bluuebunny/crossref_metadata_2025_split huggingface dataset:
GHG Reduction in Gas Dehydration Using IR 4.0
Authors: Al-Aiderous Abdullah Younis 
Published: October 2022

Abstract
The objective of this paper is to showcase the successful and innovative deployment of IR 4.0 Technology in hydrocarbon gas dehydration with the goal of reducing greenhouse gas (GHG) emissions in the Tri Ethylene Glycol (TEG) regeneration systems reboiler fired heaters.
Conventionally, Direct and indirect fired heaters are utilized in gas dehydration systems Glycol regeneration to increase the temperature of the water and glycol mixture in order to vaporize water and increase glycol purity, the subject fired heaters utilize a combustion process that incorporates the usage of a mixture of air and natural gas which is in turn generates the required heat and enthalpy. In the majority of these applications high green house gasses (GHG) are dispersed to the atmosphere due to inefficiencies in the combustion process. In addition, high energy losses, equipment life time premature depletion along with significant financial losses associated with unnecessary capital and operating expenditure have plagued the conventional Tri Ethylene Glycol Dehydration and regeneration systems.
In this paper, the successful deployment of the patented IR 4.0 methodology in gas dehydration system fired heaters to reduce greenhouse gases will be discussed along with a comprehensive data analysis of the deployment trial test and success story that led to a significant reduction in greenhouse gases, minimization of energy losses and prolonging of equipment lifetime.
A Dynamically Reconciled Digital Twin for Operations Optimization and Decision Support
Authors: Thorpe Patrick 
Published: October 2022

Abstract
Operational digital twins bring together digitalization technologies, including machine learning, IIoT, data analytics, process simulation and optimization. A digital twin model reconciled with real-time process data provides the foundation for a range of layered applications that are key to transforming the way that process plants and value chains are operated and managed. After outlining the basic numerical methods, this paper will describe three industrial applications of dynamically reconciled digital twin models in gas processing, refining and olefins production. The focus is on application robustness and high availability, delivering increased operating margins and plant flexibility.
An operational digital twin model can provide a versatile tool for real-time process optimization, and what-if analysis, as well as operations planning and scheduling. To be effective, the digital twin model needs to be reconciled with real-time process data frequently. In a dynamic operating environment where feed composition, process conditions and product demand may change continuously, this challenging data reconciliation task is solved using a combination of numerical methods: Equation-oriented model structures and solvers provide the flexibility required to solve simulation, parameter estimation and optimization cases. Extended Kalman filter and moving horizon estimation enable reconciliation using real-time dynamic data without the need for steady-state operation. Process models are developed using both first principles and data-driven surrogate modelling methods.
A dynamically reconciled digital twin model provides the basis for an equation-oriented multi-period optimizer that can be solved for facility-wide steady state optimization, short-range dynamic optimization and long-range planning and scheduling problems within a single unified modelling and optimization framework. The use of hybrid models that include both data-driven and first principles components can significantly reduce the deployment and maintenance effort, the resulting low-order models facilitate model reuse across the different optimization horizons and objectives and enable large-scale optimization problems to be solved across an entire process plant or value chain.
Three industrial applications of these methods will be outlined. The first is a gas plant optimizer that provides real-time process optimization and what-if analysis. The reconciled gas plant model tracks the process under significant feed and ambient variations. The second application is a refinery-wide optimizer that encompasses multiple crude distillation, vacuum and reaction units. The third application is an olefins process scheduling application that optimizes feed distribution, cracking furnace operating conditions and decoke cycle over an extended future time horizon.
Evaluating Automated Orientation Perforation Tools for Application to Breakdown Pressures, Sand Production and Orientation Error
Authors: Crawford Elspeth Melissa , Alzahrani Bader Azib , Aljindan Salman Saad , Areujo Oscar 
Published: October 2022

Abstract
The evaluation and testing of the impact of an Automated Orientation Tool, called the Accelerometer Orientation Tool, for perforation guns and oriented perforations is reviewed in this paper to establish the effect of the tool on breakdown pressures and initiation of stimulation. This AOT could be used to select optimum fracture stresses from petrophysical properties logs, and evaluate if this was a factor for success in fracture initiation, fracture propagation and proppant placement. The breakdown pressures, average fracture pressures, and flowback of proppant were all evaluated for two wells this tool was used on, and compared to the nearest offset wells. Further benefits from sand production mitigation was also reviewed and evaluated.
Sustaining Forward Momentum Towards Intervention-Less, Reliable and Safe Transportation of Hydrocarbon Through Petronas Digital Pipeline Integrity Management System
Authors: Ahmad Aisyah , Bakar M. Hisham A. , Nadzeri M. Shahrustami M. , Ali M. Nazmi M. , Tuselim Ahmad Sirwan M 
Published: October 2022

Abstract
Keeping pace with the industry digital transformation accelerated by the 2020 pandemic, PETRONAS sees the opportunities of sustaining the forward momentum of its Pipeline Integrity Management System (PIMS) digitalization deployment as it becomes more deeply integrated in day-to-day operation. After two years of utilizing digital PIMS, the platform services have matured, and PETRONAS is able to reap the benefits of having a centralized digital platform for pipeline related information. The aspiration for the pipeline digital PIMS is to give near real-time insight of overall pipeline condition and risk, and for operators to perform proactive actions to eliminate downtime by identifying early warnings of failure.
The immediate value of digital PIMS implementation was having the legacy data being contextualized and transformed into structured data, which is the backbones for descriptive, predictive and prescriptive big data analytics. The platform was devised as such that integration with Enterprise application for data ingestion was made easier. As of to-date, digital PIMS is able to pull and push data to PETRONAS Enterprise Resource Planning tool, geographic information system (GIS) supported software, as well as operational monitoring real-time data through Supervisory Control and Data Acquisition (SCADA) system.
In addition to conventional deterministic fitness for service analytics modules, the platform is also currently integrated with analytics modules, primarily for onshore and offshore pipeline threats such as internal corrosion, free span, geohazard and damage from third party. By implementing predictive analytics approach to assess pipeline threats, it will assist stakeholders and operators in optimizing inspection campaign and rectification, thus ultimately reduce OPEX throughout pipeline remaining lifecycle.
Saudi Aramco's Process Digital Twin: Advances & Implementation Strategy
Authors: Al Ghazal Abdullah H , Winter Gabriel A , Al Nutaifi Abdulaziz M , Al Shaikh Ahmed A , Altook Talal A , He Yufeng 
Published: October 2022

Abstract
In the process industry, a digital twin is a broad concept of a virtualized representation of the manufacturing plant's assets. It aims to seamlessly connect Information Technology, Engineering Technology and Operational Technology under one over-arching framework. The goal is to integrate the contextual and operational data of projects, assets, process and buildings. Within this framework, it encompasses digital solutions for visualization, virtualization, simulation, optimization and predictive analysis. More specifically, a process digital twin (PDT) encompasses solutions that aim to simulate, predict or optimize the behavior relating to a set of integrated equipment, which define a process.
PDTs vary significantly in their type and implementation methodology. They can be physics-based, data driven or a hybrid combination of both. They can also be time-invariant or fully dynamic. Nevertheless, they generally work by mirroring the actual process in simulated mode but with full knowledge of its historical performance and an understanding of its potential. They also provide a deeper insight into the working of complex and integrated assets; often enabling users to perceive things that are not immediately apparent or are not directly measured. They further democratize access to knowledge and avail insights that everyone can understand and agree on. In their most advanced forms, they assess the process’ real-time performance and issue intelligent instructions, advising users on how to best operate complex chemical processes in order to meet operational objectives.
Numerous PDT solutions have been developed to optimize and integrate the company's hydrocarbon value chain. These continue to yield millions of dollars in annual benefits and play a major role in meeting the company's safety and sustainability targets. This paper draws on Saudi Aramco's substantial experience in conceptualizing, scoping and deploying PDTs. It further illustrates some of the advances in this domain and details the implementation strategies for solutions in this vital and emerging domain.
Engineering Letter of Assurance (ELOA)
Authors: Al Naumani Yahya Hamood , Sabahi Yasser Nasser 
Published: October 2022

Abstract
This paper presents the work completed to develop the Engineering Letter of Assurance (ELoA). An enhanced mechanism to assure and confirm that the process safety risks in projects are identified, documented, and managed to ‘As-Low-As Reasonably Practical’ (ALARP). ELoA provides focus and consistency in ‘Asset Integrity Process Safety Management’ (AIPSM); performance reports at various phases and portfolios; and enforces transparent discussions and escalation of issues to higher level. In summary, the innovative approach presented by this paper offers best practice to incorporate and embed learning from incidents (LFI); and to assure and confirm that the process safety risks in projects are identified, documented, and managed to ALAPR. This includes Engineering gaps identified related to existing facilities and wells due to legacy or new critical learnings. Which in turn, assures that facilities are operated safely while achieving highest standards of operational excellence.
Inter-Well Stress Shadow as a Tool to Improve Productivity and Longevity in Multi-Fractured Unconventional Wells
Authors: Buijs Hernán , Ibrahim Mahmoud 
Published: October 2022

Abstract
Multi-horizontal well pads are the norm in unconventional reservoirs development with wells having multi-stage fracture treatments pumped across predefined perforation cluster configurations, horizontal landing positions and well lateral spacings. Inter-intra stage and inter-well stress shadow patterns for a predefined chronological fracturing sequence can be utilized to enhance proppant distribution across the host rock, contacting additional net pay and improving the production performance of wells in ultra-tight reservoirs. 3D geomechanical models of a middle cretaceous carbonaceous shale are calibrated to UAE Shilaif Shale conditions and incorporated into a grid-oriented planar 3D fracture simulator. A large number of simulations are utilized to develop knowledge and understanding regarding the stress shadow effects on fracture geometry related to the geological heterogeneity, completion parameters, hydraulic fracture design and different chronological hydraulic fracturing sequences. The main purpose of this paper is to develop a hydraulic fracturing completion methodology focused on finding a chronological fracturing sequence across several wells that will maximize their production performance and longevity by enhancing the proppant coverage in the host rock.
Automation Via Robotic Process Automation in Pipeline Integrity Management Towards ALARP Risk Level
Authors: Shaikh Othman Shaikh Abdullah , Mohd Ali Napiah Mohd Nazmi , Zakaria Nor Salwanie , A Karim Khairol Hazman , Koi Swee Ling , Abul Kalam Azad Mohd Fithri Azad , Supian Aishah Mastura 
Published: October 2022

Abstract
PETRONAS is operating total of more than 1,200 km in total length of pipeline onshore/offshore transporting processed and semi-process hydrocarbon with total no of &gt;500 nos of pipelines within Peninsular of Malaysia as well as Sabah and Sarawak in Borneo. PETRONAS Group Technical Solutions (GTS) as a Centre of Excellence (COE) is providing services in design engineering as well as integrity solutions to pipeline operators operating assets wholly owned by and partly owned by PETRONAS operating within Malaysia as well as overseas.
Records shows that aging facilities in the upwards trending approaching or beyond design life. Thus, for the past 10 years, PETRONAS has adopted ISO/TS 12747 – Recommended practice for pipeline life extension in ascertain current and future integrity of aging pipeline and determine risk-based inspection plan.
Other than managing aging facilities, GTS also providing solutions in managing pipeline integrity for all major pipeline threat which requires proactive approach to reduce the risk at ALARP Level.
One of the pipeline threats is geohazard due to soil movement along the pipeline right of way. EML Survey was conducted but requires further assessment by pipeline engineers to determine the severity to the pipeline due this soil movement.
Each integrity assessment maybe painstaking and repetitive with duration of 1 months to 6 months based on the severity and complexity of inspection records. Taking advantage of PETRONAS inhouse digital platform data icloudbased and to align with Industrial Revolution 4.0, PETRONAS has embarked Robotic Process Automation to leverage on the digital data and to improve on the productivity. PETRONAS Group Digital, were consulted to assist on the development of RPA using commercialised software available in the market.
This paper describes the process of RPA PipeRBot - Pipeline Integrity Assessments Virtual Robots to assist our Engineers to perform Integrity Assessments and increase process cycle efficiency. This PiperBothas completed proof of concept and ready to for deployment.
With this Automation, we have achieved more than 50% of process efficiency and increase in productivity and more cost saving to end users which is pipeline operators, operating assets wholly owned by and partly owned by PETRONAS operating within Malaysia as well as overseas in PETRONAS Canada.
Expanding Downlink Capabilities Using Autonomous Directional Drilling with Rotary Steerable Systems
Authors: BA Samba , Kim Jinsoo , Goel Prateek , Sahli Hussein , Chacko Arun , Olanubi Olakunle , Lojek Steve , Hoarau Loic 
Published: October 2022

Abstract
Directional drilling is a major activity in today's oil and gas industry, as most wells currently being drilled are directional wells. The complexity of the wells being drilled today creates the greatest risk and most promising scenarios for proper planning and implementation.
The Autonomous Directional Drilling system includes an automated directional drilling advisor application that provides real-time working trajectories updates, steering recommendations, and downlink commands for rotary steerable system (RSS) tools. This DD Advisor system can be used in any type of well from start to finish by automatically providing the next sequence of actions at each survey point and carefully monitoring the steering responses to advise important actions to be taken. DD Advisor leverages the downhole closed-loop control capabilities to reduce the amount of intervention needed from surface.
DD Advisor enables directional drilling with minimal human intervention either from the rig or from the office, taking advantage of the outstanding digitalization platform. DD Advisor with RSS capabilities is now deployed in the field and has been used in more than fifty different wells. These operations included successfully using the steering recommendations and sending the automated downlinks via software and hardware automated downlinking solutions. This operation was quite challenging and required automatically finding the optimum trajectory path with steering capability constraints, rate of penetration constraints, target deviation constraints, number of downlinks constraints, and minimizing risks to avoid costly failures.
This paper presents a novel technical approach for automatically making directional drilling decisions. Autonomous Directional Drilling recently (March 2022) won a Houston Offshore Technology Conference technology award.
Development of an Artificial Intelligence-Based Well Integrity Monitoring Solution
Authors: Aditama Prihandono , Koziol Tina , Dillen Dr. Meindert 
Published: October 2022

Abstract
The question of how to safeguard well integrity is one of the most important problems faced by oil and gas companies today. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), many companies explore new technologies to improve well integrity and avoid catastrophic events.
This paper presents the Proof of Concept (PoC) of an AI-based well integrity monitoring solution for gas lift, natural flow, and water injector wells. AI model prototypes were built to detect annulus leakage as incident-relevant anomalies from time series sensor data.
The historical well annulus leakage incidents were classified based on well type and the incident relevant anomalies were categorized as short and long-term. The objective of the PoC is to build generalized AI models that could detect historical events and future events in unseen wells. The success criteria are discussed and agreed with the Subject Matter Experts (SMEs). Two statistical metrics were defined (Detected Event Rate – DER – and False Alarm Rate – FAR) to quantitively evaluate the model performance and decide if it could be used for the next phase.
The high frequency sensor data were retrieved from the production historian. The importance of the sensor was aligned with the SMEs and only a small number of sensors was used as input variable. The raw data was pre-processed and resampled to improve model performance and increase computational efficiency.
Throughout the PoC, the authors learnt that specific AI models needed to be implemented for different well types as generalization across well types could not be achieved. Depending on the number of available labels in the training dataset, either unsupervised or supervised ML models were developed. Deep learning models, based on LSTM (Long-Short Term Memory) autoencoder and classifier were used to detect complex anomalies. In cases where limited data were available and simplistic anomaly patterns were present, deterministic rules were implemented to detect well integrity-relevant incidents. The LIME (Local Interpretable Model-Agnostic Explanations) framework was used to derive the most important sensors causing the anomaly prediction to enable the users to critically validate the AI suggestion. The AI models for gas lift and natural flow wells achieved a sufficient level of performance with a minimum of 75% of historical events detected and less than one false positive per month per well.
Rapid NMR T2 Extraction from Micro-CT Images Using Machine Learning
Authors: Li Yiteng , He Xupeng , Alsinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: October 2022

Abstract
Nuclear magnetic resonance (NMR) is an important tool for characterizing pore size distributions of reservoir rocks. Pore-scale simulations from digital rocks (micro-CT images) provide deep insights into the correlation between pore structures and NMR relaxation processes. Conventional NMR simulations using the random walk method could be computationally expensive at high image resolution and particle numbers. This work introduces a novel machine-learning-based approach as an alternative to conventional random walk simulation for rapid estimation of NMR magnetization signals.
This work aims to establish a "value-to-value" model using artificial neural networks to create a nonlinear mapping between the input of Minkowski functionals and surface relaxivity, and NMR magnetization signals as the output. The proposed workflow includes three main steps. The first step is to extract subvolumes from digital rock duplicates and characterize their pore geometry using Minkowski functionals. Then random walk simulations are performed to generate the output of the training dataset. An optimized artificial neural network is created using the Bayesian optimization algorithm.
Numerical results show that the proposed model, with fewer inputs and simpler network architecture than the referenced model, achieves an excellent prediction accuracy of 99.9% even for the testing dataset. Proper data preprocessing significantly improves training efficiency and accuracy. Moreover, the inputs of the proposed model are more pertinent to NMR relaxation than the referenced model that used twenty-one textural features as input. This works offers an accurate and efficient approach for the rapid estimation of NMR magnetization signals.