bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence Aims to Save Lives in Offshore Marine Vessels
Authors: Almadhoun Wael , Al-Shimmari Nasser , AL-Qamzi Abdulla , Al Owaid Hatim , Al-Shamsi Ali , Al-Naqbi Maha , Ateya Ali 
Published: November 2020

Abstract
In ADNOC Oil and Gas 4.0 mission, we are committed to empower people with the needed capabilities and Artificial Intelligence (AI) technologies to fuel innovation, efficiency and more importantly achieve and sustain a 100% HSE, by transforming the way of handling HSE events by moving from reactive to proactive approach. The ultimate objective is to save lives, empower the vessel Captains to immediately identify and respond to violators, improve the HSE culture of the crew, and automatically generate live data analytics and statistics with the aim of improving safety in operations. The implemented AI use cases are; deviation for not wearing Protective Safety equipment in designated areas, violation of not utilizing safety passages, alert when no watchman in muster station, alarm when man overboard incident, alarm when man fell on stairs, and live Personnel on board each weather-deck. When introduced the Artificial Intelligence cameras, our marine vessels will adopt a smarter automated response and reporting culture, which will in turn, lead to increased safety oversight of our critical offshore operations. Therefore, with the advent of the AI technology, many common business processes have been automated thus enabling personnel to increase their focus on more important tasks while technologies like the AI System can handle many of the time consuming tasks.
The solution components consists of Artificial Intelligence platform, high definition cameras, local server, wide-range WiFi access point, network infrastructure and a tablet. On the tablet device, the captain have full coverage of the vessel weather decks, working areas and restricted zones with a feature to generate alerts when detecting an emergency situation. This was provided to empower the vessel Captain to acknowledge and respond to violations as well as take a proactive action to prevent incidents from happening. The Machine Learning algorithm has been trained on actual scenarios and will be continuously improved by adding more recorded event to retrain the initial model. Currently, the prediction model is performing on the vessel operation mode and recording events with high rate of accuracy. In case of automatically detecting an alerting or non-compliance event, the captain would be notified, beacon lights and sound, and log recorded in the local and central system with a photo and a short video clip of the incident. The process of identifying HSE deviations are becoming digitally transformed by deploying AI capabilities on real-time video streams. The AI-based camera system leverages Computer Vision features that enables machines to get and analyze visual information and take action. The whole process of identifying HSE violation events has been digitally transformed by deploying an artificial intelligence solution to perform real time video analytics.
Miscibility Tracking in Reservoirs with Large Compositional Variation Undergoing Miscible Gas Injection
Authors: Mogensen Kristian , Ali Ahmed Elsayed 
Published: November 2020

AbstractMiscible gas injection is a proven enhanced oil recovery method for medium-light oils. Miscibility is typically assessed with the slimtube experiment, which aims at identifying the minimum pressure (MMP) above which the displacement process is multi-contact miscible and leads to low microscopic residual oil saturation. During implementation of a miscible gas injection scheme, there will often be areas close to producing wells where the reservoir pressure is below the MMP and the question then arises whether the process can still be considered miscible. Another complicating factor occurs in reservoirs with significant lateral fluid property variation, where it is not intuitively clear how the MMP varies aerially.We employ compositional reservoir simulation to investigate the impact of flowing bottom-hole pressures below MMP on the development and propagation of a miscible front. Starting with 1D simulations, we track a number of important properties such as the effluent gas composition over time, the component K-values at distinct positions along the displacement front, as well as the pressure at the front over time.Due to the high gas mobility, the pressure drop per unit distance is greatly reduced in the areas flooded by gas. This means that the reservoir pressure at the gas-oil front is higher than the pressure at the same position before arrival of the front. Therefore, a miscible displacement can be maintained even when not only the flowing bottom-hole pressure but also the average reservoir pressure is below MMP. A key requirement is, of course, that the gas is injected at a pressure above MMP. We evaluate various parameters in terms of their ability to distinguish between miscible and immiscible displacement and find that the phase density difference versus pressure provides a unique advantage for estimating the MMP and we believe it can be used not only in 1D slimtube simulations but also in other thermodynamics-based algorithms. We also find that the vapor mole fraction, inferred from GOR measurements, is a better metric than GOR itself and that molar ratios applied to the effluent gas are very useful for tracking miscibility.
Novel Approach to Link Petrophysical Groups into Electrofacies by using NMR Logs
Authors: Celma R.. , Farooq U.. , Douik H.. , Alawadhi A.. , Saputelli L.. 
Published: November 2020

Abstract
Petrophysical Groups (PG) from cores and log facies (electrofacies) are two of the main outputs generated in the petrophysical domain. Those are particularly contributors to the highest degree of uncertainty during the reservoir modeling, with the subsequent high-impact in field development decisions. Detailed core analysis is the preferred main source of information to condition the PG, however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to extrapolate petrophysical groups by the use of log facies. The approach of this investigation is to apply machine learning to move from the core domain to the log domain and to determine relationships to generate properties for the three-dimensional reservoir model.
All wells have a full set of logs (Gamma Ray, Resistivity, Density, PEF and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. The idea of this step is to observe the capability of the log data to self-classify according to the log response. In a second stage, core data is used to define petrophysical groups (PG) by clustering the data in into similar pore throats from the MICP and permeability vs. porosity relationships, also thin section were used to introduce geological meaning to the to the PG. The PG are linked to the NMR pore-size distribution (Invasion correction were applied in order to observe only the pore geometry content on the T2 distribution) by correlating into a pre-determined standard NMR pore geometry distribution, the result is that each PG group has a particular T2 range and proportion. In the next step a pseudo NMR curve is create by the use of a Neural Network constrained by the range and proportions obtained and observed in cored wells. This pseudo NMR can be generated in all wells (even the ones Un-cored or without NMR) and generate PG groups at log scale hence the PG logs are controlled by the relationship between core (Pore throat, permeability, porosity and Geological Facies) and pore-size distribution coming from the NMR Log.
The result is a robust model capable to capture the core relationships and able to predict properties preserving the geological features of the reservoir. The application of this method makes possible to determine the minimum and most relevant source of data to address the issues caused by the heterogeneity of the rock. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.
This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.
Evaluating and Optimizing A Program to Reactivate Water Injectors in A Mature Field by Integrating Conventional Physics with Artificial Intelligence
Authors: Calad Serrano Carlos Mario , Gutierrez Fernando , Sarma Pallav 
Published: November 2020

Abstract
The application of a novel data and physics driven modeling technique integrating many data sources such as historical production and injection data, wellbore andcompletions data for a mature waterflood in the Cuyo basin in Argentina is presented. This innovative technology combines the benefits of speed of machine learning with the predictivity capabilities of traditional simulation. The initial objective was to identify opportunities for optimization of ongoingsecondary recovery projects to increase production and reduce costs in the subject field. During the execution of the project the need to evaluatereactivation of inactive injectors was identified and the software was modified to include this feature.
The technology integrates machine learning techniques with partial differential equations of fluid flow such as thoseapplied in conventional reservoirs simulation models. Because the presented approach honors reservoir physics, it provides long term predictivecapacity and always ensures physically realistic solutions. The technique is inherently low order and continuous scale, and therefore orders of magnitude fasterthan traditional approaches.The approach is probabilistic in nature, allowing for quantification of uncertainty. Given the complex conditions of operation and control in secondaryrecovery projects, and sparse and noisy data, it is not feasible to define a unique deterministic solution. Therefore, the proposedapproach providesphysically and statistically possible distributions (ranges and frequencies) of the variables affecting reservoir behavior.
One of the features developed for this project is the ability to "reactivate" injectors and model the response of thefield with these injectors operating at an optimum level. This feature was used to evaluate the reactivation of 12 inactive injectors. The tool delivered theoptimum number of injectors to be reactivated, the optimum volume to be injected at each injector and the order in which the reactivation would bringthe largest/quickest value. The results were compared with the optimisation plan obtained by redistributing water without the injectors and the best NPVsolution chosen.At this time the operator has not implemented the plan due to market conditions.
A Novel Well Design, Completion and Testing Strategy in a Changing Fluid Distribution of an Appraisal Well
Authors: Aftab Muhammad , Swain Ashis , Sultan Mir , Al Blooshi Abdulla , Al Shamsi Mohamed , Mingsheng LV , Abu Snaineh Bashar , Al Mansoori Sara 
Published: November 2020

AbstractGenerally, appraisal wells are drilled to reduce uncertainty. However, occasionally reserves uncertainties may increase in a heterogeneous carbonate reservoirs specially challenging stratigraphic limit of reservoir facies. Under such circumstance, sometime operators rethink of further investment in the field development when in-place volumes are marginal.The objective of the study is to present how we achieved well design modification and test strategy in a dynamic environment. Optimal well test design, execution and analysis can help mitigate major uncertainties, which were not considered during initial planning phaseThe subject appraisal well was drilled as a vertical hole in an up dip direction to the first appraisal well. However, Open Hole (OH) and mud log data indicated the reservoir to be tight and in some portion dominated by water flow during sampling even though clear hydrocarbon presence observed in core chips and cuttings analysis. After detailed studies of the available data, a decision was taken to horizontalize the well towards first appraisal well. While drilling, geological barriers were encountered as indicated by the presence of different fluids in the horizontal section. Variable fluid presence (water and oil) posed a challenge with respect to well completion and testing. This paper describes the process of completing the well in an evolving complicating situation and how successful well test design and execution helped to mitigate the uncertainties.OH Logs, Wire Line Formation Tester (WFT) and test data from the studied and existing wells in the area were used to design the well test and interference with first appraisal well in an evolving situation, which is not typically faced in well operations. Hence, the results obtained provide an additional information that helped to conclude variable fluid distribution and its dynamic connectivity to the first appraisal well.Well was completed followed by test as designed and Production Logging Testing (PLT) was conducted to define reservoir contribution. Post well test analysis and comparison with existing WFT and test data from existing well helped to conclude results and address the uncertainties.This paper summarizes the design process, challenges faced in an unexpected variable fluid distribution in the horizontal section and accordingly how well test analysis was performed to conclude the results that helped to take optimal investment decision for the development of this marginal reservoir.
Face the Challenge to Reap the Benefits Automation and Smart Technology
Authors: Thonthe chettiar Tharmalingam , El-Sawah Mohamed , Almentheri Ibtihaj 
Published: November 2020

Abstract
In the current time of uncertainty, Oil & Gas Industries need to manage the cost, extract the most value from current assets and sustain its production. Automation and smart technologies are key enablers to fulfill such objectives. Current era of digital world, automation is a major driver in Oil and Gas industry today. Digitalization, automation and deployment of smart technologies, improves the plant availability and provides plant information, equipment conditions at fingertips of engineering, operation / maintenance team to troubleshoot for the improved plant performance. Availability of such information at desired location facilitates early decision and supports in attaining an operational excellence.
Deployment of ‘Digital Oil Field’ concept is a key enabler to manage the plant operation effectively from centralized remote location. It supports 100% HSE with minimized exposure of hazard to the work force. Automation introduces a ‘single window’ operation for an efficient management of plant operation and supports predictive / preventive maintenance to ensure plant availability with no production loss. Deployment and realizing the benefits offered by automation and smart technologies are not without challenge. We face challenges at every stage of deployment, right from selection of technology, building infrastructure, lack of skilled manpower to handle the new technologies, multiple systems interfaces, abundant data, alarm flooding, cyber compliance etc.,. In order to seize the full potential benefits of "automation and smart technology, each challenge needs to be turned in to an opportunity.
Chemical Sand Consolidation as an Effective Alternative to Conventional Sand Control Methods in Ecuador
Authors: Izurieta Alvaro , Serrano Fabrizio , Andrade Patricio , Burgos Angel , Rodriguez Ricardo , Jaramillo Jose 
Published: November 2020

Abstract
This paper shows the results from chemical sand consolidation treatments used in the M1 sandstone as the main sand control method. For a successful implementation of this technique a workflow that combines field data, temperature modeling, laboratory analysis, petrophysics and geomechanics has been developed. Production results reports zero to near zero sand production with almost no impact on well productivity.
Pre job design steps used to secure a consolidated reservoir after the treatment are described. Temperature is a key parameter to model since the resin is temperature activated. Laboratory tests and simulated temperature defines the curing time. A step rate test is used to calculate the injectivity index and evaluate the need for an acid stimulation treatment before the consolidation system is pumped into the well. This test is repeated after curing time to evaluate any possible impact on productivity. Determination of the Unconfined Compressive Strength (UCS) of the consolidation sand and critical sand production rate are also included in the analysis to understand sand production potential in the field.
Chemical sand consolidation consists of two main components, resin and hardener. The consolidation fluid along with a pre-flush and an over-flush have typically been pumped into the well using production tubing. This sand control method has been proven to be relatively low in cost compared to other sand control techniques and the treatment can be performed in a relatively short period of time. These two characteristics and the positive production results observed thus far makes this technique attractive for upcoming sand control completions with unconsolidated reservoirs with similar characteristics to the M1 sandstone.
In addition, conventional sand control methods rely on a particle size distribution (PSD) from a representative core sample for a successful implementation whereas chemical sand consolidation system is PDS independent.
Ground Robotics: The Route to Intelligent and Reliable Operation & Maintenance
Authors: Duco Drenth , Matthias Biegl 
Published: November 2020

Abstract
This paper identifies the interdependency and impact of the maintenance of assets with the use of robotics in combination with artificial intelligence to support the modern ‘Operator 4.0’. The execution of high-quality, well-planned and efficiently-executed maintenance is crucial in today's energy production process, both from a reliability/ functionality and from an economic point of view. Applying such smart maintenance is an important driver to create long-lasting reliable performance during the operational lifetime of an asset, which will also be extended by executing such intelligent maintenance.
Since a few years, a number of Robotics and Big Data technologies have come to the market and these are two of the major market disruptors, accelerating innovation and noteworthy changes to the industry. Well-equipped ground robots will become the new data gatherers, using a suite of high-quality sensors and predictive algorithms to provide real-time advice. Interpreted by experienced maintenance engineers - this leads to intelligent decision-making to sustain or enhance high performance levels on any production site. Important breakthrough of this development is the strong reduction in cost, the ‘simplicity’ and the almost miniatured size of the associated equipment, and the acceleration in e.g. computing power to process the many data streams and convert it in easy to interprete information.
We have assessed, in practice, how such robots can indeed act as the (extra) eyes, ears and brains on the ground, for today's - and tomorrow's - operator and/or maintenance technician. This paper will detail a number of examples of state-of-the-art robotic solutions for operation and maintenance purposes, coupled to Big Data technologies, and the challenges that lie ahead of the industry to create the high-capital industrial installations of the future, starting today. The information gathered during our demonstrations and described in this paper will lead to new, inspiring and smart ways of designing, operating and maintaining energy production infrastructures - brownfield and greenfield, onshore & offshore - by using ground robots and the latest sensor and predictive maintenance technologies, adapted to the workforce of the 21st century.
Drilling Real-Time Data: Data Acquisition Time Synchronization Framework for Critical IR 4.0 Solutions
Authors: Alotaibi Bader , Contreras Otalvora William , Alzahrani Majed 
Published: November 2020

Abstract
In recent years, oil and gas Drilling and Workover (D&WO) operations have grown significantly and as a result produced huge amount of data. Today, modern rigs transmits more than 80,000 real-time data points every day. The quality of the generated data is directly proportional to the value it can provide in predicting drilling issues and discovering hidden knowledge. One of the challenges with this high frequency overwhelming amount of data is the concept of time.
Data providers in a single site may have different time references. As every data point is stamped with the time of the computer running the data generation software, having different reference to the time makes it impossible to integrate the different types of data being generated. Additionally, it significantly affects the accuracy of critical Internet of Things (IoT) and machine learning models that rely on precise timestamps within a narrow window for detection, alerts and action.
In this paper, a full framework is introduced to address the time synchronization challenge using the industrial standard and adopting Wellsite Information Transfer Specification Markup Language (WITSML). The framework tackles the problem using three integrated steps. The first step is identifying time drift from each data source. The second step is fixing the time drift by intelligently correcting the data based on trends of the drift. The final step is deploying an IoT device to ensure GPS level time. The paper will demonstrate how such a framework can help in detecting potential satellite communication failures.
The solution improves drilling data quality and provides accurate early warning signs of operational issues. Additionally, it enables the use of fully synchronized real-time data sources to perform analytics and engineering calculations to further advance drilling automation.
Long-Term Inflow Profile Monitoring with Markers in Horizontal Gas Wells with Multistage Hydraulic Fracturing
Authors: Katashov Alexander , Ovchinnikov Kirill , Belova Anna , Husein Nadir , Buyanov Anton , Suprankov Kirill 
Published: November 2020

Summary
Traditional methods of inflow profile monitoring are not always effective, especially when tight reservoirs are considered. One of the most progressive solutions is the use of marker technologies based on the markers' selection and subsequent identification in fluid taken at wellhead [1]. The main advantage of this technology is that there is no need to suspend work for hoisting operations in order to obtain informative field-geophysical data during well operation. The main subject of work is a fractured gas well with pumped marked quartz polymer-coated sand.