bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Power Generation Utilizing Turbine Inlet Air Cooling (TIAC)
Authors: Kutbi Jehad Fawzi , Georgiou Ioannis 
Published: October 2022

Abstract
Electricity demand is growing globally due to increasing population and growing economy, it is projected that the global energy demand will increase by 2.3% a year. One of the most reliable ways to generate electricity is by using gas turbines. Although gas turbines are efficient in terms of generating electricity, their power output significantly varies throughout the year due to temperature variance, especially in the summer time where the temperature can reach up to 50 C°. This is because gas turbines power output is proportional to the mass flow of air/fuel through the engine. Since the volumetric flow of air through the engine is essentially constant, mass flow therefore changes with temperature of the air entering the compressor of the gas turbine. When the ambient temperature is high, the power output and efficiency are reduced. Thus, by cooling the gas turbine inlet air, the turbine power output is significantly increased and its efficiency improves. Cooling the combustion air even by few degrees can improve the power output substantially. This is because cool air is denser and therefore gives the turbine a higher mass flow-rate and pressure ratio, resulting in increased turbine output and efficiency as much as 0.5% per degree Fahrenheit or about 1% per degree Celsius. Another advantages in cogeneration is the higher exhaust mass flows resulting in more steam generation. Turbine inlet cooling can be installed in most of the turbines of Saudi Aramco in order to increase their nominal capacity and improve their NOx emission. This paper will analyze the mechanism behind the turbine inlet cooling and will presents the results of a techno economical study conducted for retrofitting a TIAC system on the existing GE Frame VII gas turbines of Abu Safah and Qatif plant facilities.
Innovative Approach to Develop an Old Complex Reservoir Bearing Multi-Billion Barrels of Reserves in a Middle-East Carbonate Field
Authors: Younes Dania Wayel , Keshtta Osama Mohamed , AlAmeri Khaled Husain , Bigno Yann 
Published: October 2022

Abstract
This paper showcases the development approach recently adopted in an offshore reservoir bearing multi billion stock tank barrels of oil-in-place that solely depended on natural depletion starting from the year of 1962 until the introduction of the crestal gas injection mechanism in 1994, yet less than three percent of this potential has so far been recovered! This low recovery can be justified by the very complex nature of this reservoir varying from the expected highly fractured crest to the very poor characteristics of the matrix spreading towards the down flank, in addition to the high competition between this as a lazy reservoir and the other high potential reservoirs to produce against a high-pressure system of surface facilities. Even though the very first oil shipment in 1962 from this field was exported from this reservoir, all these surface and subsurface bottlenecks have been making its development so challenging that it had to be put to a halt yielding a hydrocarbon recovery that is less than three percent of its potential in the past 60 years.
A new development approach was adopted in this reservoir introducing the line drive water injection mechanism as a basis for all future development, in addition to introducing artificial means in order to overcome the high-pressure system of the existing surface facilities. Several uncertainties were encountered linked to this type of development plan for this type of complex reservoir such as well spacing (OP/WI), well architect (short horizontal vs. MRC/ERD vs. deviated), well completion, well productivity/eruptivity and for water injection as it is the first time to inject water in this type of reservoirs water injectivity, water quality, scale risk..etc. So, a dedicated de-risking plan was introduced & implemented to try to overcome and mitigate all these challenges and risks before full implementation of the proposed development plan.
The line drive pilot (OP/WI) plan was implemented mirroring the future long term development plan for the main purpose of proving the economical feasibility of the line drive recovery mechanism in such a reservoir while maximizing the acquisition of associated static and dynamic monitoring data. This will ultimately appraise and feed into the long-term development plan set for this reservoir while paving the way for it to reach its true potential and meet its future production target.
This paper discusses both the de-risking pilot design and its results, which overall, support the implementation of this development plan. The pilot was started with the oil producer since Aug-2020 followed by the water injection since Mar-2021.
Re Configuring and Restaging Centrifugal Compressors for Depleting Reservoir Pressures
Authors: Sahadevan Roy 
Published: October 2022

Abstract
Compressor stations are essential to export natural gas through pipelines. Gas reservoirs deplete over the years, as a consequence, gas compressor stations experience a lower-than-normal suction pressure. Hence it becomes increasingly difficult for the existing compressor hardware to export gas to destination.
Variable speed compressors are designed with certain amount of flexibility to accommodate changes in inlet pressure conditions. However significant changes in inlet operating conditions, because of reservoir depletion, adversely affect natural gas export through centrifugal gas compressors.
This prompts operating companies to develop alternate solutions to export low pressure gas. In the process, compressor modification opportunities need to be identified, developed and be applied to facilitate gas export at revised inlet conditions, either by changing pressure ratio across the compressor or gas flow rate or both. Rearranging parallel compressors to series operation is one of the likely methods of achieving higher pressure ratio for gas export. Restaging of existing compressor may also be required to optimize compressor operation.
Cost effective projects are delivered by lining up the compressors differently and by changing compressor internal stator and rotor components to suit new operating conditions, avoiding off-design operation, and allowing the modified compressor to operate in an envelope with increased operational efficiency. This is also known as upgrade or re-rating of compressors.
The paper is about rearranging and restaging existing gas compressors in onshore gas compressor stations, to export natural gas, when the gas wells experiencing significant pressure depletion.
The paper explain method of establishing maximum gas export rate, highest pipeline back pressure for gas export and the compressor discharge pressures at battery limits. The adequacy checks are performed on compressors, motors, and all associated auxiliary systems.
A Fit-For-Purpose ESP Technical Solution Enables 17% Production Increase in Complex Reservoirs in Mature Fields in Ecuador
Authors: Leon Fernando , Pancho Juan , Franco Ernan , Balseca Diego , Ruales Fernando , Quinzo Maria Elena , Perez Mauricio , Villalobos Jorge 
Published: October 2022

Abstract
A fit-for-purpose electrical submersible pump (ESP) technical solution, based on a combination of pump types and an innovative extended gas-handling device resulted a production increase of 17% in four mature fields in Ecuador. The extended gas-handling device enabled a superior performance of ESP systems to decrease downhole parameter fluctuations normally observed in challenging production conditions to improve oil production.
Historically, ESP operations in mature fields in Ecuador have posed extremely demanding conditions, where reservoir depletion generates lower static and flowing pressures that are typically associated with an increment in free-gas content in the produced fluid. Low flow rates, high lifting requirements, and high gas content degrade ESP performance and reliability while complying with field economics.
This innovative ESP technical approach improves fluid hydraulics through the pump by combining a newly developed extended gas-handling device with proper selection of pump stage models to avoid production losses due to gas blockage, leading to an increase of pressure drawdowns and the maximization of flow rates. Additionally, when the reservoir has had a fracturing job to improve its productivity, the ESP selection process becomes even more challenging. Therefore, this ESP configuration incorporates a robust technology to face abrasive conditions, and flow rates are thoroughly controlled during the well's initial production phase to mitigate excessive migration of proppant into the pump. The technical solution developed has helped to increase production and improve the equipment reliability. On the 47 wells where this technology was successfully adopted, a 17% increase in oil production was observed and was attributed to the system's capacity to maximize reservoir drawdowns, with intake pressures as low as to 150 psi, while maintaining stead operation in gassy conditions. The enhanced equipment performance obtained with this technology contributed to a reduction in the number of ESP failures having as root cause low productivity, which decreased from 34 in 2019–2020 to 16 in 2021–2022 with zero ESP-related failures recorded among the 47 wells where this technology was installed.
This innovative ESP configuration has emerged as a pioneering solution in the region to fulfill challenging production conditions in mature fields where a cost-effective solution is required. Furthermore, it opens a new spectrum to maximize oil production in mature fields by expanding the currently understood technical boundaries for ESP operation in low-flow gassy applications.
An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach
Authors: Tariq Zeeshan , Gudala Manojkumar , Xu Zhen , Yan Bicheng , Sun Shuyu , Mahmoud Mohamed 
Published: October 2022

Abstract
Carbonate rocks are very heterogeneous and have very complex pores structure due to the presence of intra-particle and inter-particle porosities. This makes the characterization and evaluation of the petrophysical data, and the interpretation of the carbonate rocks a big challenge. Porosity in complex lithologies, particularly carbonate reservoirs, is difficult to measure using conventional (Quad-Combo) well logs. Nuclear Magnetic Resonance (NMR) derived porosity is considered the total porosity "gold standard", as it is measured exclusive of matrix and mineralogy. However, due to NMR tools existing as relatively new technology, and the extra expense in logging runs and rig time, most wells lack these data. Most of the existing approaches to predict the rock porosity was developed on the Neutron-density porosity logs that usually are resulted in inaccurate estimation, especially in the fractured zone and highly dolomitized rocks. In this study, deep learning model was efficiently utilized to predict the Nuclear Magnetic Resonance based effective porosity in carbonate rocks. The petrophysical well logs such as bulk density, gamma-ray, neutron porosity, photoelectric log, and caliper log were used as predictors. A total of 3800 data points were obtained from several wells located in a carbonate reservoir. A comprehensive data exploratory analysis tools (EDA) was utilized to evaluate the quality of the dataset which led to removing the extreme values and outliers. A fully connected Deep Neural Network (DNN) was trained to predict NMR based effective porosity. The hyperparameters of DNN model such as number of hidden layers, number of neurons, activation functions, and learning algorithms were varied using a grid search optimization approach. The K-fold cross-validation criteria were used to enhance the generalization capabilities of ML models. The evaluation of ML models was assessed by the coefficient of determination (R2), root means square error (RMSE), and. average absolute percentage error (AAPE). The results showed that the DNN resulted in a significantly low error and high R2 between actual and predicted values. An accuracy of 87% was recorded between actual and predicted NMR values. The new model to predict the NMR porosity is trained on the NMR-determined porosity. NMR porosity is based on the number of hydrogen nuclei in the pore spaces that are independent of the rock minerals and related to the pore spaces only.
A Practical Capacitance–Resistance Model Approach to History Matching and Full Field Chemical EOR Forecasting
Authors: Al-Rawahi Hamza Salim Humaid , Groenendijk Dirk Jan , Wunnik Johannes Maria van , Mahruqi Dawood Ahmed Al , VanBatenburge Diederik , Karpan Volodimir , Al-Abri Mohammed 
Published: October 2022

AbstractCommercial application of chemical flooding requires understanding the performance of patterns already subjected to water- or chemical flooding and the capability to extrapolate this performance to new patterns. The conventional approach that relies on complex finite-difference models to obtain oil profiles is time-consuming and often unable to provide a good match on a well-by-well level. The workflow developed here instead starts from the well-by-well level, using a Capacitance Resistance Model (CRM) and oil response type curves to allocate gross rates and generate oil profiles between injector-producer pairs. These are then matched to historical performance and used for forecasting on a pattern and field scale.In the workflow, multiple patterns are analyzed simultaneously. The analysis comprises the following aspects: (1) Gross Rate Analysis: an iterative scheme is developed to calculate allocation factors between injector-producer pairs that match the measured injection and production history; (2) Swept Pore Volume Analysis: injector-producer connections are assigned pore volumes and properties such as heterogeneity and initial recovery factor at the start of the forecast period; (3) Oil Response Analysis: type curves are used to calculate oil production from each injector-producer connection to match historical oil production and predict future oil response under different development scenarios.The workflow was first verified against synthetic finite-difference models and a good match of gross rates and oil profiles was obtained. The allocation factors and pore volumes assigned to injector-producer pairs were comparable to the values obtained from streamline calculations and simulated tracer data. The workflow was subsequently applied to an actual brownfield development with 24 waterflood patterns, 46 polymer patterns and 2 alkali-surfactant-polymer (ASP) patterns. The historical performance of individual producers and patterns were well captured. Finally, the incremental recovery obtained from EOR was evaluated by comparing the performance forecast of existing EOR activity (NFA) with the waterflood baseline.The workflow produces comparable if not more accurate results than alternative methods in a fraction of the time. In addition, it provides good physical insights into the origin of the produced oil, making it especially suited for development optimization studies. The established workflow also sets the foundation for a generalized approach that can be applied for development and reservoir management of various chemical EOR applications.
Data Science for Water Cut Analysis System Utilizing ESP Sensor Data – Prototype, Pilot, and Its Potential Impact
Authors: Akbar Bibi Hussain , Sumarto Brahmantiyo Aji , Nugroho Bogi Haryo , Arief Mohammad Joenaedy , Al-Aradi Huda Jassim , Achmad Pajar Rachman , Hakim Zainal 
Published: October 2022

Abstract
Coming up with reliable water cut analysis and estimation has been challenging. An increase in water cut reduces oil production to no oil or is no longer economical. Water cut from the Portable Gas Oil Ratio (PGOR) test, while considered the most accurate is not sufficient to provide continuous water cut monitoring. Wells with Electrical Submersible Pump (ESP) have sensor reading acquired in real-time which can be utilized with supporting data to estimate water cut, fill the gap between PGOR tests and come up with continuous readings. This is conceptualized on spreadsheets and manually gathered the data from multiple sources. It may take hours to work on a single well and days for multiple wells with a complete cycle may take around 3-4 months. During the project, the prototype was built based on the solution design to provide seamless, advanced, and efficient water cut estimation through scalable data science-driven approach, data quality assessment, and improvement to ensure the data meeting the requirements, and a pilot was performed on the prototype. The prototype showed excellent results compare to the conventional approach with simply a few clicks process that yielded the results in seconds. It delivered pleasing water cut model results and disclosed major business benefits. Potential impact through transforming the prototype into full implementation includes time-saving – running time to 5400 times faster and cost saving with potential oil that can be minimized by approximately 465,000 BBLS (observed on 9 wells between Oct-2019 to Dec-2021).
FDP Simulation Studies for Green Fields Cluster Development in Less than 30 Days Utilizing Cloud Technologies
Authors: Papapetrou Georghios Dorou , Chuen Chia Pei , Mahamad Mohd Nur Adzizie , Md Rabi Ros Aliza , Seah Yong Han 
Published: October 2022

Abstract
BD Cluster green fields development located offshore Sabah, Malaysia, consists of three multi-stacked turbidite fields, namely A, B and C, encompassing thick and thin bed sands. Due to the lack of existing infrastructure in close proximity, a wellhead platform (WHP) will be installed on top of Field A. Fields B and C will be developed with a respective 8 and 7km subsea tie back to this WHP. Gas will be exported from the WHP to Facility-1 situated 5km away, whereas oil from a single thin oil rim reservoir in Field A will be exported to Facility-2 50km away.
The challenges faced by the Reservoir Engineering (RE) Team was delivering an extensive number of dynamic simulations while adhering to the Field Development Planning (FDP) submission deadline: 1) uncertainty analysis and probabilistic modelling for 9 models, 2) construction of coupled reservoir models 3) screening alternative oil and gas export routes, and 4) optimizing capex phasing by determining the optimum startup sequence of the fields. Delivering the FDP work on time with the limited software licenses and computing infrastructure available on-premise appeared to be a "bridge too far".
The limitations were addressed by PETRONAS LiveFDP digital transformation initiative commenced in 2019, through deployment of digital cloud technologies and solutions with scalable High-Performance Computing (HPC) environment. A total of 9 geological models were delivered to REs for dynamic simulation studies. Probabilistic modelling was then employed to obtain the dynamic P10, P50 and P90 models for each field. The Reservoir Coupling facility and Extended Network option were used in the numerical simulator to couple the standalone models in order to honor the overall facility constraints and incorporate the pipeline effects. Utilizing the coupled network model, multiple studies including condensate banking, determining optimum field sequencing and export route scenario were performed.
The FDP subsurface development simulation runs were completed within 1 month using HPC cloud solutions and workflows compared to 9 months if using on-premise infrastructure. It provided the necessary tools to allow the team: 1) accurately assess the impact of condensate banking on well productivity, 2) executed over 1200 cases for probabilistic modelling for the 9 models in 24 hours of simulation time, 3) reduced the number of wells derived from a previous study from 14 to 9 yielding a saving of ~US$115 million, 4) ~US$50 million savings as a result of capex phasing by optimizing the field start up sequence, and 5) US$130 million savings by establishing the lowest cost oil and gas export route scenario.
An Intelligent Approach to Predict Minimum Miscibility Pressure of Injected CO2-Oil System in Miscible Gas Flooding
Authors: Haider Ghulam , Khan Muhammad Arqam , Ali Faizan , Nadeem Ayesha , Abbasi Faiq Azhar 
Published: October 2022

Abstract
ANN Model was developed utilizing experimentally determined MMP data of 201 reservoir oil and CO2 injected gas. The data bank was randomly divided into training (70%) and testing parts (30%). The conventional statistical measures like coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the predictive efficiency of the model and correlation. Cross-plot of predicted values versus the predicted data was also made to examine the accuracy of developed model. All the important parameters that affect MMP were considered in developing ANN model. These parameters include reservoir temperature, reservoir oil compositions and properties of heptane plus and composition of N2, C1, H2S in the injected CO2 gas stream. The results showed that developed correlation and ANN model can predict the MMP value with high R2, low RMSE and low MAE. The values of R2, RMSE and MAE are 0.9469, 218.7832 and 175.8902 respectively for testing data points. The presented technique can be used to provide an estimate of the MMP in the absence of experimental data and should be utilized in the initial screening of CO2 miscible flooding process. A novel correlation using artificial neural network (ANN) to predict MMP has been developed in this study. The MMP plays an important role in designing the miscible gas flooding processes and to plan appropriate surface injection facilities. MMP is traditionally measured through experimental and non-experimental techniques. The experimental methods are expensive and time consuming and results from currently used correlations vary significantly and hence there is need of reliable, easy and fast prediction technique.