bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aging LNG Static Equipment Life Extension - A Case Study
Authors: Sabry Houssam 
Published: November 2018

Abstract
ADNOC LNG is a pioneer LNG and Gas Operator, Owner and Exporter. ADNOC LNG owns and operates three LNG Trains in addition to operating five Gas Processing and Export Trains.
ADNOC LNG operates its facilities from Das Island which is approximately 186Km Offshore Abu Dhabi. ADNOC LNG commissioned two of its three LNG trains in 1977. Third LNG train was commissioned in 1994. The design lifetime of all the trains is 30 years.
Trains 1 and 2 have gone through a life extension study to extend its lifetime from 2007 till 2019. Furthermore, recently another longevity study was concluded to increase the lifetime of Trains 1 and 2 till 2024. Further study is being conducted to further extend the design lifetime of the three LNG trains till 2040. This paper shares ADNOC LNG experience in terms of lifetime extension in terms of LNG static equipment reviews for assets life extension.
These life extension studies were done through an ADNOC LNG multidisciplinary team. The team reviewed the entire LNG facilities. Facilities were divided into the following systems: –Static Equipment–Safety Critical Equipment–Rotating Equipment–Civil and Structures–Electrical and Instrumentation
The static equipment assets life extension exercise started with data collection from all available static equipment integrity systems and tools. The following systems were used to extract data: –Inspection Management System – containing the entire inspection and maintenance records for static equipment–Risk Based Inspection (RBI) System–Risk Based Assessment (RBA) exercises–Focused Integrity Reviews–CMMS (SAP Records)–Root Cause Analysis (RCA) reports–Fitness for Service (FFS) exercise–Major Turn Around (TA) Reports
These data in terms of inspection and failure history, degradation rates, probability of failure and FFS results were all analysed to answer one question, which is whether these static equipment degradation rate would allow them to survive till 2040 and in case this is not possible then what would be the recommended actions, cost for these actions and its schedule. Hence a tecno-econimc evaluation for the recommendations was done.
In the paper ADNOC LNG will share the methodology of evaluation, share few cases in terms of longevity like the LNG storage tanks and export jetties in addition to key findings of the study.
Invisible Lost Time Initiative ILT in Drilling-Toward More Efficiency, High Performance and High Profitability-Do More with Less
Authors: Shamsi Juma Al , Nauimi Mouza Al , Hosani Faisal Al , Sairi Abdulrahman Al 
Published: November 2018

Abstract
Dropping of oil price has significant influence in Oil & Gas industry in term of cost saving. A lot of efforts and initiatives were made in order to sustain with the current situation and to facilitate the Oil and Gas industry to lead the market. Existing of alternative energy is another challenge which threats the market.
ILT initiative was introduced in the Drilling Function to Drill Wells in more efficiency and high performance considering the priority for safety and integrity. The main objective of ILT is to identify the area's of invisible lost time, track it and to Eliminate As Low As Possible (ALAP) and to Drill Well in Shortest Time compare it with Best Of Best Wells (BOB)
During the period when the oil price was high, a lot of expansion in terms of rigs and manpower were there, where there are no focus in the performance of the wells which lead to big inflation in well duration resulted by Non Production Time and Invisible Lost Time.
Invisible Lost Time is the hidden operation time which is lost due to many factors such as shortage of the key players, lack of experience, reluctant of people and shortage of proper equipment & tools.
All these factors are affecting the main drive mechanism in the well which is, the ROP, Tripping Speed and Connection Time. A clear road map for the project was set for three phases. Pilot Phase, where focus was on the mentioned areas and identify the gaps and lesson learns. 2nd Phase, where the gaps can be filled and the lesson learns can be applied to get more results. The Final Phase were can be implemented to all the rigs after Healthy Check were implemented in Phase I & Phase II.
This Paper will describe clearly the Objective of the ILT Project, The Key Elements of ILT; the Challenges were faced to achieve the Target, Case Study and Recommendation.
Detection of Stuck Pipe Early Signs and the Way Toward Automation
Authors: Alshaikh Abrar A. , Albassam Mohammed K. , Al Gharbi Salem H. , Al-Yami Abdullah S. 
Published: November 2018

Abstract
The earlier a stuck pipe incident is predicted and mitigated, the higher the chance of success in freeing the pipe or avoiding severe sticking in the first place. Time is crucial in such cases as an improper reaction to a stuck pipe incident can easily make it worse. In this work, a novel and practical model was developed using real-time drilling data to automatically detect leading signs of stuck pipe during drilling operations and communicate the observations and alerts, sufficiently ahead of time, to the rig crew for avoidance or remediation actions to be taken.
The model uses key drilling parameters to detect abnormal trends that are identified as leading signs to stuck pipe. The parameters and patterns used in building the system were identified from published literature and historical data and reports of stuck pipe incidents. The model is designed to be implemented in the real-time drilling data portal to provide an alarm system for all oil and gas rigs based on the observed abnormalities. The alarm is to be populated on the real-time environment and communicated to the rig crew in a timely manner to ensure optimal results, giving them more time to prevent or remediate a potential stuck pipe incident.
Testing the model on several wells showed promising results as anomalities were detected early in time before the actual stuck pipe incidents were reported. It further facilitated better understanding of the underlying physics principles and provided awareness of stuck pipe occurance. It improved monitoring and interpretating the drilling data streams. Beside such pipe signs, the model helped detecting signs of other impeding problems in the downhole conditions of the wellbore, the drilling equipment, and the sensors.
The model exceptionally uses the robustness of data-based along with the physics-based analysis of stuck pipe. This hybrid model has shown effective detection of the signs observed by experts ahead of time and has helped providing enhanced stuck pipe prediction and risk assessment.
Coupling Low Salinity Water Flooding and Steam Flooding for Heavy Oil in Sandstone Reservoirs; Low Salinity-Alternating-Steam Flooding LSASF: A Novel EOR Technique
Authors: Al-Saedi Hasan, N. , Flori Ralph E. , Mortadha Alsaba 
Published: November 2018

Abstract
Recently Al-Saedi and Flori et al. (2018a) studied the potential of low salinity alternating steam flooding (LSASF) in laboratory Berea sandstone cores and the results were promising for increasing heavy oil recovery. In this study, we applied the new LSASF method on oil-impregnated reservoir sandstone cores from the Bartlesville sandstone reservoir in eastern Kansas. These samples contain high viscous oil up to 600 cp.
Combining low salinity water (LS water) flooding and steam flooding is a novel idea which takes advantage of the relative strengths of both methods. LS water has been extensively studied by many researchers with promising results. Steamflooding is also much studied for applications particularly in heavy oil fields where recovery is increased by reducing the oil viscosity. The density difference between steam and heavy oil raises difficult problems in some reservoirs that can only be solved by injecting water with steam. In particular, those problems are gravity override, channeling, and early breakthrough.
In this study, numerous reservoir sandstone cores were retrieved from the Bartlesville sandstone reservoir and cleaned, dried, vacuumed, and saturated with formation water (FW) that was identical to the reservoir FW. The water was displaced with a heavy crude oil to achieve Swi, and pre–aged for five weeks at 90°C. These cores were then flooded with 2 PV FW and then followed by different cycles of LS water + steam. A variety of LS water salinities were used (d4FW, d8FW, and d40FW).
The laboratory experiments showed that optimum recovery is achieved by diluting the FW 40 times and using the same water in a shorter steam cycle. The oil recovery was 70.6% of the OOIP, which represents a quite high percentage of oil recovery in heavy oil reservoirs. The results of contact angle showed that the cores flooded using the novel LSASF were shifted to more water–wet conditions. Chemical analyses showed that using this novel LS water plus steam method improves recovery by enhancing the permeability of the rock, reducing precipitation caused by LS water flooding, and increasing dissolution of minerals which increased oil recovery. We also developed the LS water alternating steam flood in order to gather the benefits of LS water itself and to reduce oil viscosity by steam and prevent the aforementioned steam problems by using LS water.
Integrated Data Analytics and Visualization for Reservoir and Production Performance Management
Authors: Mohan Richard , Narayanan Ram , Al Yazeedi Haifa , Al Naqbi Tayba , Willems Maarten , Escorcia Alvaro , Saputelli Luigi , Abdulla Nada , Gioria Gian Marco 
Published: November 2018

Abstract
ADNOC operates its onshore and offshore fields through its operating companies. In order to effectively govern and steer the operations, it is essential for ADNOC to monitor, track and measure key reservoir and production performance indicators efficiently on time. In this regard, ADNOC Upstream has established an integrated reservoir and production performance visualization environment that enables upstream departments to efficiently monitor the performance of onshore and offshore operations at different levels from the headquarter in a collaborative manner with the operating companies.
The integrated visualization environment is built on four key components, the upstream data hub, visualization, process automation and data governance which forms the common foundation for various upstream projects, enabling multi-project collaboration, reporting integrity, common business data and KPI definitions. The datawall in Thamama Collaboration Center is powered by the integrated reservoir and production performance visualization dashboards configured on objective based themes such as Reservoir Management, Production Assurance, Business Plan Assurance and others required by different departments. The visualization capability is enabling engineers and managers in the upstream directorate, to monitor key reservoir and production performance information on a daily and periodic basis, and the solution facilitates collaborative review sessions with OPCO team either on a regular or adhoc manner and to initiate and track actions.
The integrated visualization environment shall be continuously enhanced to add more themes based on business needs, and to carry out advanced data analytics to predict and forecast performance leveraging the established foundation. Business processes shall be streamline through automation of the performance measurement processes to achieve higher degree of digital transformation.
Comparison of Steam Front Shape During Steam Flooding Process Under Varying Steam State Condition: Numerical Analysis
Authors: Sun Fengrui , Yao Yuedong , Li Guozhen , Li Xiangfang , Sun Jian 
Published: November 2018

Abstract
It has been reported that superheated steam can greatly increase the oil recovery efficiency. After superheated steam injection, the viscosity of heavy oil is greatly reduced. Besides, the permeability of the rock is increased. However, this previous works were focused on the chemical respects of oil displacement mechanisms by superheated steam injection. The difference in physical heat transfer between superheated steam and wet steam has not been revealed.
At present, there is a lack of study of its physical heating on the increase of productivity. In this paper, a numerical study is conducted to reveal the difference between superheated steam and wet steam during the steam flooding process. The contribution of physical heating on the productivity is studied.
Simulation result show that: (a) The effect of physical heating of superheated steam can be neglected. The contribution of chemical reactions of superheated steam with heavy oil and rock minerals is the dominant factors for oil recovery mechanisms under superheated steam injection. (b) When the temperature is higher, the pressure wave propagates more widely. However, the difference in pressure field between superheated steam and wet steam with a steam quality of 1.0 is negligible. (c) There exists severe steam fingering phenomenon under superheated steam injection. This is because the superheated steam is completely vaporized which greatly increases its percolation capacity.
This work sheds light on the contribution of physical heat transfer on temperature spread in reservoir under varying steam state during the steam flooding process. This work pointed out that the study of chemical reactions should be the research direction of the next step in oil displacement mechanisms by superheated steam injection.
Applying Automated Model Building to Predictive Maintenance in Oil and Gas
Authors: Herve Philippe , Moore Keith , Rosner Marla 
Published: November 2018

Abstract
Predictive maintenance has become a major focus for the largest industrial companies because of the value it derives, including reduced downtime, improved efficiency, reduced maintenance costs, and others. Success of predictive maintenance programs is achieved when data, analytics, and subject matter expertise intersect. While data and subject matter expertise are always available, analytics talent is often lacking or facing numerous challenges which hinders the success of predictive maintenance programs.
Automated model building (AMB) aims at delivering artificial intelligence to the fingertips of industrial companies and hence ensuring the success of predictive maintenance programs without the need of large data science organizations.
The automated model building platform ingests the operational (sensor) and failure/fault data and automatically builds AI models to predict the remaining useful life for the asset. The patented technology behind the platform drives feature engineering and model selection which allows customers to automatically create numerous new variables from the sensor data and tests thousands of different models. The platform will then select the optimal set of variables and the model that will achieve the best performance.
The entire process can be performed in a matter of few minutes without the need to know the details of all AI models. The platform also gives details on the selected models, which aids with interpretability.
This paper will discuss why automated model building and artificial intelligence are needed to deliver effective, scalable predictive maintenance to the oil and gas industry, as well as specific use cases in which AI-powered automated model building has been applied.
Characterization of the Pore System of Cretaceous Limestone Samples from Onshore UAE Field, Using Lab NMR, MICP, Petrography and Electrical Resistivity Measurements
Authors: Zakaria Hasan , Gomes Jorge 
Published: November 2018

Abstract
The use of Lab NMR, MICP, electrical resistivity measurements and conventional core analysis coupled with petrographic investigations, enabled the authors to understand the pore structure of several carbonate samples and establish a link between pore geometries, Archie's cementation exponent, capillary pressure behavior and NMR T2 response.
The lab work was performed on 68 carbonate plugs retrieved from 5 vertical wells completed on a Cretaceous carbonate reservoir, and located at different structural positions (from crest, mid-flank and down-flank areas), capturing different hydrocarbon column heights and, therefore, different degrees of diagenesis and porosity degradation. The equipment used was Magritek 2MHz NMR Rock Analyzer for the T2 distributions, Micromeretics for MICP, Pantera for the ambient resistivity measurements and PORG-200TM for the conventional core analysis. The petrography was done using conventional polarized light microscope.
From the combined analysis of NMR T2 spectrum and electrical resistivity measurements we concluded that the magnitude of the Archie's cementation exponent "m" is greater when the samples exhibit variability within the largest pore class ranges (meso to macro scale) than on the smaller pore class range (micro to meso scale). The bi-modality of the pore system tends to increase the cementation exponent "m". The results of this study also shed some light on the rate of porosity degradation with depth which was correlated with the amount of stylolitization (pressure solution) downflank.
The novelty of this work is the combined analysis of NMR, electrical resistivity and petrography on the assessment of the pore structure of limestone samples. The main finding of this work has direct application to the computation of water saturation models for rocks with dual porosity systems. If the understanding of the pore structure is not taken into consideration during petrophysical analysis, the volumes of hydrocarbons could be under/overestimated with consequences on the reserves assessment.
Producing from Residual Oil Zone ROZ: Concept and Strategy for Kuwait
Authors: Dhote Prashant Dhote , Al-Bahar Mohammad , Cole Anthony , Al-Sane Amal , Bora Anup , Sreenivasan Ashique 
Published: November 2018

Abstract
Residual Oil Zones (ROZs) are an area of incrasing attention from hydrocarbon E&P industry with ever depleting reserves in known oil accumulations and advent of Carbon Dioxide (CO2) Capture and Storage needs and technology. ROZ can serve as viable solution to both the future problems as a possible vast new oil resource and a prospect for reducing carbon emission. ROZs can be defined as thick pile of low-quality reservoir rock below traditional oil-water contact with about residual oil saturations of mainly irreducible oil resulting from the natural flushing of reservoir due to buoying forces and aquifer action in geological past in earlier oil-filled part of reservoir. The production of oil from ROZs from such reservoirs is technically and economicaly feasible through application of enhanced oil recovery techniques - largely through missible CO2 flooding/injection in the zone because of the nature of fluid and reservoir rock. The depostional and tectonic regime in the Kuwait Petroliferous Basins is investigated to demonstrate the occurrence of and independently assess ROZ potential. The understanding of Kuwait Petroliferous Basin indicates that ROZs might be developed by hydrodynamic actions associated with tectonic regime. The degradation of oil by water action and related increase of sulfur content of crude oil can be used as workable proxy for identification ROZ potential of the rerservoir. The regional mapping, understanding of tectionic history and regional systhesis of crude oil composition shows an extensive stratigraphic and lateral existence of ROZ potential across the Kuwait Petroliferous Basin.
This study aims to provide strategic roadmap and detail data acquisition program that will reveal ROZ production potential in Kuwait for Kuwait Oil Company (KOC).
Exploring for Wolfcamp Reservoirs, Eastern Shelf of the Permian Basin, USA, Using a Machine Learning Approach
Authors: Ribet Bruno de , Wang Peter , Meers Monte , Renick Howard , Creath Russ , McKee Ryan 
Published: November 2018

Abstract

Objectives/Scope
The objective was to leverage prestack and poststack seismic data in order to reconstruct 3D images of thin, discontinuous, oil-filled packstone pay facies of the Upper and Lower Wolfcamp formation (Sakmarian time: 293-296 Ma).

Methods, Procedures, Process
The well-to-seismic tie was carefully established using synthetic seismograms, which enabled the facies log to be properly associated with the corresponding seismic samples. The seismic data were all resampled from 2 ms to 0.5 ms in anticipation of being able to recover facies thicknesses on the order of 2 m. Six neural networks with diverse learning strategies were trained to recognize the nine facies classes in the high-resolution seismic stack: Instantaneous Frequency, Instantaneous Q Factor, Inversion (P-Impedance), Semblance, Dominant Frequency, Most Negative Curvature, and eight Angle Stacks, using a two-stage learning and voting process.

Results, Observations, Conclusions
At the wells, the nine facies were reconstructed from seismic at a 97% accuracy rate. The bootstrap classification rate, a proxy for blind well testing, was over 80%, which indicates a high-quality modeling process. The pay facies was described with no false positives or false negatives. In the 3D seismic volume between the wells, the procedure produced a Most Likely Facies volume (unsmoothed and smoothed), and nine individual Facies Probability volumes. The pay facies was visualized in a 3D voxel visualization canvas using opacity, and also in a two-way time thickness map. The usable vertical and horizontal resolution was much greater than that of the original seismic. Based on these classification results, additional drilling locations were chosen to further target the oil-filled packstones.

Novel/Additive Information
The classification results were created by neural networks, which can be used as a substitute for traditional AVO, inversion and cross-plotting techniques for seismic reservoir characterization. The time need to create the Machine Learning results for this small dataset was on the order of ten minutes.