bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing a Hybrid Digital Twin Approach in a Crude Unit Operation
Authors: Sama Rubio Sergio , Giménez Toni 
Published: October 2022

Abstract
Over the past decade, Machine Learning or, more generally, Artificial Intelligence, has made a stelar entry into the O&G world and is today routinely used from exploration all the way up to retail. At the same time, physics-based tools are still being seen as key for value generation and the O&G community is increasingly looking at combining "traditional" with "new" technologies in what has come to be known as "hybrid" tools. In this paper we explain how we Baker Hughes, C3 and KBC are combining Physics and Machine Learning to create Hybrid Digital Twins that help operators improve their margins, reduce their emissions and, in general, position themselves for sustainable growth.
Before they are in operation, plants (and oilfields alike) are typically simulated using physics-based tools. In practice, therefore, physics-based simulation models pre-date any plant data. Once plant data starts being produced, however, it becomes clear they do not match exactly the theoretical models that were used before operation. It also becomes clear that plants are operated in a very narrow range, due to quality, production and HSE requirements.
In this paper we document a hybrid approach for a digital twin used to generate optimized targets to a Crude Unit operation: synthetic data has been generated to overcome the limitations of available plant data. These synthetic data have then been used as additional training input for ML models, complementing instrumentation data, while at the same time, instrumentation data is used for calibration of the physics-based model.
This hybrid approach has been applied to a Crude Unit, in an optimization use case, with remarkable results. The final goal was to make energy optimization targets to operations through its machine learning algorithms which are based on two years of historical data optimized by a rigorous process simulator. The targets are manually downloaded to the unit multivariable controller by the operators. The hybrid approach has been found to combine the benefits of each type of technique: First Principles provides a sound bases for (limited) extrapolation, while Machine Learning ensures the First Principles models remain tuned to the reality of the process and complements the physics in areas where it is insufficient to model the reality (as is the case of equipment performance degradation/failure prediction). The range of what-if studies (and, correspondingly, optimization options) is thus radically extended.
This paper shows incremental benefits in optimization cases illustrated by a Crude Unit use case, resulting from the application of a hybrid digital twin using both physics-based and machine-learning models. The hybrid approach allows optimizers to evaluate scenarios beyond the historical operating envelope and opens the door to incorporating equipment condition considerations.
Takamul: A Digital Solution to Manage New Oil Performance from End-to-End
Authors: Al Shabibi Hilal , Ghulam Junaid , Al Azri Nasser , Mohammed Mushtaq , Sheheimi Awadh , Al Khabouri Khalid , Al Raisi Amina 
Published: October 2022

Abstract
In any E&P organization, drilling new wells is part of field development activity. This allows company to bring into production additional oil from different fields. Number of new wells drilled varies from company to company from a few to hundreds as in case of Petroleum Development Oman (PDO). Drilling new wells is a capital-intensive activity. Contribution to the overall production from new wells makes for higher returns on investment.
New oil production, is an integrated activity involving a number of departments and different stakeholders. Information and data come from different sources and has to be validated by different stakeholders.
Over the years, this has been done in a traditional manner with smart use of spreadsheets. However, with increase in activity, this way of working became cumbersum and time consuming. In business planning, it is important to select an optimum set of activities for drilling which will generate most new oil from allocated resources and budget. It should also be able to quantify opportunity oil in changing requirements. This means generating alternative sequences and comparing them against a set of KPIs and constraints. This exercise is required to be carried out within a cluster and across different clusters in a business unit. With the use of smart spreadsheets, this was getting difficult and time consuming. Resulting in suboptimal drilling sequence and missed opportunity to increase New Oil production.
Enabling Multiple Digitalization Opportunities Using Latest Wi-Fi 6
Authors: Langford William Louis , Al Kalbani Ayman Khalifa , Al Kaabi Ahmed Ali , Hassan Ahmad , Arce Rodel Francisco , Koodali Rajkumar , Chaudhary Kunal 
Published: October 2022

Abstract
The objective of the proposed paper aims to demonstrate an innovative approach and the value of retrofitting a plant area, where there is lack of adequate cellular coverage, with the latest Wi-Fi 6 wireless technology on an IT network. This helps to enable multiple use cases such as real-time headcount, asset tracking, and connected workers accessing key applications and video communication using tablets, mobile devices & digital wearables that requires high bandwidth and speeds, on an existing site and in a secure manner.
A Novel Approach to Building an Autonomous System that Enables Predictive Maintenance for Compression Systems
Authors: Cao Gaojing , Wang Xiangzeng , Kang Jinlong , Liang Quansheng , Xie Yundong , Williams Paul , Kartoatmodjo Gayatri , Henson Richard , Florez Fabian , Cui Jining , Fendt Andrew , Mu Xiaotao , Wang Lang , Yang Wan , Zhang Bo , He Jianping , Zheng Haibo 
Published: October 2022

Abstract
Big data and analytics are reshaping asset management workflows in the oil and gas industry, and the speed of implementation is accelerating. In many assets, the gas compression system is the core element to drive the process flow from one point to another and, is critical for maintaining the uptime of an asset. However, performance data often shows that compression systems have a high frequency of failures due to the complex assembly and high-speed moving parts. Therefore, the ability to monitor and predict the operational status of a compression system will benefit the asset in three ways: improve operational safety and reliability, increase the mean time between failure (MTBF), and reduce system downtime.
This paper presents a novel approach to building a predictive workflowto do just that, using a case study of a compression system deployed in a gas field surface facility to illustrate the development process and demonstrate the benefits. The workflow presented covers data collection and selection,cleaning, preprocessing, and prediction of compression system health status through machine learning (ML).
Digital Twin and Big Data Technologies Benefit Oilfield Management
Authors: Lai Wenhua , Zhang Hu , Jiang DaWei , Wang YanHui , Wang RongDe , Zhu Jun , Chen Qiulin , Gao Yuan , Li Weixiang , Xie Dengpan 
Published: October 2022

Abstract
The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the operators are facing much more challenges in oilfield management especially how to improve their decision efficiency and situation awareness.
Thanks to the different sensors we deployed on oilfield from drilling to completion and production, tremendous data contributed to the digital asset we are having now. The digital twin makes oilfield management much easier than ever before, hundreds of wells’ performance could be displayed in front of the decision maker or key management level of oil companies, and big data technique helps them get easy understanding of real time behavior on well construction progress, cost management, pain spot of each project. Combining these two methods, it is possible to have an up-to-date awareness of oilfield development status and perceptual intuition to very detail situations.
There is a major operator manages over 200 wells per year and some of these wells are challenging exploration well with measured depth over 20000ft which requires experienced team to get the well to total depth, also a lot of shale gas wells with lateral intervals over 8000ft which demands intensive control of cost. All above operations or targets need be done under a safe and efficient way, then the management team taking digital twins to monitor the real time well status which help them get up to date information about whole oilfield status like drilling, completion, production and more. Big data analysis is also used to help enhance the decision- making efficiency and overcome puzzles that traditional method could not solved, like recommending the best practice way on well construction engineering parameters, or ROI (return on investment) assess. The oil company could achieve a better management level with less human resources and much more workload.
By the advantages of digital twins and big data analysis, the oil company now managing more than 200 drilling rigs and 300 completion wells in the high efficiency way, and now involving the production wells into next phase digital construction target. Furthermore, considering develop an integrative digital twin of geology and engineering map which get whole formation and well construction more intuitive. Besides, it is proven that digital method like digital twins and big data technique could improve the skill of oilfield management significantly, which optimized the resource and expenditures investigated in modern oil and gas industry.
Digital Integration to Realize the Limit (RTL) in the Giant Greater Burgan Reservoir
Authors: Dashti Qasem , Abdulrazzaq Hanan , Al-Shammari Nouf , Al-Habashi Haytham , Al-Rumaidhi Meshal , Franco Francy , Muhammad Yaser , Prakash Roshan , Iheanaetu Samuel 
Published: October 2022

AbstractKOC-SEK asset has the main goal of maintaining, increasing, and optimizing the production of the Greater Burgan, the largest clastic reservoir in the World. Discovered in 1938, Greater Burgan is a multilayer formation with large hydrocarbon volumes of different oil type (from heavy to light) for which a detailed characterization and exploitation strategy was required to develop a sustained asset production.With the asset facing multiple challenges to maximize recovery and sustain target production field rate, one of these challenges has been to optimize the segregation of a large number of wells into 15 gathering centres. This is to ensure de-bottlenecking and to address back-pressure issues of the production networks while flowing wells in their optimal operational envelope to avoid the rapid depletion of the reservoir and the increasing water production.Due to the vast number of wells, the intricate network, the limitations in the gathering system and the dynamic operations, maintaining and sustaining the reservoir performance requires continuous surveillance, rapid and robust understanding of the different variables at reservoir, well and network level to make timely decisions and maximize reservoir recovery. To support this, a standardized and integrated system based on data analytics and numerical models, has been developed in the context of Realize the Limit (RTL) of the Greater Burgan reservoir as a tool for the quick identification of potential oil gain opportunities, the evaluation of bottlenecks across all the production system, and track and measure the impact of different proposed operational and development scenarios to unlock potential production and support optimum production forecasting.The system has been developed in a digital framework, with inputs from a multi-disciplinary team to define the datasets, calculated parameters and visualization requirements related to reservoir properties, well completion status, operational parameters, and key indicators from subsurface and surface hydraulic models. These has been integrated to identify key performance issues involving several aspects of the reservoir's development and operational plans. Also, the process provides a high-level overview and a platform for all asset groups including management, field development and operations, where observing the same set of results can initiate collective decisions that improve wells, network and facility management and enhancement recommendations.
Enhancing Drilling and Production Through a Digital Twin Methodology
Authors: Thomas Philip Titto , Ziatdinov Sergey 
Published: October 2022

Abstract
In recent years digitalization has become one of the fastest growing disciplines in the oil and gas industry. Digital solutions reduce project costs, improve personnel and process safety. However, underestimation of the deployment complexity can also bring severe damage to the project execution, unnecessary delays, and ramp-up costs. To minimize negative effects and smoothen delivery process digital twins provide for oil and gas the opportunity to create a digital representation of the equipment, processes, and people behavior.
Drilling is a very capital-intensive part of the oil and gas industry. It usessophisticated heavy machineries and typical rig contains various equipment from different vendors.Moreover, drilling process by itself has a lot of uncertainties and depends on human behaviors. Thismix makes most service and operating companies very cautious in introducing new technologies.Therefore, any solutions that can smoothen delivery process and minimize or even avoid any hitchesduring deployment bring a lot of benefits to the industry. Digital twin methodology is an ideal way toaccelerate new technologies deployment through offline full-scale digital testing.
The paper highlights efforts to assist operators and drillingcontractors to create a methodology to optimize usage of their assets and improve their processes, using a combination of Systems Architype model and Systems Development lifecycle (SDLC), both well-established methodologies for systems development. Thefocus is on performance improvement, non-productive time reduction, oil and gas productionmaximization. Through emulating rig equipment, drilling process and personnel behavior it is possible toidentify the most critical parameters which guarantee reaching technical limits. Application of digital twin isan iterative process and typically includes several steps. Starting with setting targets, like negative impactreduction of the drilling process on subsequent production. Following by determination of the optimaldrilling parameters based on either historical or modelled data both from drilling and production. Theseparameters are the basis for the new drilling methodology. All deviations from existing drillingmethodologies and generic programs must be carefully analyzed and implemented. Finally drillingmethodology built using digital twin is ready for implementation in real world. The received data is goingto the next iteration to improve the methodology. This approach allows us to train personnel for field specificsituations without risking expensive mistakes in the real world.
Real-Time Enabled Digital Technologies Solution to Deliver Consistent Performance in Gas Wells Construction
Authors: Barrera Edison , Nunez Andres , Corona Mauricio , Dorantes Arnott , Atriby Kamal , Aswal Uttam , Ponomareva Daria 
Published: October 2022

Abstract
The oil and gas industry has constantly evolved riven by data management and technology application. Although the first well was drilled over 150 years ago, the last decade has shown a huge step change leaning towards digital technologies and automation dependency. The main purpose is to leverage from field experience in multiple disciplines, enable quick data transmission for accurate decision making and automate repetitive processes, aiming to reduce lost time, increase performance and ensure consistency.
A multi-rig integrated project of gas wells construction in a field in the Middle East has successfully introduced selected Real-Time digital technologies connected with rig automation features and, along with standard practices developed through years of experience, has proven consistency assurance delivered by the implemented solution. Fully automated technologies combined with high-frequency Real-Time data generators and the latest generation of algorithms that model downhole conditions precisely, further enhance drilling performance, and allow faster and more reliable detection of non-desirable downhole events.
This paper describes the strategy to combine fit-for-purpose technologies which deliver digital inputs working in communication with advanced digital technologies to deliver a solution for multiple stages of well construction operations to increase performance, reduce major risks, ensure procedural adherence and well delivery consistency.
Gap to Potential Identification through An Online Process Digital Twin
Authors: Kumar Suresh Machingalthodi , Al Mahmoud Mahmoud Abdul Hameed , Al Yahyaee Nawal 
Published: October 2022

Abstract
An application has been deployed to identify the gap to potential and provide actionable intelligence to the plant operators on a real time hourly basis to maximize the value-added products and minimize energy consumption in a natural gas processing plant. An online digital twin based on thermodynamic models coordinating the underlying multisite Advanced Process Control (APC) applications, has been implemented enabling multisite gap to potential identification.
Model Predictive Control has been for decades the de facto technology adopted in the oil and gas sector to optimize the process operations and improve yield. This falls short of identifying the unconstrained regions of optimization. A fit for purpose online thermodynamic model was developed for the entire NGL value chain. This is then mapped with the underlying APC limits as bounds. A data reconciliation algorithm coupled with a stead state detection algorithm creates first the exact replica or digital twin of the process plant. An optimization algorithm then identifies the true gap to potential and provides the advisory targets.
The application solution is scheduled to run every hour mapping the real constraints of the plant in real time. This ensures that the optimized targets provided are realistic in nature and is within the safe operating limits of the plant. The online digital twin resulted in the following:
➢ A true representation of the plant with the mass and energy "balanced" across the plants on an hourly basis representing the true operation of the plant. This has resulted in the identification of suspected process measurements across the plant. ➢ This digital twin has enabled monitoring of the true representation of the different efficiency parameters like the polytropic efficiency of compressors, heat exchanger fouling etc., which can further be used as an input to Machine learning algorithms for the calculation of remaining useful life. ➢ Dynamic calculation of the energy consumption and setting up of the dynamic baseline as against static baseline for energy KPIs ➢ The optimization algorithm further provides advisory for identifying the optimized targets, which on implementation will enhance the yield with reduced specific energy consumption. ➢ Identification of the hardware constraints of the plant with estimated dollar value impact for unit change in that constraint enabling debottlenecking studies.
This is a first of its kind implementation, in a geographically separated multisite operations. In an online environment, this digital twin can provide the optimized advisory targets, resulting in increased revenue and an attractive ROI. In an offline environment the same solution can be used for what if analysis and debottlenecking studies. Buoyed with the success of this project, there is a plan to scale up, integrating the upstream and downstream operation.
Monitoring and Troubleshooting of Subsea MPFM by KPIs and Virtual Metering Application in Offshore Environment
Authors: Ursini Fabrizio , Torsello Antonio , Fiorita Simone , Akinmade Abimbola , Ciriello Giuseppe , Vignati Emanuele , Tumiran Tumiran 
Published: October 2022

Abstract
This paper presents the application of real time monitoring techniques on production allocation in a complex scenario, focusing on definition of dedicated KPIs to identify possible issues in flowmeters installed at well level. The analysis includes a comparison with a dedicated Virtual Meter system.
Real time production allocation is monitored by means of a dedicated KPI dashboard, detecting any possible drift in Multiphase Flowmeter (MPFM) measurement processes. The ratio between reference measurements and MPFM readings is calculated for assessing allocation quality. Furthermore, an independent virtual flowmeter (VM) technique was developed to estimate well production rates and compare them with the MPFM flow rates. The VM relies on analytical models (Vertical Lift Performance) to correlate real time pressure losses in the tubing with flow rates. KPIs monitoring and VM are developed in a digital environment available to the engineers operating the field to provide a quick problem detection and a promptly response.
During the initial production phase, a good match between subsea and topside total MPFM readings and daily production figures resulted in a robust production back allocation. This trend was confirmed by the dashboard of allocation KPIs being within the acceptable thresholds. The Virtual Metering system was calibrated against MPFM rates during this initial production phase. After one year of production, KPIs showed a drift between the total subsea and topside MPFM readings, while a good match was achieved by virtual metering, topside MPFM and daily fiscal production. A well by well comparison between VM and MPFM installed at wellhead allowed to identify reliable MFPM and pinpoint the ones requiring retuning. In the meantime, during the troubleshooting process and actions implementation to restore the MPFM functionalities, the virtual metering estimations were used in the production back allocation process.
A combined application of the Virtual Meter and the KPIs monitoring enabled us to improve the quality of back allocation relying on the best type of well production estimation available at each phase of production. The methodology described in this paper entered the daily routines followed by engineers operating the field.
This paper shows the benefits of the digital platform for asset monitoring, as well as the technology that allowed to set up a cooperative environment. Sharing real-time data and model based estimation for a multi-disciplinary team, from Reservoir to Operations, facilitated working together to achieve faster trouble shooting and optimize production performance.