bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expanding Polymer Injectivity Tests on a Second Giant Carbonate UAE Oil Reservoir at High Salinity & High Temperature Conditions.
Authors: Baloch S.A. , Leon J.M. , Masalmeh S.K. , Chappell D. , Brodie J. , Romero C. , Al Mazrouei S.H. , Al Tenaiji A. , Al Balooshi M. , Igogo Arit , Azam M.U.F. , Maheshwar Y.K , Dupuis G. 
Published: December 2021

AbstractOver the last few years, ADNOC has systematically investigated a new polymer-based EOR scheme to improve sweep efficiency in high temperature and high salinity (HTHS) carbonate reservoirs in Abu Dhabi (Masalmeh et al., 2014). Consequently, ADNOC has developed a thorough de-risking program for the new EOR concept in these carbonate reservoirs. The de-risking program includes extensive laboratory experimental studies and field injectivity tests to ensure that the selected polymer can be propagated in the target reservoirs.A new polymer with high 2-acrylamido-tertiary-butyl sulfonic acid (ATBS) content was identified, based on extensive laboratory studies (Masalmeh, et al., 2019, Dupuis, et al., 2017, Jouenne 2020), and an initial polymer injectivity test (PIT) was conducted in 2019 at 250°F and salinity &gt;200,000 ppm, with low H2S content (Rachapudi, et al., 2020, Leon and Masalmeh, 2021). The next step for ADNOC was to extend polymer application to harsher field conditions, including higher H2S content. Accordingly, a PIT was designed in preparation for a multi-well pilotThis paper presents ADNOC's follow-up PIT, which expands the envelope of polymer flooding to dissolve H2S concentrations of 20 - 40 ppm to confirm injectivity at representative field conditions and in situ polymer performance. The PIT was executed over five months, from February 2021 to July 2021, followed by a chase water flood that will run until December 2021. A total of 108,392 barrels of polymer solution were successfully injected during the PIT. The extensive dataset acquired was used to assess injectivity and in-depth mobility reduction associated with the new polymer.Preliminary results from the PIT suggest that all key performance indicators have been achieved, with a predictable viscosity yield and good injectivity at target rates, consistent with the laboratory data. The use of a down-hole shut-in tool (DHSIT) to acquire pressure fall-off (PFO) data clarified the near-wellbore behaviour of the polymer and allowed optimisation of the PIT programme.This paper assesses the importance of water quality on polymer solution preparation and injection performance and reviews operational data acquired during the testing period.Polymer properties determined during the PIT will be used to optimise field and sector models and will facilitate the evaluation of polymer EOR in other giant, heterogeneous carbonate reservoirs, leading to improved recovery in ADNOC and Middle East reservoirs.
Well Lifting Capability to a Green Power Generation Using Microturbine Generator
Authors: Sedaralit Mohd Faizal , Ibrahim M Imran Iskandar , Maam Azman 
Published: December 2021

Abstract
A new way of utilizing access gas to power generation using newly developed technology named micro-turbine electric generator (mTEG). This technology is using relatively low gas flow rate: same region of gas-lift consumption between 0.3 – 0.5 MMscf/d of power generation for a minimum of one kilo Watt (1kW) per unit. The technology is classified under green category is due to no combustion process instead using pressure differential as the mechanism for power generation. Therefore, it supports PETRONAS’ net carbon zero program especially for unmanned platform operation.
The mTEG is simple unit developed based on differential method by allowing gas to flow inside for rotation without combustion for power generation. Flow and pressure requirements are relatively low at 0.4 MMscf/d at 10 bar, respectively. One of gas supply sources to run the unit is coming from well or gas lift skid. Per skid of mTEG it can produce up-to 4 kW power. This technology helps to support unmanned platform operation to reduce operation cost and moreover, this technology is compact and superior than solar power scheme unit. Life cycle cost between the two technologies was indicated superior saving is possible for mTEG technology.
Completed prototype model – integrated skid and in 2019 team managed secured full in-house development under prototyping and engineering centre unit located in PETRONAS Research Sdn. Bhd. (PRSB), Bangi. One of the technologies used to produce the mTEG unit is 3D printer with also available in PRSB. Passed factory acceptance test (FAT) in 2019 based on positive outcome of endurance test performance. Embarked detail engineering for installation of the skid and completed electrical and installation in 2020 and the skid unit is expecting to provide 1 kW power by end of this year at field E and upon completing the test, this technology will be able to benefits other operating unmanned platform or event small plant operation.
The technology was designed for green power generation: operation greenification with no combustion elements instead provide flexibility for recycling of the gas and reuse for another means such as gas lift gas application to improved oil recovery.
Horizontal Water Injection Wells: Injectivity and Containment of Injection-Induced Fractures
Authors: Hwang Jongsoo , Sharma Mukul , Chiotoroiu Maria-Magdalena , Clemens Torsten 
Published: December 2021

Abstract
Horizontal water injection wells have the capacity to inject larger volumes of water and have a smaller surface footprint than vertical wells. We present a new quantitative analysis on horizontal well injectivity, injection scheme (matrix vs. fracturing), and fracture containment. To precisely predict injector performance and delineate safe operating conditions, we simulate particle plugging, thermo-poro-elastic stress changes, thermal convection and conduction and fracture growth/containment in reservoirs with multiple layers.
Simulation results show that matrix injection in horizontal wells continues over a longer time than vertical injectors as the particle deposition occurs slowly on the larger surface area of horizontal wellbores. At the same time, heat loss occurs uniformly over a longer wellbore length to cause less thermal stress reduction and delay fracture initiation. As a result, the horizontal well length and the injection rates are critical factors that control fracture initiation and long-term injectivity of horizontal injectors. To predict fracture containment accurately, thermal conduction in the caprock and associated thermal stresses are found to be critical factors. We show that ignoring these factors underestimates fracture height growth.
Based on our simulation analysis, we suggest strategies to maintain high injectivity and delay fracture initiation by controlling the injection rate, temperature, and water quality. We also provide several methods to design horizontal water injectors to improve fracture containment considering wellbore orientation relative to the local stress orientations. Well placement in the local maximum horizontal stress direction induces longitudinal fractures with better containment and less fracture turning than transverse fractures. When the well is drilled perpendicular to the maximum horizontal stress direction, the initiation of transverse fractures is delayed compared with the longitudinal case. Flow control devices are recommended to segment the flow rate and the wellbore. This helps to ensure uniform water placement and helps to keep the fractures contained.
The Concept of Floating Shipyard
Authors: Samir Ghowel Ahmed 
Published: December 2021

Abstract
Shipyards are known as a land-based facility that ships steer to for docking and repair. This study represents an introductory conceptual study for a new principle of developing a Floating shipyard: changing the phenomena of a fixed site shipyard into a self-propelled floating shipyard.
This Floating Shipyard can travel and conduct dry-docking activities at the client's location or even lift the client's vessel and travel to the F-Yard extension, taking advantage of having comprehensive repair or conversion.
The arising challenges to this floating shipyard, such as lay-offs, restructuring, and environmental legislation, requires a balanced solution. The solution of all this relies on adapting talent management and competitive production tools during this massive undergoing change in its backbone structure.
The Floating shipyard, here named "F-Yard", changes the mindset of dry-dock. This is because F-Yard travels to the client's location or is capable of pick the client's unit nearby the cargo destination, where it can complete the required dry-dock or repair.
The cutthroat advantage of the F-Yard comes from self-propelled, where the other approaches depend on others for mobilizing and anchoring from one location to another.
In addition, F-Yard could serve other industries, due to its fully equipped workshops, that able to serve different market needs such as oil and gas and renewable energy...etc. These open the door for a variety of business scope.
F-Yard depends on front-end engineering and marketing teams to optimize its route and sort the supply chain requirements.
Backpressure Minimization as Objective Function for Production Optimization in Absence of Wells Performance.
Authors: Corti Mauro , Montini Marco , Gioja Giorgio 
Published: December 2021

AbstractIn the Oil & Gas sector, the production optimization is one of the most challenging problem, since it involves many operational variables linked by complex relationships. Moreover, during the asset life cycle those parameters could change. Conflicts and interactions between variables, constraints, and operational limitations could be solved holistically by an optimization tool based on an Evolutionary Algorithm. The algorithm searches for the optimum field configuration from the operational point of view, leading to the production maximization.Eni Production Department developed a tool based on this algorithm for management and optimization of surface asset hardly focused on field viewpoint. e-Rabbit (Risked Algorithm for Biogenetical Balance Integration Tool) provides integration for reservoir, well, network and process models.The present work has been developed to overcome a recurrent problem in the Oil & Gas business: the lack of data. There are many cases in which this situation occurs, for example in old fields where measurement tools and digitalization are not so widespread or in assets characterized by many wells and complex gathering systems, in which the detail on well performances could not be available. e-Rabbit, to perform its optimization, requires that information so, under those conditions, somehow, it is necessary to find another way to be able to optimize and manage the surface asset.A novel technique to optimize the whole production system has been introduced, whose objective function rely on backpressure minimization. To verify its effectiveness two case studies have been analyzed comparing the proposed optimal configuration with the output of the classical e-Rabbit - optimization.
Automated Drilling Data Quality Control Using Application of AI Technologies
Authors: Battocchio Francesco , Sreekantan Jaijith , Arnaout Arghad , Benaichouche Abed , Al Shamsi Juma Sulaiman , Awad Mohamad Abdul Salam , Alnuaimi Mohamed Ahmed , Peraza Luis Ramon Baptista 
Published: December 2021

AbstractDrilling data quality is notoriously a challenge for any analytics application, due to complexity of the real-time data acquisition system which routinely generates: (i) Time related issues caused by irregular sampling, (ii) Channel related issues in terms of non-uniform names and units, missing or wrong values, and (iii) Depth related issues caused block position resets, and depth compensation (for floating rigs). On the other hand, artificial intelligence drilling applications typically require a consistent stream of high-quality data as an input for their algorithms, as well as for visualization. In this work we present an automated workflow enhanced by data driven techniques that resolves complex quality issues, harmonize sensor drilling data, and report the quality of the dataset to be used for advanced analytics.The approach proposes an automated data quality workflow which formalizes the characteristics, requirements and constraints of sensor data within the context of drilling operations. The workflow leverages machine learning algorithms, statistics, signal processing and rule-based engines for detection of data quality issues including error values, outliers, bias, drifts, noise, and missing values. Further, once data quality issues are classified, they are scored and treated on a context specific basis in order to recover the maximum volume of data while avoiding information loss. This results into a data quality and preparation engine that organizes drilling data for further advanced analytics, and reports the quality of the dataset through key performance indicators.This novel data processing workflow allowed to recover more than 90% of a drilling dataset made of 18 offshore wells, that otherwise could not be used for analytics. This was achieved by resolving specific issues including, resampling timeseries with gaps and different sampling rates, smart imputation of wrong/missing data while preserving consistency of dataset across all channels. Additional improvement would include recovering data values that felt outside a meaningful range because of sensor drifting or depth resets.The present work automates the end-to-end workflow for data quality control of drilling sensor data leveraging advanced Artificial Intelligence (AI) algorithms. It allows to detect and classify patterns of wrong/missing data, and to recover them through a context driven approach that prevents information loss. As a result, the maximum amount of data is recovered for artificial intelligence drilling applications. The workflow also enables optimal time synchronization of different sensors streaming data at different frequencies, within discontinuous time intervals.
New Recommendations for Offshore Wellhead Platform Structural Design Due to Well Conductor Casings Failures: Outcome of a Study Based on Actual Findings
Authors: Varikkodan Abdul Gaffur , Sarkar Anjan , Mohammed Mohammed Eissa 
Published: December 2021

Abstract
Detailed study on structurally failed well conductors on offshore wellhead platforms lead us to believe that existing assumptions of conductors transfer only lateral loadings to wellhead platforms while entire well vertical loading will be carried by conductor itself; could be wrong. The well conductors could become ‘forced’ to carry a very large vertical loads incase the conductors are structurally failed; especially once exceeded its original design life. As such, some new considerations during the wellhead platform design, which need to be followed, are recommended here. These are to cater any catastrophic eventuality of conductor failures which will restrain further collapse of the conductors or to avoid any progressive collapse of the platform. The recommendations are from a study based on actual findings observed recently in the offshore field.
The connection between conductors and platforms are conventionally designed as guided based on the load transfer assumptions. That is the huge vertical loadings from internal conductor casings and associated items were not a concern for platform design structural engineers, traditionally, and as such the conductors were designed to be structurally connected to the wellhead platforms using vertical guides. Due to extended design service life of platforms, in many cases the design life went up to two times of their original design life, severe degradation of structural integrity of the conductors were observed in the field. Structural analysis and assessment were carried out on many old intact and failed conductors, in the offshore field, in order to assess its structural and loading behavior with respect to supporting wellhead platforms.
The study provided that the failed conductors were leaning / collapsing to the wellhead platform resulting in transferring a huge vertical loads which originally were designed to be carried by conductors alone. This huge transfer of vertical loads from conductor to the platform was unexpected and was not considered in platforms original design. Therefore, the platform should have sufficient structural strength to cater such extreme eventuality to avoid the risk of complete collapse. A risk assessment of a tilted / failed conductors indicated that the consequence of total failure of a conductor could be catastrophic in case the platform failed to resist the collapsing conductors. This paper presents the details of the study carried out on aged wellhead platforms, having failed long serving conductors, in Giant offshore field, Abu Dhabi, along with details of new recommendations to be followed while designing new wellhead platforms.
The paper also recommends the structural design consideration to be followed while designing wellhead platforms in-case a conductor repair is necessitated in future.
Pushing the Limits for Open Hole Zonal Isolation – Design and Qualification of Expandable Steel Packer
Authors: Md Noor Mohd Nazri , Batita Nabil , Gaby Mohamed , Riou Yoann , Roselier Samuel , Saltel Benjamin 
Published: December 2021

Abstract
Enhancing gas productivity is linked to multistage stimulation (MSS). Choosing a cemented over uncemented solution is driven by factors such as operational efficiency, drilling practices, and isolation techniques. Swellable and mechanical packers have been used widely. A new packer type, an expandable steel packer, has been qualified recently, the expandable steel packer combines the strengths of mechanical and swellable packers and will provide an option for openhole completions.
The 4.5-in. expandable steel packer design was optimized to meet most demanding applications with the following characteristics: reduced running outside diameter (OD) to 5.6 in., premium assembly technique by crimping, double sleeve pressure self-compensation, and use of nickel alloys for sour environment. After the design of the packer was completed, the 4.5-in. expandable steel packer was qualified according to the API Spec 19OH (API 2018) standard protocol at 15,000 psi with thermal variation between 320°F and 68°F. The packer was tested in a casing with inside diameter (ID) of 6.5 in.
The test casing had an ID of 6.5 in. whereas nominal hole size ranges from 5.875 in. to 6.125 in. It was chosen to simulate a washout and considering the calculated maximum expansion ratio for the steel to verify the 15,000-psi pressure rating capability. The test casing was built with a heat exchanger, high-pressure pump, and pressure and temperature sensors.
The packer was expanded inside the dummy well with all the measuring instruments in place. Expansion pressure signatures were observed as predicted. The analysis of the packer setting pressure curves showed expansion initiation and full casing ID contact. The liquid differential pressure test from each side of the packer proved the internal pressure compensation performed as expected. No leak was observed during the pressure steps of 15.000 psi held for 15 minutes while cycling the temperature from 320°F to 68°F and back to 320°F.
The expandable steel packer utilizes a unique double-sleeve system for self-pressure compensation during ball-drop stimulation operations. The packer expandable sleeve is protected during deployment by the end fittings. Expandable steel packers exhibit robustness during running in hole, enable setting on demand, have a high expansion ratio, require no de-rating vs. hole size, and have low sensitivity to thermal variations.
Optimizing Rotating Equipment Maintenance Through Machine Learning Algorithm
Authors: Beduschi Francesco , Turconi Fabio , De Gregorio Basso , Abbruzzese Francesca , Tiozzo Annagiulia , Amabili Matteo , Prospero Alberto 
Published: December 2021

Abstract
This work highlights the development and results of a Rotating equipment predictive maintenance tool that allows to monitor the status of rotating machines through a synthetic "health index" and early detection of anomalies. The data-driven proposed solution is of great help to maintenance engineers, who, alongside the existing methodologies, can apply an effective tool based on artificial intelligence for early prevention of failures.
Taking advantage of the high availability of remote sensors data, an anomaly detection machine learning model, which relies on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE), has been built. This model is capable of estimating, in real time, the health status of the machine, by matching the sensors actual values with the reference ones based on the Normal Operating Conditions (NOC) periods, that have been previously identified. If an anomalous behavior is detected, the Fault Isolation step of the model allows to evaluate which are the most contributing sensors for the investigated anomaly. These outcomes, combined with a failure mode matrix, which links the sensors deviations with the possible malfunctions, allows to highlight the most likely failure modes to be associated to the investigated anomaly.
The developed predictive tool has been implemented on operating sites and it has demonstrated the capability to generate accurate warnings and detect anomalies to be processed by the maintenance engineers. These alerts may be aggregated into events in order to be monitored and analyzed by remote and on site specialists. The availability of alerts gives to the users the possibility to predict any deterioration of the machines or process fluctuations, that could lead to unplanned events with consequent mechanical breakdowns, production losses and flaring events. As a consequence, tailored operative adjustment to prevent critical events can be taken. Thanks to the tool, it is also possible to monitor over time the equipment behavior in order to provide suggestions for maintenance plans optimization and other useful statistics concerning the most recurrent failure.
The tool's innovative feature is the ability to utilize the giant amount of data and to reproduce complex field phenomena by means of artificial intelligence. The proposed tool represents an innovative predictive approach for rotating equipment maintenance optimization.