Relevant bibliographies by topics / Tree-based optimization tool

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Tree-based optimization tool'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Tree-based optimization tool"

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Soukaina, Mihi, Ait Ben Ali Brahim, El Bazi Ismail, Arezki Sara, and Laachfoubi Nabil. "Dialectal Arabic sentiment analysis based on tree-based pipeline optimization tool." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4195–205. https://doi.org/10.11591/ijece.v12i4.pp4195-4205.

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The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic
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Fati, Suliman Mohamed, Amgad Muneer, Nur Arifin Akbar, and Shakirah Mohd Taib. "A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool." Symmetry 13, no. 4 (2021): 686. http://dx.doi.org/10.3390/sym13040686.

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High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PP
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Kuk, Edyta, Jerzy Stopa, Michał Kuk, Damian Janiga, and Paweł Wojnarowski. "Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees." Energies 14, no. 18 (2021): 5702. http://dx.doi.org/10.3390/en14185702.

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The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit v
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Arjun, Mantri. "Intelligent Automation of ETL Processes for LLM Deployment: A Comparative Study of Dataverse and TPOT." European Journal of Advances in Engineering and Technology 11, no. 4 (2024): 154–58. https://doi.org/10.5281/zenodo.12755714.

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This paper presents Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline designed for large language models (LLMs). Additionally, it compares Dataverse with TPOT (Tree-based Pipeline Optimization Tool), an automated machine learning (AutoML) tool, to highlight their respective strengths and use cases. Dataverse aims to address the challenges associated with data processing at scale by providing a user-friendly and automated solution. TPOT, on the other hand, focuses on automating the machine learning pipeline optimization process. This paper discusses the architecture, featur
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Seda, Milos. "Steiner Tree Problem in Graphs and Mixed Integer Linear Programming-Based Approach in GAMS." WSEAS TRANSACTIONS ON COMPUTERS 21 (July 2, 2022): 257–62. http://dx.doi.org/10.37394/23205.2022.21.31.

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The Steiner tree problem in graphs involves finding a minimum cost tree which connects a defined subset of the vertices. This problem generalises the minimum spanning tree problem, in contrast, it is NP-complete and is usually solved for large instances by deterministic or stochastic heuristic methods and approximate algorithms. In this paper, however, we focus on a different approach, based on the formulation of a mixed integer programming model and its modification for solving in the professional optimization tool GAMS, which is now capable of solving even large instances of problems of expo
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Zhao, Jianyin, Liuying Ma, Yuan Sun, Xin Shan, and Ying Liu. "Optimization of Leakage Risk and Maintenance Cost for a Subsea Production System Based on Uncertain Fault Tree." Axioms 12, no. 2 (2023): 194. http://dx.doi.org/10.3390/axioms12020194.

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Traditional fault tree analysis is an effective tool used to evaluate system risk if the required data are sufficient. Unfortunately, the operation and maintenance data of some complex systems are difficult to obtain due to economic or technical reasons. The solution is to invite experts to evaluate some critical aspect of the performance of the system. In this study, the belief degrees of the occurrence of basic events evaluated by experts are measured by an uncertain measure. Then, a system risk assessment method based on an uncertain fault tree is proposed, based on which two general optimi
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Mikita, Tomáš, and Petr Balogh. "Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 3 (2015): 793–801. http://dx.doi.org/10.11118/actaun201563030793.

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This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the fi
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Morel, Benoit, Alexey M. Kozlov, Alexandros Stamatakis, and Gergely J. Szöllősi. "GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene Family Tree Inference under Gene Duplication, Transfer, and Loss." Molecular Biology and Evolution 37, no. 9 (2020): 2763–74. http://dx.doi.org/10.1093/molbev/msaa141.

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Abstract Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstr
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Uthayasuriyan, Agash, Ugochukwu Ilozurike Duru, Angela Nwachukwu, Thangavelu Shunmugasundaram, and Jeyakumar Gurusamy. "FLOW PATTERN PREDICTION IN HORIZONTAL AND INCLINED PIPES USING TREE-BASED AUTOMATED MACHINE LEARNING." Rudarsko-geološko-naftni zbornik 39, no. 4 (2024): 153–66. http://dx.doi.org/10.17794/rgn.2024.4.12.

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In the oil and gas industry, understanding two-phase (gas-liquid) flow is pivotal, as it directly influences equipment design, quality control, and operational efficiency. Flow pattern determination is thus fundamental to industrial engineering and management. This study utilizes the Tree-based Pipeline Optimization Tool (TPOT), an Automated Machine Learning (AutoML) framework that employs genetic programming, in obtaining the best machine learning model for a provided dataset. This paper presents the design of flow pattern prediction models using the TPOT. The TPOT was applied to predict flow
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Li, ZiZheng, LiuChen Dai, YiMing Wang, HanLin Qin, JInPing Zhang, and XinRan Yin. "Flight Technology Evaluation Based on Flight Parameters." Advances in Computer and Engineering Technology Research 1, no. 2 (2024): 79. http://dx.doi.org/10.61935/acetr.2.1.2024.p79.

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Based on the flight safety, this paper develops a flight technology evaluation method based on the gradient boosting decision tree ( GBDT ) model by collecting and analyzing flight data. This method comprehensively considers flight parameters and provides a more accurate pilot flight technology assessment tool through the analysis of flight records. It is expected to improve the training plan and enhance the technical level of pilots to further improve the safety and sustainable development of air transportation. By introducing the deep learning network structure optimization evaluation method
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Dissertations / Theses on the topic "Tree-based optimization tool"

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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr
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Book chapters on the topic "Tree-based optimization tool"

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Olson, Randal S., and Jason H. Moore. "TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning." In Automated Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_8.

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Li, Yichen, and Xiaoting Jing. "Optimization Design of Tree Array Space in Pedestrian Street Based on PM2.5 Distribution." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8401-1_42.

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AbstractWind, as one of the important elements of urban microclimate, plays a crucial role in the dispersion of air pollutants. Adequate improvement should be made to mitigate the impact of urban spaces on the wind environment. Green spaces along pedestrian streets are an essential component of the public activity space system, accommodating most outdoor communication activities for citizens. However, the design of existing green spaces along pedestrian streets is often based on past experience, lacking empirical research on the relationship between green spaces, wind environment, and pollutant dispersion. Therefore, the correlation research between tree array space and PM2.5 pollutant distribution in the air, and how to optimize the layout of tree array space by design means according to its distribution regularities, are the main research contents presented in this paper. Taking two important green spaces in Jiefangbei pedestrian street of Chongqing as the sample points for research, this paper adopts the methods of measurement and numerical simulation. Firstly, the pollutant concentration and wind environment in the green space are measured and analyzed, and then use the PHOENICS tool to simulate and compare the wind environment of the green space in the study area before and after optimization design. The results show that the concentration of pollutants is usually high in green space as the greening has aggregation of pollutants to itself; An obvious negative correlation is found between wind speed and the concentration of pollutants, good ventilation is an important means to alleviate the air with low quality; Reasonable design of tree array space based on microclimate can effectively improve the air quality in local space.
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Moore, Jason H., Pedro H. Ribeiro, Nicholas Matsumoto, and Anil K. Saini. "Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)." In Handbook of Evolutionary Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3814-8_14.

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Pommellet, Adrien, Daniel Stan, and Simon Scatton. "SAT-Based Learning of Computation Tree Logic." In Automated Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63498-7_22.

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AbstractThe learning problem consists in finding for a given sample of positive and negative Kripke structures a distinguishing formula that is verified by the former but not by the latter. Further constraints may bound the size and shape of the desired formula or even ask for its minimality in terms of syntactic size. This synthesis problem is motivated by explanation generation for dissimilar models, e.g. comparing a faulty implementation with the original protocol. We devise a -based encoding for a fixed size formula, then provide an incremental approach that guarantees minimality. We further report on a prototype implementation whose contribution is twofold: first, it allows us to assess the efficiency of various output fragments and optimizations. Secondly, we can experimentally evaluate this tool by randomly mutating Kripke structures or syntactically introducing errors in higher-level models, then learning distinguishing formulas.
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Page, Sebastien, Marc Mansuy, Katrien Eloot, Maxim Candries, and Roeland Adams. "Benchmark of Turning Basin Options for the ECMT Class V Network of Nord-Pas-de-Calais (France)." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6138-0_28.

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AbstractThe Nord-Pas-de-Calais Division of Voies navigables de France (VNF), the French Waterways authority, is preparing a master plan for the development of turning basins for its ECMT class V network. In this frame, IMDC has been asked to carry out a comparative analysis (benchmark) of the different designs of turning basins in France and elsewhere in the world and to propose design criteria adapted for future developments (for ships of 110 m and 135 m) on the Nord-Pas-de-Calais network. The first part of the study consisted in a desktop review of available national and international guidelines or recommendations on the subject of turning basins design. This helped building a better understanding of the needs in terms of geometry for safe and/or comfortable turning maneuvers. Technical studies of built or projected turning basins on the Nord-Pas-de-Calais network were also analyzed to refine the knowledge on local conditions. Based on the findings of the desktop analysis, interviews of European waterways authorities, international experts and skippers were carried out to further improve the understanding of the problem, both from the infrastructure side as well as from the users’ side. On top of geometry concerns, the interviews tackled hydrometeorological constraints, operational and environmental aspects, auxiliary equipment, etc. From this, a draft technical reference system was proposed for future turning basins on the Nord-Pas-de-Calais network. It proposes two options, depending on whether the basin is located on two banks or on one bank only, and aims at offering each option in two safety and ease levels: a comfort level version, on which the turn must always be relatively easy, and a safety level version, on which the turn must always be possible, but with less ease. The designs were then tested using real time navigation simulations. Optimizations of some of the designs were also proposed to adapt to severe hydro-meteorological conditions. Finally, a decision tree has been developed to facilitate the selection of a design solution according to the local characteristics of the projected basin location on the network. This tool allows the selection of the geometry allowing the easiest turn possible according to the available space and the hydrometeorological conditions present on the envisaged site.
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Gouri, Mohd Hyder, and Mr Anuj Kumar. "AUTOMATED MACHINE LEARNING: EMPOWERING DATA-DRIVEN DECISIONS WITH TPOT AND AUTO-SKLEARN." In ENGINEERING THE FUTURE: MACHINE LEARNING AND DATA SCIENCE IN PRACTICE. NOBLE SCIENCE PRESS, 2023. http://dx.doi.org/10.52458/9789388996747.nsp2023.eb.ch-03.

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This chapter explores the fast-developing topic of automated machine learning (AutoML), which aims to democratize and streamline the machine learning process. We examine the idea of AutoML and its importance in lowering the entry barriers for machine learning so that people with different degrees of competence can use it. TPOT (Tree-Based Pipeline Optimization Tool) and Auto-sklearn, two prominent AutoML tools, are thoroughly investigated. While Auto-sklearn makes efficient use of Bayesian optimization, TPOT uses genetic programming to automate the development and optimization of machine learning pipelines. For those wishing to employ these potent tools in their machine learning projects, we also evaluate the advantages and disadvantages of AutoML while offering real-world use cases and automation advice.
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Marcozzi, Anthony, Jesse Johnson, Russell Parsons, and Jacob Downs. "Sensitivity of LIDAR Derived Fuel Cells to Fire Modeling at Laboratory Scale." In Advances in Forest Fire Research 2022. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_56.

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Computer models of wildfires are an important tool for fire managers and scientists. However, fuel inputs to wildfire models can be difficult to represent with sufficient detail in a computationally efficient manner. Recent advances in fuel mapping with airborne and terrestrial laser scanning (LIDAR) techniques present new opportunities to capture variation in fuels within a tree canopy and on a landscape. In this paper, we develop a technique for building 3D representations of vegetation from point clouds created by Terrestrial Laser Scans (TLS). Our voxel based approach is capable of representing heterogeneous crown fuels as collections of fuel cells in modern 3D Computational Fluid Dynamics wildfire models such as FDS, QUIC-Fire, or FIRETEC. We evaluated the effectiveness of our technique at different fuel cell resolutions by using the DAKOTA optimization toolkit to compare simulated fire behavior in FDS with observed burn data collected during a series of experiments at the Missoula Fire Sciences Laboratory. The difference between simulated and observed fire behavior was quantified by taking the Root Mean Square Error between the observed and simulated mass curves over time. Our primary finding was that the point cloud derived fuel cells accurately describe observed fire behavior when evaluated over fuel moisture content and dry foliage mass. We also found that the global minimum RMSE persists across fuel cells at each resolution. However, the RMSE gradient increases with fuel cell size. This result implies that while both low and high resolution fuel cells are capable of accurately representing complex fuel distributions in 3D fire models, low resolution fuel cells are more sensitive to changes in fuel moisture content and dry foliage mass. The results of this paper offer fire managers and scientists responsible for simulating fire behavior guidelines for how best to translate LIDAR data to 3D fire models, and what fuel cell resolution can best capture accurate fire behavior. With these best practices in mind, fire managers will be able to generate improved fire behavior predictions for prescribed burns, forest thinning plans, and suppression activities.
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Shedyakov, Vladimir. "CHAPTER 3.1. CATHARSIS AND SOCIO-ECONOMIC DEVELOPMENT: ACTUALIZATION OF OPPORTUNITIES." In International Partnership and Cooperation of Ukraine in Wartime: Collective monograph / edited by D. Nascimento, G. Starchenko. NGO «Research and Educational Innovation Center of Social Transformations», 2022. http://dx.doi.org/10.54929/monograph-02-2022-03-01.

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The main task of the text is to comprehend the possibilities of reviving the economy and society, highlighting the current correlation of the tree of goals and complexes of development tools in the current conditions. The applied methods of research and presentation of the material are based on the unity of historical and logical, analysis and synthesis. It is shown that the conditions that develop when global and national trends are superimposed require fundamental social changes. For their optimization, the main possible ways and directions are highlighted. Particular attention is paid to changing the context of the revival and development of the economy. Conclusions are drawn about the possibilities for improvement at the structural-institutional and organizational-managerial levels. Concrete steps are proposed to improve the environment and management mechanisms.
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Conference papers on the topic "Tree-based optimization tool"

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Javel, Irister M., Rodolfo C. Salvador, Elmer Dadios, Ryan Rhay P. Vicerra, and Antipas T. Teologo. "Epileptic Seizure Detection via EEG using Tree-based Pipeline Optimization Tool." In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ). IEEE, 2019. http://dx.doi.org/10.1109/hnicem48295.2019.9073465.

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Olson, Randal S., Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. "Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science." In GECCO '16: Genetic and Evolutionary Computation Conference. ACM, 2016. http://dx.doi.org/10.1145/2908812.2908918.

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Alinejad, F., D. Botto, M. Gola, and A. Bessone. "Reduction of the Design Space to Optimize Blade Fir-Tree Attachments." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75781.

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The blade attachment, both dovetail or fir-tree, transfers the centrifugal load from the blade to the disc, generating high mean and peak stresses in notches as well as on contact surfaces. Hence, the strength of the attachment is one of the main concern of the designers for improving the performance of the engine and several optimization procedure have been put forward to minimize the state of stress in the attachment for a given centrifugal load. The optimization process is generally driven by a parametric model. The selection of the proper parameters and their variation ranges represent one
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Swantner, Albert, and Matthew I. Campbell. "Automated Synthesis and Optimization of Gear Train Topologies." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86780.

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A method for automating the design of simple and compound gear trains using graph grammars is described. The resulting computational tool removes the tedium for engineering designers searching through the immense number of possible gear choices and combinations by hand. The variables that are automatically optimized by the computational tool include the gear dimensions as well as the location of the gears in space. The gear trains are optimized using a three-step process. The first step is a tree-search based on a language of gear rules which represent all possible gear train configurations. T
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Aranha, P. E., C. O. De Souza, L. F. Ferrarezi, et al. "Look Ahead: Optimization of Well Operations Using Real Time Digital Twin for Well Integrity." In Offshore Technology Conference. OTC, 2025. https://doi.org/10.4043/35617-ms.

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Abstract This study introduces a digital twin forecasting tool for well operations, combining a transient multiphase flow model and a structural model to predict safety factors (SF) for casing, tubing, equipment, and wellhead seal assembly. By utilizing historical and real-time data, the tool constructs dynamic envelopes for future operations (lookahead), optimizing parameters for well shut-in, restart, and bullheading when SFs are unsatisfactory. Real-time pressure and temperature profiles are calculated using a transient multiphase flow simulator, with updates every 20 minutes from gauge rea
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Hajimirza, Shima. "A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells." In ASME 2016 Heat Transfer Summer Conference collocated with the ASME 2016 Fluids Engineering Division Summer Meeting and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ht2016-7306.

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Patterned thin film structures can offer spectrally selective radiative properties that benefit many engineering applications including photovoltaic energy conversion at extremely efficient scales. Inverse design of such structures can be expressed as an interesting optimization problem with a specific regime of complexity; namely moderate number of optimization parameters but highly time-consuming forward problem. For problems like this, a search technique that can somehow learn and parameterize the multi-dimensional behavior of the objective function based on past search points can be extrem
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Siddique, Faisal Anwar, Muhammad Adeel Aslam, Imran Khan, Yasir Abbasi, Zaid Khan, and Syed Tariq Hasany. "Case Study of Successful and Optimal Strategy to Test and Evaluate Three Target Reservoirs in Single Exploratory Well." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211332-ms.

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Abstract A case history of an exploratory well in Lower Indus basin in Pakistan, is described where Massive Sand Deep, Massive Sand Shallow and Basal Sands of Cretaceous Lower Goru formation were target reservoirs, however, during drilling, positive indications were also observed in Sembar formation of early Cretaceous age. Successful formation evaluation of three target reservoirs posed a great challenge, due to bad hole conditions and bad cement job. This resulted in risk of no formation zonal isolation between target reservoirs along with risk of operational issues due to high mud weight. T
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Dong, Chensong, Chuck Zhang, Zhiyong Liang, and Ben Wang. "Tolerance Analysis and Synthesis for Composite Assemblies of Resin Transfer Molded Components." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42466.

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With the increasing demand for composite products to be affordable, net-shaped and efficiently assembled, tight dimension tolerance is critical. Due to lack of accurate process models, dimension analysis and control for resin transfer molding (RTM) processes are often performed using trial-and-error approaches based on engineers’ experiences or previous production data. Such approaches are limited to specific geometry and materials and often fail to achieve the required dimensional accuracy in the final products. This paper presents an innovative dimension variation prediction approach. First
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Wu, Zhengkai, Thomas M. Tucker, Chandra Nath, Thomas R. Kurfess, and Richard W. Vuduc. "Step Ring Based 3D Path Planning via GPU Simulation for Subtractive 3D Printing." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8751.

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In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning ba
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Kongkiatpaiboon, Suradech, and Asit Apornsupavit. "Statistical Causality Analysis: Use Cases In Oil and Gas Applications." In APOGCE 2024. SPE, 2024. http://dx.doi.org/10.2118/221168-ms.

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Abstract Identifying the underlying causes of issues is important yet difficult. Facilities and operation engineers struggle to determine process variables causing operational difficulties. Causality analysis has made significant advancements in recent years. However, the application in oil and gas field is seldom reported. This study examines the application of statistical and machine learning methods for causality analysis in the oil and gas industry. It includes various use cases to demonstrate the effectiveness of these approaches. This study investigates causality analysis techniques and
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