bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reservoir Management Optimization Model Employing Combination Deterministic and Probabilistic Approach for Carbon Sequestration Storage and Utilization: A Case Study from East Natuna
Authors: Cherdasa Jeres Rorym , Ariadji Tutuka , Sapiie Benyamin , Siagian Ucok W. R. 
Published: December 2021

Abstract
East Natuna is well known for its huge natural gas reserves with a very high CO2 content. The appearance of CO2 content in an oil and gas field is always considered as waste material and will severely affect the economic value of the field. The higher the content, the more costly the process, both technically and environmentally. In this research, the newly proposed reservoir management approach called CSSU (Carbon Sequestration Storage and Utilization) method is trying to change the paradigm of CO2 from waste material into economic materials.
The novelty of this research is the combined optimization of deterministic and stochastic methods with the Particle Swarm Optimization (PSO) algorithm to answer complex and non-linear problems in the CSSU (Carbon Sequestration Storage and Utilization) method. The CSSU method is an integration of geological, geophysical, reservoir engineering and engineering economics with the determination of technical and economic optimization of the use of CO2 produced as working fluid in a power generation system that has been conditioned through an injection-production system in geological formations.
The CSSU research area is located in a sedimentary basin that has a giant gas field with 70% CO2 content. The Volumetric Storage Capacity for CO2 injection process in research area is 1,749.14 BCF or 94.01 MMTon which being calculated based on static modeling considering geological, geophysical and petrophysical aspects.
A combination of Compositional, Geomechanics and Thermal reservoir simulation model had been conducted to determines the Storage Injection capacity and later to prove the CSSU method in which CO2 fluids will be utilized as working fluid, 1 case was built using 2 Injection Wells and 1 CO2 fluid Production Well. The simulation results show with 1 production well the total of CO2 fluid injected from 2 Injection wells can almost double the injection total capacity up to 1,150 BCF. The utilization of supercritical CO2 fluid as working fluid can produce 55 – 133.5 MMBTU/Day or 0.67 - 1.63 MW from 1 production well for 25 years timeframe.
The CSSU method is optimized by deterministic and stochastic methods using the Particle Swarm Optimization (PSO) algorithm by looking the technical and economical aspects. The technical optimization aspect is being analyzed by electricity production versus well counts. The economical optimization is being analyzed by operational expenditure saving versus well counts and electricity produced versus NPV 10%. From both aspects the 4 injector wells case and NPV 200.00 MM US$ gives the most optimum result within technically and economically. The CSSU economic model proved with CSSU scheme the economical value is being increased by 57 MMUS$ after operating cost efficiency due to the electricity savings, 92 MMUS$ due to Carbon Trading which resulting the NPV 10% is 172.77 MMUS$.
Fractures and Flow Patterns Detection in Carbonate Reservoirs Using Intelligent Sensor Selection in a Deep Learning and Uncertainty Framework
Authors: Katterbauer Klemens , Marsala Alberto , Al Shehri Abdallah , Yousif Ali 
Published: December 2021

Abstract
4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.
Novel Eco-Friendly Kinetic Hydrate Inhibitors
Authors: Xu Kui , Stewart-Ayala Jonathan , Jackson Steve , Hutchinson Benton , Sanders Christina , Jakubowski Wojciech , Jardine Joanne , Lehman Rose 
Published: December 2021

Abstract
Amid concerns over negative the environmental impacts of offshore chemicals, Baker Hughes explored new chemistries to develop environmentally friendly kinetic hydrate inhibitors (KHI). Our efforts were focused on improving biodegradability and toxicity of KHIs to meet environmental protection requirements, as well as mitigating challenges in field applications. A novel KHI design with branched polymers containing sugar alcohol ester groups as linkages, was proposed and synthesized. The new KHI polymer demonstrated &gt; 20% biodegradability and &gt;100 mg/L toxicity to seawater algae, and it also exhibited competitive or even better KHI performance to traditional non-biodegradable KHI products. Additionally, new KHI showed improved stability in water/brine at elevated temperatures as compared to traditional KHI products, which might mitigate concerns on polymer deposition at high temperatures.
Characterization of Tight Carbonate Reservoir by Using Nuclear Magnetic Resonance Log Analysis in the Bahrain Field
Authors: Al Saffar Rabab , Dowen Michael 
Published: December 2021

Abstract
The Bahrain Field (the "Field"), discovered in 1932, is an asymmetric anticline trending in a North-South direction of the Kingdom of Bahrain. It is a geologically complex field with 16 multi-stack carbonate and sandstone reservoirs, most of them oil bearing. The fluids varying from shallow tarry oil in Aruma to dry gas in the Khuff and pre-Khuff reservoirs. The Field has more than 2000 wells of which 90% have good quality log data.
The Ostracod and Magwa reservoirs are heterogeneous, layered tight reservoirs and have been on production since 1964. The Ostracod reservoir consists of very heterogeneous with limestone intervals intercalated between shale layers, with a total thickness of around 200 ft. The Magwa reservoir conformably underlies the Ostracod reservoir. The Ostracod averages 120 ft in thickness and is dominated by limestone with high porosity, low permeability, and variable water saturations. Core derived permeability measurements are usually less than 5 mD and porosities average 22%.
Production performance of individual wells is extremely variable and in many cases appears to be at odds with log-calculated saturations. Wells having good oil saturation often produce water and wells with low oil saturation produce high volumes of oil. Several studies have been conducted in an attempt to understand and resolve this.
The variability of oil saturation which has been mapped both laterally across the Field and vertically within wells, led to the question of what caused the variation in oil saturation. The variation is not a function of depth, which one might expect. Causes might include oil failure to migrate into certain reservoir compartments, a loss of the original charge to shallower reservoir or the oil charge been restricted by rock quality.
This paper attempts to address the variability in saturations seen across the Field and link known productivity to the Petrophysical interpretations. Nuclear Magnetic Resonance (NMR) logs had been employed in a targeted area of the Field in order to investigate rock quality in an attempt to explain the oil saturation distribution. A small NMR core study was undertaken in order to calibrate the NMR log response. The NMR data had been initially processed with what was considered a representative cut-off for Middle East Carbaonte rocks. This core study resulted in a surprisingly low series of T2 cut-off. The NMR logs were reprocessed with the more representative T2 cut-off. The resulting bound and free fluid fractions seemed to explain the observed well production.
A New Model for Predicting Liquid Loading in Shale Gas Horizontal Wells
Authors: Zhou Chao , He Zuqing , Chen Yashu , Wang Zhifa , Mulunjkar Amol , Zhao Weishu 
Published: December 2021

Abstract
Current critical flow rate models fail to accurately predict the liquid loading statuses of shale gas horizontal wells. Therefore, a new critical flow rate model for the whole wellbore of shale gas horizontal wells is established. The results of the new model are compared to those of current models through the field case analysis. The new model is based on the dynamic analysis and energy analysis of the deformed liquid-droplet, which takes into account the liquid flow rate, the liquid-droplet deformation and the energy loss caused by the change of buildup rate. The major axis of the maximum stable deformed liquid-droplet is determined based on the energy balance relation. Meanwhile, the suitable drag coefficient equation and surface tension equation applied to shale gas horizontal wells are chosen. Finally, the critical flow rate equation is established and the maximum critical flow rate of the whole wellbore is chosen as the criterion for liquid loading prediction. The precision of liquid loading prediction of the new model is compared to those of the four current models, including Belfroid's model, modified Li's model, liquid film model and modified Wang's model. Field parameters of 29 shale gas horizontal wells are used for the comparison, including parameters of 18 unloaded wells, 2 near loaded-up wells and 9 loaded-up wells. Field case analysis shows that the total precision of liquid loading prediction of the new model is 93.1%, which is higher compared to those of the current four models. The new model can accurately predict the liquid loading statuses of loaded-up wells and near loaded-up wells, while the prediction precision for unloaded wells is high enough for the field application, which is 88.9%. The new model can be used to effectively estimate the field liquid loading statuses of shale gas horizontal wells and choose drainage gas recovery technologies, which considers both the complex wellbore structure and the variation of flowback liquid flow rate in shale gas horizontal wells. The results of the new model fill the gap in existing studies and have a guiding significance for liquid loading prediction in shale gas horizontal wells.
Sensor Ball: Modernized Logging
Authors: Buzi Erjola , Seren Huseyin , Hillman Thomas , Thiel Timothy , Deffenbaugh Max , Bukhamseen Ahmed , Zeghlache Mohamed Larbi 
Published: December 2021

Abstract
The latest development in the electronics and manufacturing industry has enabled work towards the modernization of oil-field instruments. As a part of this trend, it is the time to invent and design small size oil-field instruments that could be much more practical to handle, easy to use, and less costly. High temperatures and pressures of the downhole environment make it very challenging to design and further develop such downhole instruments. To create such apparatuses, a thorough study of downhole conditions needs to be done upfront. This study will further help to define the design specifications and requirements. By targeting liquid wells in Saudi Arabia, we have overcome the challenges posed by the harsh downhole environment and managed to design and manufacture a hand-held device called ‘Sensor Ball’ and tested it in the field.
Satellite Fields Digitalization & ALS Optimization with EDGE & Advance Analytics Application
Authors: Johri Nitin , Pandey Nimish , Kadam Sanket , Vermani Sanjeev , Agarwal Shubham , Gupta Debashish 
Published: December 2021

Abstract
Data monitoring in remote satellite field without any DOF platform is a challenging task but critical for ALS monitoring and optimization. In SRP wells the VFD data collection is important for analysis of downhole pump behavior and system health. SRP maintenance crew collects data from VFDs daily, but it is time consuming and can target only few wells in a day. The steps from requirement of dyna to final decision taken for ALS optimization are mobilizing team, permits approvals, download data, e-mail dynacards, dyna visualization, final decision.
The problems with above process were: -
Insufficient and discrete data for any post-failure analysis or ALS-optimization Minimal data to investigate the pre failure events
The lack of real time monitoring was resulting in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning was implemented to shift from the reactive to proactive approach which helped in ALS Optimization and reduced production loss.
The data was transmitted to a Cloud server and further it was transmitted to web-based app. Since thousands of Dynacards are generated in a day, hence it requires automated classification using computer driven pattern recognition techniques. The real time data is used for analysis involving basic statistic and Machine learning algorithms. The critical pump signatures were identified using machine learning libraries and email is generated for immediate action. Several informative dashboards were developed which provide quick analysis of ALS performance. The types of dashboard are as below
Well Operational Status Dynacards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics
After collection of enough data and creation of analytical dashboards on the three wells using domain knowledge the gained insights were used for ALS optimization. To keep the model in an evergreen high-confidence prediction state, inputs from domain experts are often required. After regular fine-tuning the prediction accuracy of the ML model increased to 80-85 %. In addition, system was made flexible so that a new algorithm can be deployed when required. Smart Alarms were generated involving statistic and Machine Learning by the system which gives alerts by e-mail if an abnormal behavior or erratic dynacards were identified. This helped in reduction of well downtime in some events which were treated instinctively before.
The integration of domain knowledge and digitalization enables an engineer to take informed and effective decisions. The techniques discussed above can be implemented in marginal fields where DOF implementation is logistically and economically challenged. EDGE along with advanced analytics will gain more technological advances and can be used in other potential domains as well in near future.
Annual Work Plan Execution Performance Using an Automated Production Forecast Tool
Authors: Nuñez Gustavo , Tellez Camilo , Florez Fabian , Gallegos Johanna , Eremiev Francisco , Triviño Diego , Vallejo Renato 
Published: December 2021

Abstract
Shaya Consortium ramped up its production from 60 KBOPD to almost 85 KBOPD as a result of an agile execution of its Field Development Plan, made of infill drilling, workover interventions, and full-field expansion of waterflooding. This combined activity made the planning process very complex and dynamic due to the high volume of operations and scenario evaluation. Additionally, the consortium was requested to provide a weekly production forecast to its major stakeholders highlighting all deviations from the original execution plan and remedial activities to come back on track. The proposed application tool has simplified and automated the forecasting processes using short-term updates of the executed activities from field reports, current well status, planned workover interventions, and new wells drilling schedule. Any deviation of the Annual Work Plan due to schedule variance or well performance is automatically adjusted by the tool, creating a new forecast to End-Of-Year or Quarter even Weekly, thus, reflecting the impact on the estimated recoverable volumes.
The tool pulls information from different sources and consolidates them in a single unified environment, not only for forecasting but also as a visualization and analysis tool. Furthermore, it has several modules to facilitate the control of official type curves, scenario profiles for the Annual Work Plan, and it is fully linked to key corporate applications.
This paper presents the development of a production forecasting tool that introduced a new way of working within the Shaya Production Team by improving activity scheduling and overcome underperforming new wells, keeping the operations team informed to facilitate the production management.
Large Scale Placement For Multilateral Wells Using Network Optimization
Authors: AL-Qahtani Ghazi D. , Berlow Noah 
Published: December 2021

Abstract
Multilateral wells are an evolution of horizontal wells in which several wellbore branches radiate from the main borehole. In the last two decades, multilateral wells have been increasingly utilized in producing hydrocarbon reservoirs. The main advantage of using such technology against conventional and single-bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact with fewer resources. Today, multilateral wells are rapidly becoming more complex in both designs and architecture (i.e., extended reach wells, maximum reservoir contact, and extreme reservoir contact wells). Certain multilateral design templates prevail in the industry, such as fork and fishbone types, which tend to be populated throughout the reservoir of interest with no significant changes to the original architecture and, therefore, may not fully realize the reservoir's potential. Placement of optimal multilateral wells is a multivariable problem, which is a function of determining the best well locations and trajectories in a hydrocarbon reservoir with the ultimate objectives of maximizing productivity and recovery. The placement of the multilateral wells can be subject to many constraints such as the number of wells required, maximum length limits, and overall economics.
This paper introduces a novel technology for placement of multilateral wells in hydrocarbon reservoirs utilizing a transshipment network optimization approach. This method generates scenarios of multiple wells with different designs honoring the most favorable completion points in a reservoir. In addition, the algorithm was developed to find the most favorable locations and trajectories for the multilateral wells in both local and global terms. A partitioning algorithm is uniquely utilized to reduce the computational cost of the process. The proposed method will not only create different multilateral designs; it will justify the trajectories of every borehole section generated.
The innovative method is capable of constructing hundreds of multilateral wells with design variations in large-scale reservoirs. As the complexity of the reservoirs (e.g., active forces that influence fluid mobility) and heterogeneity dictate variability in performance at different area of the reservoir, multilateral wells should be constructed to capture the most productive zones. The new method also allows different levels of branching for the laterals (i.e., laterals can emanate from the motherbore, from other laterals or from subsequent branches). These features set the stage for a new generation of multilateral wells to achieve the most effective reservoir contact.
Field-Scale Modeling of Smart Completion Tools for Optimum Recovery
Authors: Chen Zhen , Shaalan Tareq , Qahtani Ghazi , Manzoor Shahid 
Published: December 2021

Abstract
Flow control devices (FCDs) like inflow control devices (ICDs) and interval control valves (ICVs) (i.e., equalizer) have increased applications in both conventional and unconventional resources. They have been used to mitigate water or gas coning problems for mature fields in conventional reservoirs, to alleviate premature water breakthrough in naturally fractured reservoirs, and to optimize the steam distribution in heavy oil reservoirs. There have been increased trend in using FCDs in the real field. Previously, complex well models have been implemented in a large-scale parallel reservoir simulator by Tareq et al. (2017). The implementation can simulate an intelligent field contains tens to hundreds of multilateral complex wells commonly referred in the literature as maximum reservoir contact (MRC) wells with mechanical components such as ICVs and ICDs.
In this paper, a new framework to model controlling the FCDs in complex well applications will be presented. The implementation is integrated into a complex well model. It can be easily used to model the dynamical control of devices. Simulation studies using both sector model and field model have been conducted. A systematic full-field operation is used for device control applications of smart wells. Successful application of field level controls in smart wells has the benefit of the improved overall GOSP performance.
Troubleshooting Gas Compression Systems Using Data Analysis
Authors: Al-Aiderous Abdullah 
Published: December 2021

Abstract
The objective of this paper is to showcase the successful and innovative troubleshooting data analysis techniques in one of the gas compression systems in upstream gas oil separation plants (GOSP-A). The gas compression system using gas compressors, dry gas seal systems and due point controls is used in almost all of upstream operation.
These proven data analysis techniques were used to tackle major and chronic issues associated with gas compression system operation that lead to excessive flaring, mechanical seal failures, solidification, hydrate formation and off-specification products. Dry Gas mechanical seals are an important key element in gas compression and its lifetime represents a concern to the operation personnel. Most gas compression systems have a mechanical seal lifetime of 2 years which in some cases limit production, increase the potential of unnecessary flaring and increase OPEX significantly. In addition, solidification due to constant liquid carry over result in a wide range of undesirable results such as blockages that constrain production rates and result in safety concerns.
In this paper, comprehensive data analysis of the potential root causes that aggravate undesired premature mechanical seal failure, material solidification, equipment damage and off-specification gas products will be discussed along with solutions to minimize expected impact. For example, improper product specification in some applications have been found to promote seal failures, corrosion, solidification and incur additional flaring which is both costly and environmentally undesirable. In addition, after extensive analysis improper operation practices during compressor startups, steady state operation and gas conditioning have been linked with premature compressor failures, product off spec and safety device failures.
The field trial proved the effectiveness of the proposed innovative troubleshooting data analysis techniques in reinstating the gas compression unit in GOSP-A to its recommended design conditions, eliminated compressors and mechanical seal failures and avoided the off-specification products at the lowest operating cost. This innovative technique was based on deep and extensive process data analysis, evaluating operating and design data, reviewing international standards, benchmarking against other facilities, process simulation using Hysys, and finally the actual field trial.