and revenues, supply limited to a few edible items, increase in e-commerce, new entrants in the market and operational issues, such as lack of standard operating procedures (SOPs), performance management and training of drivers. These challenges were more pronounced in the COVID-19 era; however, they are still impacting the industry. Thus, in the post-COVID-19 era, transportation companies need to opt for efficient strategies, such as contactless deliveries, expansion in e-commerce, tech-based performance management of drivers and digital trucking for sustainability, in a developing economy’s transportation and logistics sector.

Experimental and numerical investigation of the expansion mechanism of beam-end expansion devices in large-span bridge

The beam-end expansion device of a large-span bridge is critical equipment for relieving temperature stress and also a weak point. In this paper, a detailed analysis of the expansion mechanism and jamming phenomenon of the beam-end expansion device is conducted for the first time. A dynamic model of the beam-end expansion device is established using complex flexible body coupled kinematics theory. The model overcomes two major challenges in simulation: (1) the expansion function of the scissor device provides the beam-end expansion capability while transmitting force to drive the movable sleepers, and (2) the coupling effects in the longitudinal, vertical, and horizontal directions between the movable sleepers and the upper structure need to be simulated to provide not only vertical support stiffness but also force on contact surfaces during expansion motion. The phenomenon of jamming caused by the asymmetry of the scissor device is found and the reason is analyzed. By comparing the paths of load transmission caused by different types of scissor devices, the influence on scissor devices and movable steel sleepers is summarized. The conclusions derived from theoretical deductions are consistent with the simulation results and validated through four monitoring data sets. The research findings of this paper can provide insights for addressing the design, operation, and maintenance issues of beam-end expansion devices in large-span bridge projects, ensuring that they meet the requirements of engineering practice.

Multi-region combined heat and power economic dispatch based on modified group teaching optimization algorithm

Multi-region combined heat and power economic dispatch (MRCHPED) is a brand new optimization issue in power system operation. The MRCHPED uses the multiple regional structure to integrate power-only units, CHP units and heat-only units to achieve optimal economic dispatch while ensuring the supply of heat and power within each region. In this paper, an educational achievement guided group teaching optimization algorithm (EAGTOA) is developed and applied to solve the MRCHPED problem. In EAGTOA, aiming to improve the search efficiency, two historical archives are introduced to collect the valuable information of educational achievements from students and teachers, and are used in the update process of the EAGTOA. The EAGTOA algorithm is applied to solve three MRCHPED problems, including a small-scale MRCHPED with 2 regions and 9 units, a medium-scale MRCHPED with 3 regions and 17 units, and a large-scale MRCHPED with 3 regions and 96 units. The performance of EAGTOA is compared with several classical and recently-developed optimization algorithms. The simulation results show that the proposed EAGTOA can achieve better results in terms of solution accuracy and convergence speed for the MRCHPED problems.

A visual detection method for conveyor belt misalignment based on the improved YOLACT network

Conveyor belt misalignment is an operational and maintenance issue for many belt conveyor systems and can result in significant downtime and equipment failure. This article presents a visual detection method for belt misalignment detection using real-time instance segmentation. An improved version of the You Only Look At CoeffcienTs (YOLACT) network is discussed, where the feature backbone of the original YOLACT network is optimized. The improved YOLACT network is used to experimentally detect the belt misalignment with different combinations of bulk solid and operating conditions. The results show that the detection precision of the improved YOLACT network achieves 86.05% mean Average Precision (mAP), in which the greatest improvement on the mAP is the FPN modification with an increase of 1.75% compared to the YOLACT network with the ResNeXt-50. In terms of the detection speed, the improved YOLACT network (15.19 Frames Per Second [FPS]) outperforms the Mask R-CNN (3.42 FPS) and the MS R-CNN (3.41 FPS). Moreover, the averaged detection error from the tests is 0.74 mm for a belt loaded with different bulk solids. It can be concluded that the application of the improved YOLACT network is effective for the real-time detection of conveyor belt misalignment.

Deep sequence model-based approach to well log data imputation and petrophysical analysis: A case study on the West Natuna Basin, Indonesia

Well log data imputation is crucial for subsurface geology interpretation, which helps identify the most productive areas for drilling, minimize exploration risks, and maximize hydrocarbon recovery. Obtaining high-quality well data can be challenging due to various issues such as drilling problems, improper logging processes, or operational issues with logging tools. Accurate and reliable imputation methods are essential to address missing or incomplete information in well log data, allowing better decision-making in the oil and gas industry. This study presents a novel well log data imputation method for the West Natuna Basin in Indonesia, using time-series deep learning models. Focusing on the “Kappa” well dataset with missing Vp log data, we trained long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) models using a window-based time step sequence of 10-timesteps intervals as input. The LSTM was observed as the best model, achieving a mean absolute percentage error (MAPE) of about 2.2% and an R-squared (R2) of about 94%. This result suggests that deep sequence model can be effectively used for missing data imputation in well log. Lastly, a petrophysical analysis further underlines the importance of these findings, offering deeper insights into reservoir properties, and value of our research in real-world exploration scenarios. The findings can aid in improving the accuracy and reliability of imputation in well log data, enabling better decision-making in the oil and gas industry.