bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital Twin in Oil and Gas Equipment for Predictive Maintenance
Authors: Dehghan M. 
Published: October 2023

Abstract
Digital twins as a virtual model that is designed to reflect a physical system can improve the performance of electrical and mechanical components in industries. With digital twin technology, the industrial organization can be assumed in a way that they implement a sustainable solution with stability and predictability. By using MATLAB and Simulink, in this paper, a digital twin was demonstrated for Electro-Mechanical parts of the drilling for oil and gas industries. Hence, every single element of electrical and mechanical components was defined including the rotational parts separately, then by connecting those parts, the entire process in a closed-loop control system can be controlled. Having this digital twin, healthy data was generated for the situation that the system is working properly, as well as failure data that can be assigned for those components that are prone to wear and failure. Considering a fault in the digital twin in every specific part of the equipment, fault data was generated that can be used for data analysis and machine learning to find a Remaining Useful Life (RUL). To show how it is possible, in the next step, a digital twin for a triplex hydraulic pump that is crucial equipment in the oil and gas industries was investigated, then by having a simulation in healthy and faulty situations the required data were generated. Having data, an RUL was defined by implementing a data analysis and machine learning that can be implemented to have predictive maintenance for the system and it is shown how building a digital twin and having predictive maintenance for our system may boost productivity while decreasing unexpected downtime, which is costly and time-consuming.
The 3W Project and its Strategy to Foster the Development of Data-Driven Solutions for the Offshore Sector
Authors: Vargas R. E. V. , Pinto R. L. A. 
Published: October 2023

Abstract
This paper presents the 3W project and the strategy adopted in contrast to the classical buy and build approaches to develop a data-driven solution which can be used by oil companies to overcome their technological challenges. With this strategy, solutions are developed in a collaborative and symbiotic way with several types of players in the global community. Results so far obtained with 3W are promising, which justifies and motivates other similar projects to adopt the strategy proposed in this article.
Formation Evaluation Enhanced by Cloud-Based Platform – A Presalt Case Study Using Public Data
Authors: Bertolini. A. , Dantas M. , Baines V. C. , Bize-Forest N. , Mullins O. C. 
Published: October 2023

AbstractThe cloud-based formation evaluation platform aims to accelerate real-time subsurface domain insights and enable collaboration with domain experts during operations for faster and better decision-making. The proposed platform quickly creates a 3D near-wellbore reservoir model honoring geological trends that can be updated within minute-to-hours as new data are acquired and interpreted.The cognitive E&P environment caters for real-time applications and near-real-time applications using PRISM and OSDU as their respective cloud architecture. The fast modelling is enabled by an innovative meshless smart structure algorithm that inputs the borehole geology interpretation to create an implicit function at the wellbore, which represents the geological trends, and populated with petrophysical properties using a combination of geostatistics and machine learning methods. The workspace and its content are available for visualization and download. The more complete the shared dataset, the higher the accuracy of the pre-job model.The proposed cloud-based workflow provided a focal point for integration of all advanced well and seismic data streams presented in this paper and a stringent test for present-day reservoir realizations. The 3D contextualization of critical wellbore insights combining rock, fluid, and pressure enhances the real-time and near-real-time capabilities to properly evaluate different formations and productive reservoir intervals. Those capabilities accelerate real-time subsurface domain insights and enable collaboration with domain experts during downhole measurement operations for faster and better decision-making. Operational decisions and data acquisition programs need to be made in an operational timeframe, where derivatives from one or more sensors run may affect decisions for subsequent data acquisition runs in the well and future rig activities.The case study was created with data from the public datasets published by National Petroleum Agency (ANP). Apart from drilling data such as mud-logs, logging while drilling logs, and deviation well surveys, the available wireline well datasets include high quality image logs, dynamic pressure measurement, downhole fluid scanning stations, and advanced petrophysical logs. The data integration of wells was interpreted with the surface seismic providing a connection among the presalt wells for a proper 3D near-wellbore modeling.Data from different sources can now be consumed using the cloud-based wellbore measurement workflow. The coupling of dynamic and fluid data with the facies identification provides a means to incorporate fluid data into a geo-model at early stages of reservoir evaluation. The results of this integrated analysis can be potentially used to develop a geo-scenario, which is the sequence of major events that occurred in the reservoir, both rock and fluid, over geologic time.
Automated External Corrosion Detection for Process Equipment With Ai
Authors: Ferguson Eric L. , Potiris Steve , Castillo Marco , Dunne Toby F. , Bargoti Suchet , Kazzaz Ibrahim 
Published: October 2023

Abstract
Atmospheric corrosion poses the most significant threat to the integrity of offshore Oil and Gas (O&G) platforms in the Gulf of Mexico (GoM). Traditional manual inspection of topside equipment on these platforms is not only expensive, time-consuming, and labor-intensive but also subjective and incomplete, leading to an increased risk of unplanned shutdowns due to overlooked repairs. To address these challenges, computer vision and machine learning algorithms can be employed to detect and categorize corrosion accurately. This approach enables an objective and comprehensive management of corrosion throughout the facility. By identifying and reporting areas with detected corrosion, the system can prioritize high-risk equipment, which is prone to failure and can have severe consequences, for prompt remediation, thereby significantly reducing the likelihood of unplanned downtime. This paper introduces a pioneering AI-based system that revolutionizes corrosion management and inspection processes, specifically designed for offshore O&G platforms. The authors present a case study illustrating the application of this AI-based corrosion management system on a large GoM offshore platform. The practical impacts of this technology on corrosion management are demonstrated, showcasing how machine learning and computer vision algorithms vastly enhance inspection, maintenance, and overall management processes, ultimately leading to reduced operating costs and risks associated with offshore O&G platforms.
Mechanistic Analysis of Various Scale Inhibitors on Calcium Sulfate Precipitation
Authors: Alissa F. M. , Aljeaban N. A. , Tobon-Gonzalez J. , Albaiz A. I. , Alzahrani A. A. 
Published: October 2023

Abstract
The deposition of scales is one of the major challenges in the oil and gas industry. Prevention of such challenge is carried out using chemicals known as scale inhibitors. However, not all inhibitors function the same. For instance, some inhibitors function as threshold inhibitors, while others function as crystal modifiers. In order to investigate this, several experiments for precipitating Calcium Sulfate were carried out in the laboratory by mixing Calcium, and Sulfate brines. Following the precipitation process, the solid was filtered out using vacuum filtration. Subsequently, ion chromatography was used to determine the concentration of sulfate ions present in the solution. Herein, the inhibition efficiency of various phosphonic-acid based inhibitors: ATMP, PAPEMP, and BHMTMP were examined and compared against a baseline blank test. Afterward, the mechanism of inhibition was interpreted from the kinetic experiments. Generally, the performance of a threshold inhibitor tends to plateau. Both ATMP and BHMTMP reached a maximum inhibition efficiency of calcium sulfate precipitation around 30%. No significant change was noticed when increasing the concentrations to 300 ppm. On the other hand, the performance of PAPEMP was non-threshold reaching 53% inhibition efficiency at the same concentration.
Multi-Scale, Multi-Process and Multi-Issue Integrated Geological Modeling: A Solution for Brazilian Pre-Salt Reservoirs
Authors: Massonnat G. , Lesueur J. L. 
Published: October 2023

Abstract
In the field of geosciences for oil and gas, there is a disconcerting question that is not often tackled: could we correctly model and predict what might not have been understood? The answer seems natural and obvious. Therefore, the capability of a geological modeling workflow to meet the needs of correctly predicting reservoir petrophysical properties in the 3D space requires that the geological processes driving the current organization of these properties have been correctly identified and characterized prior to the modeling phase.
In the case of carbonate reservoirs, whether marine or lacustrine, the complexity of prediction is largely linked to the multiplicity of processes that have generated the heterogeneity of the reservoirs. Distributing in a reliable way the final properties resulting from geological history shaped both by different organizational logics and by dependence on successive heritages is an immense challenge with major technical and economic consequences. Claiming to go beyond it, as is often the case in industrial studies, despite a lack of concepts, techniques, tools or time, could appear as a lack of lucidity. But reservoir geologists are sometimes constrained by the "good enough" approach were a lack of global understanding and modelling is weighted with regards to a potential loss of revenues generated by miscalculations of reserves.
Nevertheless, reservoir modeling remains a crucial step in the reservoir appraisal process, especially when limited dynamic data is available. For several decades, this modeling has been based on the use of geostatistical techniques whose strength lies more in their ability to honor the available hard data than in the quality and reliability of extrapolation beyond the drilled zones. It is known that the use of these techniques does not allow the validation of the geological hypotheses underlying the stochastic simulations, and that multi-scenario approaches are usually lacking in the evaluation of reserves and resources attached to discoveries.
A New Method for Torque and Drag Retro-Analysis to Obtain Accuracy to Compute the Friction Factor for Wells in Severe Drilling Scenarios of Pre-Salt Fields
Authors: De Souza Marcio Francisco Alves , Dos Passos Adriano Gouveia Lima Gomes , Fernandes Fernando Bastos , De Lima Flank Melo , Britto Guilherme Augusto Santos , Buzza Joseph Eduardo Harbi , Portugal Luis Fernando Dunley , Soares Matheus Morato , Pereira Virgínio Augusto Moreira 
Published: October 2023

Abstract
New technologies and methodologies for estimating torque and drag (T&D) response during directional and horizontal drilling wells are constantly being researched in the literature. Classical methods developed in the 80s are still used nowadays. However, their approximations involved may incur significant errors during the well drilling design and operations. In contrast, using numerical methods may increase the computational costs of a well plan and result in unfeasible designs. A methodology to determine the friction factor based on retro-analysis of data from drilled wells is presented to support well design in severe drilling scenarios, such as the pre-salt of Brazil. The systematization of the T&D retro-analysis process makes it possible to compose a database and consequently allows to determine the friction factor more accurately, as well as the analysis of the variables' influence on it and results in more accurate decision-making on resource planning and well design, such as: use of power section in the BHA, drill string assembly, maximum casing extension, back-reaming operation, rig type choice and feasibility of wellbore completion. A case study of planning and design in Pre-salt of Brasil is presented, in which the wells have very deep final depths and/or challenging trajectories, the drill string and the rig are subjected to efforts that approximates their operational capacities, even when considering the best on the market. Therefore, in these scenarios, a high accuracy of the friction factors values, used in the design phase, is crucial for planning and execution success. The methodology to determine the friction factor based on retro-analysis of drilled well data results in more realistic and accurate friction factors.
Axisymmetric Displacements of Miscible Fluids in Annulars with Washouts for Applications in Primary Cementing Operations
Authors: Carvalho F. C. , Oliveira R. M. , Author D. E. , Author F. G. 
Published: October 2023

Abstract
Primary cementing is "the process of installing cement in the annulus between the casing and the formation exposed to the well", NELSON (1990). The cement provides permanent zonal isolation to prevent contamination or undesirable fluid migration toward the annulus. It protects the casing from corrosion and provides hydraulic and mechanical stability throughout the oil well productive life. During the drilling stage, irregularities in the cross-section, defined as washouts, can occur caused by partial collapses of the open hole section due to the presence of poorly consolidated rocks in the formation. A successful primary cementing operation will depend on whether the spacer fluid system and the cement slurry adequately and completely displace the drilling mud from the annulus and washouts. Motivated by this industrial problem, the present work performs a Direct Numerical Simulation of the vertical displacement between two miscible Newtonian fluids confined in eccentric annular spaces containing an expansion followed by a contraction. We investigate how different viscosities and densities of the injected and displaced fluids, the miscibility between them, the injection rate, and the dimension of the rectangular washout affect the two-phase flow, and calculate displacement efficiencies.
Our numerical algorithm was developed in C language using finite differences and spectral methods and solves the Navier-Stokes equations with variable viscosity in cylindrical coordinates coupled to an advection-diffusion equation for a scalar field that measures the concentration of the more viscous fluid.
Our results predict very high displacement efficiencies, close to 100%. Although the fluids used in industrial cementing processes are non-Newtonian, the high displacement efficiencies found in the current results motivate the use of spacer fluids as a strategy to control the interface properties. Thus, approximating the field conditions to the ones encountered in the displacement between miscible Newtonian fluids can lead to increases in displacement efficiency. Needless to say, this might have important consequences for the safety and integrity of oil wells.
We emphasize that the axisymmetric displacement between miscible Newtonian fluids leads to very high displacement efficiencies, close to 100%. The calculation of such efficiencies for miscible fluids considers the miscibility between them and is easily obtained from averaged values of the concentration parameter.
Catching Failures in 10 Minutes: An Approach to No Code, Fast Track, AI-Based Real Time Process Monitoring
Authors: Anzai T. K. , Furtado P. H. T. , de Brito G. M. , Santos J. S. , Moreira P. C. M. , Diehl F. C. , Ferreira L. E. L. , Grava W. M. 
Published: October 2023

Abstract
Process monitoring has gained significant attention in recent years due to the need for certain industry sectors to enhance their processes' performance and safety. This development has enabled, more than ever, novel applications in real industrial systems. However, the desire to achieve quick results has led to decentralized and unstable applications that can hinder the long-term scalability and maintenance of these technologies. Moreover, the proliferation of commercial tools in response to the industry's demand for digital transformation has made selecting the right solution a daunting task. To address these challenges, Petrobras developed the SmartMonitor platform. SmartMonitor empowers users to create and configure, usually within minutes, online machine learning and first-principles monitoring tasks using a user-friendly, no-code visual programming framework. This approach ensures accessibility and democratizes the process of task creation and management. Additionally, the platform supports the inclusion of new methodologies and monitoring techniques, making it an integrated development hub aligned with best practices in machine learning models management. Currently, SmartMonitor has hundreds of tasks running in real-time, generating performance indices on critical equipment in Petrobras units. This paper provides a description of some of these tasks, along with an overview of the SmartMonitor platform's structure, its monitoring philosophy and challenges regarding process monitoring in real industrial systems.
Application of Automatic Wellhead Displacement Monitoring System by Computer Vision in Offshore Well
Authors: Teixeira G. T. , Weidlich M. C. , de Souza C. O. , Dias R. , Pestana R. G. 
Published: October 2023

Abstract
This work presents the application of an automatic wellhead displacement monitoring system in offshore wells. The system uses computer vision algorithms applied to ROV videos for estimation of wellhead motion. Obtained results are then compared to BOP (Blowout Preventer) accelerometer data.
The system identifies and quantifies, through tracking algorithms applied to ROV videos, the horizontal and vertical displacement time series of the wellhead. Subsequently, it provides quantitative data suitable for use in a wellhead fatigue analysis and preliminary risk assessment of wells where wellhead motion is observed during ROV inspection.
The results of the analysis of an offshore wellhead displacement using the developed tool are presented, as well as the comparison of this outcome with data from BOP sensors. The results were used both to quantify the criticality of the displacements and to calibrate the soil models (p-y curves) used in global riser analysis.
Comparative investigations show good agreement with BOP accelerometer data.
The novelty of the tool is to automatically quantify the horizontal and vertical displacement of the wellhead using computer vision in ROV videos.
Preliminary results show that the use of computer vision techniques can bring improvements to the analysis process, providing a tool for calibration of numeric models used in well integrity assessments, especially in situations where accelerometer data is not readily available.
Decision Tree Regression Method Applied in Well Clustering: A Supervised Approach
Authors: Vianna I. G. P. , Valladares I. F. , Pacheco C. E. P. , Brito A. C. de A. , Costa F. R. , Mendes D. H. 
Published: October 2023

Abstract
Drilling rigs are an expensive resource in the oil and gas industry; hence, planning their time properly is necessary. Estimating activity durations plays a crucial role in the rigs' planning. The selection of correlated wells to be used in estimation models is vital for good duration estimative. Based on this necessity, we present a regression supervised method to cluster wells. Results were compared with traditional unsupervised clustering methods.
The developed method transforms a machine learning unsupervised problem (without a target) into a machine learning regression supervised problem (with a target). We use a decision tree regression to transform an unsupervised clustering problem into a supervised clustering problem and compare it with the other two methods based on K-means (unsupervised). We apply multiple linear regression with leave-one-out cross-validation in the resulting clusters to evaluate the clusters.
We tested the methodology in eight different scenarios, varying in number of wells (42 to 236) and similarities. All scenarios were based on real wells from a large Brazilian energy company. The target for the decision tree regression methodology was the drilling duration, and the variables were water depth and well footage. We limited the minimum number of wells per cluster, reducing overfitting in the estimation technique. Our methodology reached the lowest mean absolute percentage error value for all scenarios. The decision tree regression methodology can increase the capability of the expert to select similar wells based on their characteristics and duration.
The novelty of the decision tree regression method applied in a clustering problem is to provide the experts with a tool that considers the operational duration, besides the traditional set of relevant variables, transforming an unsupervised problem into a regression supervised problem. With the decision tree regression method, it is possible to cluster wells in more similar groups, improving any further analysis.