bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive Maintenance of Heat Exchangers Using Digital Twin Models Systematic Literature Review
Authors: Huzaifa A J 
Published: October 2023

Abstract
Predictive maintenance plays a crucial role in enhancing heat exchanger efficiency by minimizing fouling-related performance degradation. This systematic literature review examines digital twin models for accurately predicting fouling and optimizing maintenance schedules in heat exchangers. The review covers recent research exploring the application of various digital twin models in predicting fouling in different industries. It also covers how much efficiency can be achieved by implementing prediction-based maintenance. The analysis of existing literature shows that the digital twin models are up to 99% accurate in predicting fouling, remaining useful life (RUL), and other performance parameters. Synthesis of the literature review also reveals that the implementation of predictive maintenance enhances operational efficiency, reducing the cost of maintenance and contributing to a sustainable and resource-optimized future for industries. The research aims to provide practical recommendations for businesses to implement digital twin models effectively in order to enhance heat exchanger efficiency and reduce maintenance costs. Moreover, this review identifies the need for further research to develop a comprehensive digital twin model tailored for predictive maintenance, calibrated using real-world data and scenarios.
Well Hookup Acceleration by X-Mas Tree Prediction
Authors: S. M. , T. K. , A. A. 
Published: October 2023

Abstract
Well hook up is one of the most important steps to put new wells into production. Usually, well hook up starts in an early phase of the project through the engineering phase. This phase will define the well hook up requirements depending on the nature of the well. The well could be an oil producer, a gas producer or an injector well. Based on the well type, different considerations will be adopted in view of the Well design, the drilling rig requirement and the predefined well setup. Once completed, the wells need to be swiftly connected to the production system through a set of instruments and piping hook up spools.
However, there is always an uncertainty about the final hook up spool due to its three-dimensional nature and the requirement of having a physical check (site survey) to initiate the spool prefabrication. This step could only take place after the X- Mas tree installation which will add extra days for the prefabrication and the installation of the final spool and will further delays the start-up.
In addition to that, the fact that we were recently in a worldwide Covid-19 situation with a heavy sanitary protocol with restriction on site mobilization and site POB (personnel on board) reduction, made the situation even worse and contributed to generating more delays on our hookup activities.
Robotics Application in Hazardous Operation and Construction
Authors: Mehta J. , Alshaali M. 
Published: October 2023

AbstractRobotics have the potential to provide significant value to the construction, engineering design, operation, and maintenance activities. It provides a solution to capture required data from complex/ hazardous working environments by means of robotics tools and reduces the safety concerns for personnel by eliminating entry in a hazardous environment.The main objective of this paper is to provide a solution that eliminates the risks of working in confined spaces and in preventive maintenance using robotics technology in dangerous environments. Robotics solutions can minimize risk and provide a method of acquiring site data, performing inspection and maintenance in an autonomous manner.Data acquired can combine the use of robotics with AI (Artificial intelligence) to allow the machine to perform the tasks required accurately without human intervention. Many of the challenges faced at the project site may be resolved when applying this technology to the work environment. Application in plants containing hazardous gases and offshore unmanned environment will benefit from these technologies drastically. It leads to increased reliability of the project while reducing the risk of harsh operating conditions.
Drilling Digital Twin Predicts Drilling Dysfunctions and Performance in Real Time
Authors: Gandikota R. A. , Chennoufi N. , Saxena S. , Schellenberg B. , Groover A. 
Published: October 2023

Abstract
In the new digital age, improving drilling economics with tools like simulation and predictive analytics are key to enabling digital transformation. A novel real time digital twin has been developed to predict drilling dysfunctions and improve operational efficiencies. Real time surface (EDR) data with full physics time domain models are used to predict drilling dynamics and drilling dysfunctions in the bottom hole assembly at any depth. This provides a rare insight for drilling engineers to improve drilling performance and take predictive or corrective measures for reliability and operational efficiencies.
In the study presented here, the digital twin uses real time surface along with the details of bottom hole assemblies and drillstring to predict downhole drilling dynamics responses. Detailed information of the formation, drill string, bottom hole assembly and drill bit mechanics are utilized. A fast-running time domain models based on mixed multi-body mechanics and finite element methods form the basis of the digital twin. The workflow is built to automatically recognize drilling rig states (rotary or slide drilling) and connection makeup to start and stop the predictive model. The real time integration has been tested over several wells for stability and performance metrics. With the integration of real time data, the digital twin systematically predicts ahead the true WOB, bit RPM and downhole MSE. The integration of surface data to real time models makes it a true digital twin.
Next Gen of Field Operation Era for Artificial Lift Real-Time Monitoring: A Method to Significantly Optimize Oil Production of Mature Field
Authors: Hidayat A. , Isnanto S. , Lazuardi H. , Kurniawan C. , Kusuma H. , Ramadhan G. B. , Santoso H. B. , Hesti K. , Handarianto H. 
Published: October 2023

AbstractBlock X was one of the oil blocks in South Sumatra with more than 100 wells. It was previously monitored manually by the operator. Considering the cost and feasibility, IoT surveillance and optimization were implemented. This paper discusses the benefits of IoT optimization systems implemented in mature field.The IoT optimization system includes wells such as ESP wells and SRP wells. Each well was already equipped with its control system. This project integrates the existing controllers with simple equipment that read data from the controller and transmits it to users. To the current system, automation culture was introduced. Dense data that has been collected from the controllers remotely were used to implement quick optimization analysis to give a responsive solution unique to each well. The benefits of these methods were closely observed and analyzed to determine whether the project has a long-term benefit going forward.Three types of improvement were recorded during the project. Enhanced production due to well optimization, uptime maximization due to quick response, and operation efficiency by implementing automation system to the wells. With the optimization system, wells could be quickly identified which can be optimized. Operator could hasten the process by using a workflow that captures important parameters to be observed for optimizing the wells. Well's uptime was maximized by implementing a well down alert system. Alert system that was implemented included well rates and well location for operators to decide which well needed to be prioritized. This system managed to reduce loss oil potential (LPO) by 25% compared to the previous state where IoT optimization system was not applied. The implementation of automation for each well erased the need of manual visits by operators. Trips by operators were lessened by a significant amount and now could use their time for other critical wells.The project managed to save over $500,000 and gain production equal to the value of $680,000 per month with the addition of possible gain up to $850,000. Cost-benefit of the project showed that the moderate cost invested at the beginning quickly was overshadowed by the benefits given in the following months.This paper serves as a case study for future reference on the benefits of implementing IoT SCADA to one's field by integrating existing assets. Relatively low investment and the simple method of implementation would serve as a guide for others to embrace IoT surveillance and optimization system.
Successful Deployment of Unmanned Survey Vessel for Offshore Project - Opens New World of Opportunities
Authors: Srinivasan Ashish , Athmaram Jayaprakash , Shamsi Marwan Al , Seiari Abdul Hakeem Al , Bouremel Djaafar 
Published: October 2023

Abstract
ADNOC is in the process of connecting Abu Dhabi's offshore sites to onshore electricity grid by the year 2025, through subsea transmission network that is expected to reduce costs and replace gas used for power generation. As part of the project tender, ADNOC was required to submit Geophysical Seabed Survey for two cable routes from Abu Dhabi mainland to target offshore sites (approx. 180km). ADNOC Offshore survey team were assigned with the task. Capitalizing on its technical industrial partners, ADNOC Offshore took the initiative to deploy several surveying technologies with one being deployed for the first time. An Unmanned Survey Vessel (USV) was deployed for one of the challenging nearshore segments of the route (for 24km) for the very first time in UAE waters for engineering design requirements. The unmanned hydrographic survey vessel was remotely controlled using telemetry solution including Domo Tactical Communications (DTC) radio and 4G communication system to overcome several onsite data acquisition challenges, which enabled project delivery for fraction of a cost while complying with high quality standards including operational flexibility for very shallow areas and absolutely no exposure of personnel. The company not only managed to deliver the entire geophysical data requirement on time, but also introduced a new technology that will open a new world of potential applications in the marine operations like surveillance, patrolling, geophysical surveys, light cargo shipping over short distance, etc. This will bring sustainable efficiency and effectiveness to marine operations and substantially save operational costs on large-scale implementation.
Using Multilinear Regression to Predict Key Engineering Quantities for Estimation in EPC Projects
Authors: Banotra R. , Peace S. R. J. 
Published: October 2023

Abstract

Objective
The Engineering, Procurement and Construction (EPC) industry has been slow to adopt data-driven Artificial Intelligence (AI) and Machine Learning (ML) systems. Decentralized data storages make data engineering a challenging task, highlighting a recognizable shortage of harnessing data from these data storages, and using ML to optimize estimations for large EPC projects.
Empowering proposal teams by using ML-based tools on the engineering disciplines data from previously executed projects can help them make better cost estimates and more informed decisions during project execution.

Methods, Procedures, Process
The McDermott AI team and domain Subject Matter Experts (SMEs) worked together to improve the process of using initial bills of quantities (BOQ) / Material take-off (MTO) for cost estimation during early stages of the project bidding. We combined the knowledge of EPC engineering experience and data into a real-life application - a ML-based multilinear regression application to predict MTO quantities for different engineering disciplines using MTO data from previously executed projects within the company. The database was developed by compiling MTO data from past projects and applying feature engineering on it. An example material quantity prediction is in Figure 1.

Results and Conclusions
As organizations look to modernize and optimize processes, ML is an increasingly powerful tool to drive automation. Unlike basic, rule- based automation—which is typically used for standardized, predictable processes—ML can handle more complex processes and learn over time, leading to greater improvements in accuracy and efficiency. The developed ML-based software can help proposal teams to improve initial cost estimates for engineering disciplines key quantities. In addition to this, in the paper we also share our experience and lessons learnt on how to prepare database from decentralized data banks, and how we can pre-process this data for our machine learning model.

Novel/ Technical contributions
The main aim of the developed ML solution is to harness the power of EPC project data and experience to improve work hour or cost estimation for engineering disciplines. The model has been developed using data from different types of EPC projects executed all around the world. This also presents a case on why and how companies can best utilize the data which is usually stored in decentralized data storage. The solution can assist proposal teams to make better estimates for scope of work based on the type of project. This product could be potentially applied to any EPC company working on oil & gas or petrochemical plants. It could also motivate other practitioners and researchers to replicate the experience to develop a similar tailor-made ML solution for different industries.
Enhanced Explosion Risk Management Through Ex-RBI and Data Analytics: Implementation Strategies and Case Study
Authors: Harris Raja Fernaliz Raja , Zaharin Muhammad Naiem Naqiuddin , Yahaya Ahmad Fairuz 
Published: October 2023

Summary
A Risk-Based Inspection (RBI) framework for electrical equipment for hazardous areas (EEHA) by using Analytical Hierarchy Process (AHP) tool was presented in this paper. A case study at a facility with 789 electrical equipment operating in explosive atmospheres was conducted by analyzing the EEHA's risk-based inspection (Ex-RBI) results to determine the equipment's explosion risk. The results indicate 6% of the equipment has a high explosion risk. On the other end, 66% of the equipment falls into the low-risk category. Our analysis found that implementing Ex-RBI and data analytics strategies can significantly enhance explosion risk management in industrial settings. Through this study, we demonstrated that Ex-RBI can effectively identify and prioritize high-risk equipment and areas, allowing for targeted risk management efforts. Based on the explosion risk of each of equipment, a strategy can be developed to decide on the prioritization for maintenance activity, the interval of periodic inspection, and the grade of inspection for the next inspection cycle. The case study demonstrates enhanced explosion risk management leading to operational excellence by leveraging Ex-RBI and data analytics. Facility owners can identify and mitigate potential risks before they become critical. Over a period, this leads to reduced explosion risk, a more cost-effective maintenance programme and improved overall safety.
Field Development Opportunities Unveiled in Low Resistivity Reservoirs, Sub-Andean Oil & Gas Reservoirs
Authors: Bustos U. D. , Quiroga D. , Tapia O. , Mosquera C. 
Published: October 2023

Abstract
Driven by strong initiatives of leveraging hydrocarbon production in Bolivia and reducing imports, YPFB-Andina started assessing development opportunities in Devonian, Cretaceous and Tertiary rocks. From a textural point of view, these are fine grained sandstones with variable contents of quartz, feldspars, low clay content and porosities in the 15% range. Conventional induction logs response exhibits low resistivity (below 10 ohm.m), meanwhile advanced triaxial induction confirms that electrical properties are mostly isotropic. Consequently, despite hydrocarbons being shown while drilling, conventional petrophysics suggest these reservoirs are water bearing. With the aim of ensuring the real potential hydrocarbon occurrence, we incorporate hydrogen-independent fast neutron cross section (FNXS) and total organic carbon (TOC) from a slim spectroscopy device and transverse relaxation times distribution (T2) from a pad-type nuclear magnetic resonance tool.
Design and Testing of Al Enabled Non-Radioactive Multiphase Fraction Meter
Authors: Akram Karimi Muhammad , Arsalan Muhammad , Shamim Atif 
Published: October 2023

Abstract:
Multiphase flowmeters (MPFM) are commonly used in O&G industry to monitor & optimize production in real time. However, existing multiphase flow meters are either intrusive, radioactive or don't cover full range without requiring frequent calibration. Gases have much different density than liquids and the mixture density is linearly related with the fluid fractions over the full range but the devices to measure density either possess moving parts or rely on radioactive sources (e.g., gamma rays), which are hazardous. On the other hand, some non-density based MPFM relying on dielectric properties of multiphase mixture can be implemented without moving parts, but the dielectric properties have highly non-linear and non-monotonic response with respect to salinity of the produced water, making it extremely complicated to implement. That is why, existing dielectric based MPFM also use a radioactive source specially to measure gas volume fraction (GVF). This paper presents a novel non-radioactive microwave DMOR technology which solves the complex non-linear problem with the use of a digital twin model and custom designed AI algorithm. This unique approach provides a non-intrusive and non-radioactive solution to reliably measure the multiphase fractions without being dependent on fluid mixing conditions.
From Conventional Dips to Geological Features Mask Generation for Advanced Borehole Analysis
Authors: Petrov A. , Belayouni N. , Belouahchia M. , Xin L. , Fan Y. , Leseur N. 
Published: October 2023

Abstract
The Al-based identification of borehole images by means of supervised learning techniques has recently made significant progresses to automate some of the routine interpretations carried out by borehole geologists. While deep-learning approaches have the power to significantly trim down repetitive gee-tasks, they also require a large number of annotations to accurately assist the practitioner in its day-to-day work. When it comes to borehole image interpretation, typical annotations come into the shape of an idealized sinusoid lines delineating sedimentary or structural objects such as the boundary between two geological beds. In view of extracting petrophysical and geomechanical properties at the bed level for subsequent net pay analysis, industry-standard dip picking interpretations can be used to guide the time-consuming contouring of each bed by means of customized segmentation approach.
To start with, patches of the entire borehole image are extracted around the areas where an interpreted sinusoid is available. Then, for each individual patches, a map of the distance between each pixel and the latter sinusoid is calculated. Acting as weighting spatial property emphasizing the most predominant foreground feature, such distance-based map is subsequently added as an additional channel to each image patches. Finally, the latter image patches are used as inputs to train an instance segmentation algorithm generating binary masks for each geological beds and comparing them to a set of subject matter expert interpretations.
Overall, the new method succeeded in automatically segmenting geological beds from commonly available industry-standard borehole interpretations with more than 80% accuracy on average-to-good image quality intervals. While this type of technology significantly alleviates the need for time and cost-intensive manual labeling, it is also seen as a promising approach to implement advanced semantic search of geodata bases as well as a more robust and systematic net pay analysis of thinly laminated low-resistivity reservoirs.
While Al-ready data vs. Industry-legacy data have not been designed to be mutually convertible, recent advances in the field of computer vision provide a solution to preserve the value of past information for nowadays Al-training purposes. Though the current method augment modern-day BHI SMEs with a digital companion for repetitive tasks, human-in-the-loop is still required to handle around 20% of the work depending on the quality of the well. The current techniques was developed in a limited time period and could be improved through grid search optimization, better filtering of input images and dip interpretations. This work aligns with current trends in the field of Al, trying to alleviate the traditional need for extensive human labelling to feed supervised learning models. Other future areas of research to look at self-supervised and few-shots learning.
Experimental Investigation of Hydrocarbon and Non-Hydrocarbon Gas Injection in a Tight Danish North Sea Oil Reservoir
Authors: Mokhtari R. , Talaei A. , Feilberg K. 
Published: October 2023

Abstract
Lower Cretaceous oil reservoirs in the Danish part of the North Sea, are generally deep, with high porosity and very low permeability rock, which makes the applicability of any EOR method and any reservoir development plan challenging. One potentially feasible enhanced oil recovery (EOR) method is the gas injection, where the produced gas, flue gas, and CO2 have sufficient injectivity to be the candidates. Therefore, this study aims to assess the applicability of gas injection as an EOR method for the challenging Lower Cretaceous oil reservoirs in the Danish North Sea. To exclude the effect of rock quality on recovery results, a single core plug from the Lower Cretaceous Tuxen formation without any open longitudinal fractures was selected, cleaned, and re-used for all experiments. The clean and dry core was saturated with formation water, and after that, the irreducible water saturation was achieved by displacing the water with dead crude oil. The dead oil was then displaced by live oil after four weeks of aging. The gas injection experiments afterward were conducted at two different pressures, 250 and 350 bar (current and initial reservoir pressures, respectively), and the reservoir temperature of 85 °C. During the experiments, effluent fluid density, differential pressure response across the core, and effluent gas flow rate are recorded in addition to the recovery data. This makes it possible to evaluate the two/three-phase flow characteristics accurately. The initial scenarios investigated are hydrocarbon gas injection, in this case, synthetic natural gas, at two different pressures. The results show that the recovery factor for the hydrocarbon gas injection is significantly higher at 350 bar (at near miscible condition) compared to 250 bar. Conversely, Since CO2 is at the supercritical condition at both 250 and 350 bar at 85 °C, and is fully miscible with oil at both pressures, no significant difference is expected at different injection pressures for CO2. Unlike, flue gas is immiscible at both pressures, showed a significantly lower recovery factor compared to natural gas and CO2. However, comparing the efficiency of hydrocarbon gases and CO2 is of great interest for the choice of gas for injection. The produced hydrocarbon gas might be most readily available, however, CO2 injection includes the potential for storage of the injection gas.