bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scalable Port Inspections Through Underwater, Terrestrial and Aerial Robotic Platforms
Authors: Medagoda Lashika , Galea Mitchell , Bargoti Suchet , Khan Junaid , Dunne Toby , Potiris Steve , Ul Abidin Zain , Anwar Irsa , Jolly Jordan , Windrim Lloyd , Charolia Fazilai 
Published: October 2022

Abstract
Asset management of a marine port or terminal requires inspection of the asset components below the water surface, at the water surface line and above the water surface. In order to determine asset health, high fidelity, multi-modal data is captured remotely using robotics, and subsequently analyzed. This facilitates the production of prioritized actionable insights to be produced. These insights can be scheduled as maintenance works. This paper outlines a novel multimodal inspection system that was deployed at a port to facilitate its remote inspection.
Two inspections are detailed in this paper, with differing inspection requirements and constraints. The inspection operation can be broken down into multiple components, an underwater scan of the port, a water-surface scan, a ground level scan and an aerial scan. The Port Asset required the inspection of the wharf and quay walls. The Marine Terminal Asset required inspection of the complex pipework.
The data collected from the different modalities were represented in the 3D space. The alignment of the different modalities could then be done in this representation. For the Port Asset, where multibeam sonar maps were collected, the difficulty in alignment of the different modalities is apparent due to no shared points below and under-water, which requires estimation using extended features as landmarks. There also exists slight warping between the data that must need localized scaling for certain sections, which may be done using local submaps and scaling. For the Marine Terminal Asset, artificial markers are used to allow scaling of the drone captured photogrammetry. High levels of coverage from the multiple views of the asset from different data collection strategies allowed the complex pipework to be well mapped, with further insights from the co-registered imagery.
The novel information described in this paper is the unique combination of different modalities for a broadscale port inspection, with point clouds from sonar, lidar, photogrammetry and imagery, in a combined reference frame which acts as a digital twin for the port. This also allows unique insights, such as the exact context and magnitude of faults in the port environment, to allow corrective works to be targeted correctly.
Gas Maximization Scenarios Leveraging a Countrywide Integrated Capacity Model and Digitalization of Engineering Workflows
Authors: Hamad Al Selaiti Iman , Mabrook Maged 
Published: October 2022

Abstract
This paper presents the process and results from the applicationof an Integrated Subsurface/Surface Model (Digital Twin) for computing capacity at the country level, or Integrated Capacity Model – ICM, to generate additional revenue through increased gas production from the existing asset facilities.The model is the foundation of the ICM system, a platform that allows ADNOC to evaluate different production scenarios at a field, asset, or country level for operational and planning purposes. The presented case and results are focused specifically on a gas maximization scenario, a very relevant scenario considering the global market tendencies and interest in cleaner provision of energy. The scenario maximizes the gas production of the country while adhering to oil targets and reservoir management guidelines.
Integrated Capacity Model (ICM) is one of ADNOC'sindustry leading technical solution for managing its vast (both in number and size) oil and gas reserves being exploited by thousands of wells and providing stabilized oil and gas to the various export points and processing plants. It is the world's first such large-scale model-based solution. The solution provides several predefined daily runs in an automated manner which can be visualized via an interactive web-based dashboard across the organization. ICM also allows for the configuration of ad-hoc or alternate scenarios to allow for the analysis and evaluation of special operating cases and business plan development. These alternate scenarios address the requirements to evaluate the opportunities and capability of the infrastructure to deliver in exceptional circumstances and for special needs.
This paper presents the configuration details and results of one such scenario. The paper shares the implementation and challenges of maximizing natural gas products across the ADNOCintegrated value chain. This scenario is achieved by setting up a multi-objective optimization function across every asset in theICM model to maximize its gas production while maintaining crude oil targets as required by market and ensuring that well and reservoir management guidelines are honored.
The results of the scenario demonstrate the capability to increase gas production by up to 30% across through the adjustment of the country wide production profile.It is demonstrated that producing a substantial incremental gas volume is possible while honoring any oil targets, particularly in assets with large gas-oil-ratio (GOR) variation. The additional gas availability is achieved through a number of well choke changes, therefore the size of the opportunities in terms of wells and strings can be ranked in such order to balance the practicality with field operations.
Slim Multistage Fracturing—One of the Most-Successful Solutions in the Most Recent 15 Years of China's Multistage Fracturing History
Authors: Zhao Geng , Fould Jeremie , Dong Yifan , Li Changwei , Huo Ying , Liu Xiaoli , Liu Yang , Wang Qiang , Yang Shengfang , Jin Ting , Ma Qian , He Tao 
Published: October 2022

Abstract
Hydrocarbon resources have primarily been accessed from onshore environments. Most of the explored basins have matured, with some even nearing the end of their field lives. Production from these traditional resources has declined over the past few years, and new discoveries of vast resources are becoming rarer.
Global exploration trends focused on deepwater (and even ultradeepwater) fields, deeper onshore formations, and unconventional resources are aligned with the focus of China's hydrocarbon industry. In 2021, China completed its first self-operated subsea deepwater gas project, Lingshui 17-2, and a series of new projects is planned for the next few years. In terms of developing deeper (&gt; 4000 m) onshore formations, Sinopec Northwest set a record in Shunbei Field with an ultradeep oil development that reached 8874.4 m. Famous fields in China for unconventional hydrocarbon development, some of which are in deeper formations, include PetroChina Southwest in the Sichuan Basin, PetroChina Changqing in the Ordos Basin for tight gas/shale gas and shale oil, and Sulige in the Ordos Basin for tight gas. During China's 14th Five-Year Plan period, from 2021 to 2025, the proportion of unconventional oil and gas production will further increase, and production is expected to increase rapidly to align with the country's energy security strategy. Furthermore, the oil and gas industry worldwide has been placing a greater emphasis on carbon footprint reduction; while the number of new oil projects decreases every year, natural-gas projects, especially in deepwater environments and shale/tight reservoirs, have been rapidly increasing.
This paper will focus on the unconventional hydrocarbon development methodology and discuss the evolution of fracturing technology, particularly multistage fracturing completion tools over the past two decades; then, the paper will review the various design concepts, features, and serviceability of representative completion tools during each fracturing period. A successful 10-year tight-gas sandstone application in Ordos, China, will be used to review the slim multistage fracturing sleeve (SMFS), the most-reliable fracturing string design, and its key operational improvements. The design has been informed by previous field knowledge and experiences, resulting in a simple, cost-efficient, and technology perfect adaptability tool that can be used to illustrate the development trend of multistage fracturing completion tools.
Using Ai to Optimize the Use of Gas Lift in Oil Wells
Authors: Docherty David , Erickson Dale , Henderson Scott 
Published: October 2022

Abstract
Optimization of gas lift rates in an oil field with hundreds of wells is a complex challenge. Typically traditional control systems and operating strategies fail to optimize the problem due to this complexity. Typical gas lift optimization challenges in a field include the following: Real-time flow rate prediction of various phasesReal-time wells performanceReal-time pipeline network performance
The dynamic nature of the problem and the variability of the solution space makes it extremely hard for traditional simulation-based solutions to locate the optimal performance point in real-time. The computational requirement is massive and makes it difficult to perform these calculations at the "edge". This is where combining simulations, human expertise, and machine learning technologies such as Deep Reinforcement Learning help build AI that can excel in rapidly computing optimized setpoints in complex domains.
Using pioneering machine teaching methods combined with multiphase simulations this paper will present the solution of using Artificial Intelligence (AI) to optimize gas lift rates in real-time, finding the optimum gas lift rates for a 4 well pad, 200 well system, such that net profit is increased by 5%-25% for different baselines as the reservoir conditions change over field life.
An Innovative In-House Engineering Solution to Manage the Process Alarms
Authors: Lakshminarayan Yogesh , Krishnaprasad Karthik Coimbatore , Tank Yogesh Kumar , Brahmbhatt Hiren Harshadkumar , Al Teneiji Ahmed Mohamed 
Published: October 2022

Abstract
Alarm system plays a pivotal role in the safe management of plant and its machinery. Alarm systems in Process plants are used to identify abnormal situations and to help plant Control room operators take timely, appropriate action. Ineffective Alarm system poses a serious risk to HSE and Plant profitability. The effects can be very serious if these alarm systems do not work well. Many times, alarm system effectiveness is unknowingly undermined by poorly configured alarms which cannot adapt to dynamic conditions. Similar situation was observed in ASAB / SAHIL plant.
In the year 2013, as ASAB/SAHIL commissioned Green field Full field development plant, Control room Operators were overwhelmed with thousands of alarms on Operator screens. ASAB / SAHIL being a hub for SE Fields, it was obligatory to achieve a quick solution of a stable Alarm system. Site Management soon realized the gravity of the situation and an objective was set to have best possible stable Alarm system through innovative, collaborative efforts by an established and dedicated Task force team with a thorough knowledge of the Process.
The Task force team (TFT) comprised of subject matter experts from Maintenance and Operation disciplines. The TFT was led by the Maintenance Manager and the weekly meeting was chaired by Instrument counterpart who were responsible to review, analyze and categorize the Bad actors and notify to all stakeholders of TFT for a resolution. With sheer dedication and diligence work which was performed over a span of 8 to 9 years, finally on April 2021 alarm system in ASAB and SAHIL sites were brought to the Stable state. The sweet success was finally achieved by continuous efforts of TFT members who were focused and determined to see the light at the end of tunnel.
Digitalized Well Head Control Panel Maintenance
Authors: Jayapalan Gopalakrishnan 
Published: October 2022

Abstract
Wellhead control panel ensures safe operation of Oil /Gas well operations. Digitalization of wellhead control panels helps in reducing the downtime. Distributed Control System ( DCS) can be used for collecting the wellhead control panel data and to perform panel health assessment. It triggers maintenance request / short messaging service ( SMS) once it identifies fault / trip. This paper analyses on how digitalized well head control panel maintenance can be carried out and how it reduces well down time considerably.
First Proof of Concept for an Integrated Robot Supervision System
Authors: Audouin Grégoire , Lannes Rémi , Collin Gildas , Bartoli Eric , Munoz Jean-Michel , Potapieff Hugo , Graves Matt , Chevet Guillaume , Paniccia Tommaso 
Published: October 2022

Abstract
TotalEnergies’ use of robotics on remote or offshore sites is considered with the objective of enabling unmanned operation for long periods and as the next frontier for increased personnel safety, industry attractiveness for young talents and further OPEX and CAPEX reduction. TotalEnergies performed several IT/OT system architecture evolutions and developed a specific Robot Supervision System (RSS) equivalent to a fleet management system to help and consolidate know-how in the deployments of specific mobile ground robots.
As the first autonomous and explosion-proof inspection ground robots become commercial, many have difficulties seeing how these new devices can be interfaced with legacy industrial systems and how its data can be represented in a user-friendly interface to make the remote operation of robot efficient & attractive enough. Only this level of interfacing will enable the possibility for automated inspection, operation, and maintenance tasks execution with intuitive programmable interfaces. The robotic devices also need also to comply with detailed specifications in order to communicate its data in real time to the supervision system.
We will describe the specific modifications required to the robot associated systems to be used within a private industrial network and the specification for real time interfacing of its data. Many Robot suppliers initiated their system developments focusing on robot navigation but a lot of the interfacing complexity with operator companies remained to be achieved to exchange data with the site digital twin, the maintenance task planning tool, the cloud machine learning software resources, or the Process Data Management System (PDMS) or even the plant control system (DCS).
The associated functionalities of the RSS will also be presented: Acquisition, historization and user-friendly display of robot's dataIntegrate several robot types or from several vendorsInterface with all company systemsVisual 3D digital twin environment to display data acquired by the robot or useful data for its remote operationIntuitive configuration and management of robot for routine tasks or based on a specific plant event or alarmComply with specific industrial cybersecurity rules
Feedback will also be given on how it has been possible to adapt the IT architectures and how we intend to fit the requirements of OT systems to adopt robots with more critical interactions with the industrial process. We can present our approach for upscaling robot deployment with this on-premises supervision systems capable of historizing the data, generating immersive front-end displays for robots’ operators and managing secured interconnections to corporate systems. Figure 1An experienced operator using the RSS - View of the 2 main dashboard types (summary mashups & 3D view)
Using Autonomous Control to Stabilize Well Performance without the Downhole Pressure Gauge
Authors: Gao Zhuo , Hyder Syed Zeshan 
Published: October 2022

Abstract
Optimizing a gas lift well can be challenging. Essentially, it needs to optimize a complex system, where gas is compressed and injected to the bottom of the well to lift oil/water/gas to the surface. It contains numerous facilities and equipment, from compressor, injection valves, downhole gas lift mandrel, valves, well head, surface separator to the sales lines. Flowing bottom hole pressure plays a critical role in the system. In a lot of cases, stabilizing the flowing bottom hole pressure within a desired range means the system is optimized. However, downhole pressure gauges are expensive, and it isn't realistic to install one on every well, which brings more challenges to optimize gas lift well performance against target flowing bottom hole pressure.
Edge IoT platform provides customized autonomous control logic to accommodate the customer's optimization challenge at the well level. Weatherford deployed the customized autonomous control logic which includes two distinct logics – pressure control logic and injection rate logic. The pressure control logic updates the well head choke diameter based on the calculated FBHP. The injection rate is optimized based on well daily performance.
In the 60 days trial, the field results are proved to be able to utilize the next generation autonomation device to stabilize well head pressure, and casing head pressure, and to minimize buy back gas and reduce the manual intervention.
The autonomous control logic and its corresponding workflow, embedded in the next generation automation device, not only incorporates the industry 4.0 technology, but also leverages both system analysis methods and high frequency real time data to improve the optimization granularity, deliver the values to the customer at the well site.
Utilization of ML to Validate Pressure and Temperature Measurements
Authors: Alquraini Ali Haitham , Al Kadem Mohammad Saeed , Al Ssafwany Ali Radhi 
Published: October 2022

Abstract
The process of validating and monitoring pressure and temperature data is a key element in production engineering as it ensures proper well evaluation. Consequently, wells are frequently surveyed for better reservoir monitoring and accurate measurement of productivity. This study explores a validation method using advanced Artificial Intelligence (AI) and Machine Learning (ML) classification models that were developed utilizing historical data to automatically validate conducted pressure and temperature measurement and communicate observations and alerts to engineers.
The proposed method validates pressure and temperature measurement using ML model based on previously conducted measurement using advanced algorithms. The developed model fed on pre-identified key production and pressure/temperature parameters that are used to classify surveys. Moreover, these parameters were selected based on historical data and measurement reports and then were analyzed and ranked to identify the most important parameters on the performance and accuracy of the model utilizing advance algorithm and correlation analysis. This is to predict and classify test measurement via the utilization of a non-linear relationship through the use of data-based analysis alongside physics-based analysis.
The data set of conducted pressure and temperature measurement was split into two groups i.e. training and testing. In addition, a K-fold cross-validation was performed on the training set to validate the performance of all considered and selected ML models. The results of each ML model were then compared for accuracy and the Random Forest Classification algorithm was selected. The developed classification model achieved an overall accuracy level of more than 95%. Validating and testing the model on several cases showed promising results as irregularities are detected in advance before engineers evaluate these conducted measurements. The developed model enabled an effective utilization of previous measurements to validate newly conducted ones and, consequently, alert engineers of any detected anomalies in advance. This yielded significant impact on cost and time savings due the model's ability to automatically predict and validate the conducted measurements.
The pressure and temperature validation model enhanced monitoring and interpreting the production/pressure and temperature measurements and resulted in a substantial improvement in timesaving. The model is developed to be run on the Cloud and it provides an automatic validation of the newly conducted measurements. In addition, it also delivers an alerting mechanism to engineers for any observed abnormalities.
Non-Combustion Power Generation Technology for Oil & Gas Production Facilities Towards Net Zero Carbon Emission Agenda
Authors: Muhammad Muhammad Fadhli , Myo Thant Maung Maung , Sedaralit Mohamad Faizal , Shaffee Siti Nur Amira 
Published: October 2022

Abstract
In this paper, a strategy of deploying a novel compact electric generator (n-CEG) is investigated for generation of electrical energy replacing conventional mini turbine generator (MTG) technology. Deployment of these technologies are driven by growing power requirement at offshore facilities with aging conventional equipment currently installed at these facilities. With advancement and potential benefit that the technologies can offer, a high-level feasibility study is needed to ensure suitable technology is deployed. This paper seeks to cover the feasibility of n-CEG technology deployment at offshore facility as compared to MTG and floating solar panel inclusive of technical and cost perspective.
Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques
Authors: Hassan Amjed , Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: October 2022

Abstract
Dew point pressure is a curial parameter in characterizing gas reservoirs. Several methods can be used to determine the dew point pressure, including laboratory measurements and empirical models. However, laboratory determinations are expensive and time-consuming, especially for studying high-pressure tight reservoirs where more caution and procedures will be required. While empirical correlations do not accurately reflect the complexity of fluid behavior, and limited models were developed for high-pressure reservoirs. The goal of this work is to develop a reliable tool for predicting the dew point pressure for tight and high-pressure gas reservoirs.
This work was carried out using five main phases; data collection, quality control, model construction, development of new correlation, and model validation. The data used in this work were obtained based on 250 laboratory measurements. All data were evaluated and the noises and outliers were removed. Different types of artificial intelligence methods were examined to come up with the best determination model. Artificial neural network (ANN) technique, support vector machine (SVM) approach, and adaptive fuzzy logic (AFL) systems were investigated. The hydrocarbon compositions and the molecular weights were used as inputs to estimate the dew point pressure. Different types of error indices were employed to measure the prediction performance of the developed equation. Average percentage error and correlation coefficient values were determined for the different models.
The developed model predicts the dew point pressure with a percentage error of 4.85% and an R2-value of 0.94. The ANN model developed in this study has 4 neurons and one hidden layer. An empirical equation was proposed based on the best ANN program to provide a direct estimation of the dew point pressure. The extracted equation can provide an average error of 5.74% and an R2-value of 0.93. Overall, the proposed model can reduce the cost and time required for determining the dew point pressure and help to improve reservoir management by providing fast and reliable estimations.