bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Application of Co2 Foam Fracturing Enables Paradigm Shift in Stimulation Strategy of North Kuwait Jurassic Depleted Reservoirs
Authors: Abdel Mohamed , Al-Mefleh SLB Kholoud , Al-Muhanna Danah , Fidan Erkan , Abdul-Samad Zamzam , Al-Salali Yousef , Ajayi Ayo 
Published: February 2024

Abstract
The first ever CO2 foam fracturing new technology in Kuwait Oil Company (KOC) history was executed flawlessly in late 2021. Three treatments were executed. Co2 Foam Fracturing proved its significant added value of improving productivity in deep depleted tight carbonate Jurassic reservoirs, enhance flow back, reduce water consumption and carbon emission, and enable early production plus improving operation efficiency and cost saving. The stimulation operation has proven to be a huge success for all multidisciplinary teams involved as preliminary results showed over 50-70% production increase compared to offset wells.
The main challenges of acid fracturing stimulation in depleted reservoirs are the need for extended formation cleanup to flow back the injected fluids via prolonging Nitrogen lift that add higher operational costs and intervention operations. Therefore, energetic high foam efficiency frac fluid becomes essential to assist flowback and retrieve pumped frac fluids from reservoir. To tackle these challenges, Carbon Dioxide CO2 is pumped in liquid phase as energetic fluid together with normal frac fluids. Due to CO2 liquid nature, high foam efficiency can be reached (40 – 50%) at much lower friction losses. So, it enables achieving pumping frac at high rates and high foam efficiency. The main benefits of CO2 Foam frac are better fracture cleanup due to expansion of the stored compressed gas in the liquid CO2, fluid loss control that is provided by foam, minimized fracture conductivity damage, and the increase in hydrostatic pressure while pumping that translates to lower surface pressures during injection.
The selected pilot well is in depleted deep tight carbonate reservoir area of North Kuwait Jurassic gas fields. The executed acid fracturing operation required close planning starting from Q1-2021. Many challenges faced from logistical issues, lack of infrastructure and CO2 resources for the multi-faceted operation due to COVID-19 pandemic limitations. These challenges were tackled ahead with the integration of technical and operations teams to bridge the knowledge gap and to enable executing the operation safely.
The pilot well’s net incremental production gain is estimated at 50-70% compared to offset wells, with improved flowback and formation cleanup with less well intervention. The resulting time and cost savings as well as the incremental well productivity and better operation efficiency confirmed high perspectives for the implemented foam acid fracturing approach. Another two CO2 Foam acid fracturing wells were executed with good results too.
This paper will demonstrate the value of CO2 foam fracturing in depleted reservoir and KOC experience post first application and its plans to expand CO2 Foam Fracturing application across KOC different fields.
Automated Wellhead Monitoring Using Deep Learning from Multimodal Imaging
Authors: Li Weichang , Ma Yong , Alerigi Damian San Roman 
Published: February 2024

Abstract
Wellhead growth caused by temperature and pressure effects during production can lead to severe consequences, causing well integrity failure and surface equipment damage, sometimes with catastrophic incidents at huge safety risks and economic losses. In addition, it may lead to unintended emission when pipe connections are damaged. This work develops multimodal imaging and computer vision- based methods for automated wellhead equipment health monitoring, notably wellhead displacement or growth detection and quantification. Wellhead equipment is imaged at the well site using optical and/or hyperspectral cameras if available. The captured wellhead imagery or video is then fed into a computer vision system for analysis to determine the wellhead health condition such as the amount of displacement or growth, using machine learning techniques. First a set of sample wellhead images are labeled with wellhead segmentation annotation or bounding boxes. The set of sample data are then grouped randomly into training/validation/testing subsets, according to certain partition ratio. We then construct semantic segmentation and object detection models; train these models on the training and validation subsets and then apply to testing data set for performance assessment. These trained models can then be applied to new wellsite imagery from permanent monitoring to extract wellhead equipment. The extracted wellhead equipment image is compared with the baseline wellhead image and dimension for growth detection and quantification. This removes interferences from background objects, ambient lighting variations and other non-equipment related conditions. We collected over 4000 sample well-site images that contain well-head equipment, among which we have labeled a subset of 1200 samples which are randomly partitioned into 900 training samples, 150 validation and 150 testing samples. After training and validating the Mask R-CNN model on the training and validation samples, respectively, the model is then applied on the testing samples. The training and validation performance in terms of Intersection over Union (IOU) reach 89% and 78%, respectively, and the test performance achieves 75% IOU. The segmented well equipment image is then compared with the baseline. After 2D cross-registering, we have achieved highly accurate prediction of displacement. This computer vision and image driven based approach for wellhead displacement prediction has great advantage over traditional thermos-stress model-based approaches in that it can detect displacement in real-time with high accuracy.
Field Development Plan (FDP) Cloud-based Data Integration Between Geology & Drilling Software Using Python Tools
Authors: Mwansa P. , Repilloza H. , Grebe S. , Hassan M. A. , Pietrzyk M. , Torsæter A. 
Published: February 2024

Abstract
The oil and gas industry generates vast amounts of data throughout its operations, from exploration to production. Collecting, connecting, and optimally utilizing this data is key to maximizing efficiency, accuracy, and access to new disruptive technologies.
In a typical well-planning cycle, an engineer will spend a significant amount of time looking for the data they require to do their jobs efficiently. The data are typically locked away in silos - trajectories in one data platform, Pore Pressure Gradient, Fracture Gradient or Targets in another, and so on.
A major Middle Eastern NOC and Two Norwegian software service companies teamed up to develop Proof of Concept (PoC) for a new workflow that integrates subsurface and drilling data between on-premises Geology E&P software and Drilling software through a proprietary Python Tool plug-in and Python library. This integration enables a streamlined connection to a cloud-based drilling and well planning software, facilitating collaboration among teams involved in well planning. The project's key challenges are the lack of a standardized communication, integration, and automation of data flows between subsurface and drilling teams, as well as the inability of engineers to access necessary data due to scattered information and access restrictions. The project utilizes a proprietary data science suite, named Cegal's Prizm, which allows easy configuration to integrate data from various applications, sources, and platforms. A proprietary Python Tool is used to merge data from various application silos and data sources, enabling enriched investigation. The process involves connecting to the Geology E&P software retrieving domain objects using the proprietary Python Tool, and converting these domain data objects into common Python data structures. The project aims to develop an innovative workflow that provides easier access to data for experts throughout the organization, leading to better decision-making during the well-planning cycle. This not only makes it easier, but it also ensures collaboration between the G&G and Drilling teams involved in new well planning
Development of a Hybrid Modelling Approach for Estimating Bottom Hole Pressure in Shale and Tight Sand Wells Using Smart Production Data and Machine Learning Techniques
Authors: Afagwu C. C. , Glatz G. 
Published: February 2024

Abstract
Well flowing bottom hole pressure is an important input parameter for well and reservoir performance evaluation. However, there are limitations in accurate downhole measurements due to a faulty gauge and, sometimes, the inability to obtain complete well test data to implement theoretical models. The goal of this work is to create a hybrid modeling approach for estimating bottom hole pressure in tight sand and shale wells using smart production data, engineering features and machine learning (ML) techniques. The robust feature selection process integrated the critical attributes in the traditional PTA methods. Four out of eight adopted variables were split into training, cross validation and test sets in 60:20:20 proportion and preprocessed by z-score normalization scaling. For the first time, the combination of gradient descent optimization (GDO) algorithm and Cauchy cost function was used in the estimate of bottom hole pressure from production data in this study. A smart data approach for developing ML models was used in this study. Starting with 100 data points, the training and validation input size were incremented and subject to the learning process using both Cauchy and traditional MSE cost function-based GDO algorithm to determine the optimum batch size required to train the BHP model with lowest cost. The results showed that the Cauchy based GDO algorithm provides slightly better performance in training, cross validation and testing data with a global minimum error of 0.82%, 0.64% and 0.41% respectively compared to the traditional MSE-based GD that reaches a global minimum with higher cost. However, the improved metrics scores with Cauchy loss optimization required additional expense of one to ten seconds execution time. The Cauchy cost function presents an alternative technique to obtain better optimized prediction models from production data and can be utilized in sensitivity studies for reservoir characterization and asset management purposes.
Exploration and Application of Integrated Approach for Sand Control and Remediation
Authors: Chen Huaxing , Zou Jian , Wang Yufei , Pang Ming , Liu Yigang 
Published: February 2024

Abstract
The objective of this paper is to explore and apply an integrated operation mode for sand control and sandstone acidization in offshore weakly cemented and loose sandstone reservoirs. The paper aims to investigate the use of a water-based resin for sand consolidation and chemical sand control in order to extend the effective period of sand control operations and mitigate sand production and particle migration. This paper is based on the analysis of reservoir particle transport mechanisms. It introduces the application of a water-based resin in sand control operations, forming an integrated approach combining acid stimulation and chemical sand control. The first step involves acid stimulation to restore high-permeability channels near the wellbore, followed by chemical sand control treatment to inhibit the migration of reservoir sand particles and prevent the blockage of near-wellbore flow channels, thereby extending the effectiveness of acid stimulation. Experimental results demonstrate that the resin can achieve a core permeability retention rate of 84.6%~91.4% and a compressive strength of 3.44~6.95 MPa. The integrated operation mode of acidization and chemical sand control is compared to the single acidization method, showing an 80% reduction in core plugging rate and a fourfold extension of the effective period of sand control. The application of the integrated operation mode of acidization and chemical sand control in a well affected by near-wellbore plugging (referred to as Well S) in offshore fields has resulted in a significant increase in oil production, absence of sand at the wellhead, and sustained stable production. The successful application of the integrated operation mode in offshore fields is of great significance for sand production and particle migration control in such wells. This paper presents a novel approach by integrating sandstone acidization and chemical sand control, which extends the effective period of acidization and provides a solution to sand control challenges in offshore sand and particle migration wells. The use of a water-based resin for sand consolidation and the implementation of a chemical sand control system contribute to the existing literature by offering an effective method to mitigate sand production and enhance reservoir productivity.
Engineered Low Salinity Waterflood in Carbonate Reservoirs?Boosting Fluid-fluid Interaction and Oil Recovery by Cost Effective Additives
Authors: Yonebayashi Hideharu , Hiraiwa Takeshi , Tange Masaaki , Sawata Masaru , Tareq Khlaifi Khuloud , Sakurai Risa , Bahri Sami , Abdelfattah Abed Abdalla , Inokuma Yuto , Hamdan Alkaabi Sultan , Fattahi Mehraban Mohammad , Stalker Robert , Graham Gordon 
Published: February 2024

Abstract
Low salinity water (LSW) enhanced oil recovery (EOR) has gained more attention in carbonate reservoirs with variety of mechanism hypothesis. Recent research focused on fluid-fluid interaction, forming micro-dispersion as an interfacial reaction of oil-water mixing, can reflect a main driver of oil improving mechanism. This paper focuses on the mechanism and demonstrates further boosting the fluid-fluid interactions using small amount of additives to improve LSW EOR value in an objective offshore giant carbonate field. Four stock tank oil (STO) samples were collected from multiple sub-layers (L1, L2, L3, and U). Brines were prepared as formation water (FW), sea water (SW), and LSW (diluted to 1-20%SW). The fluid-fluid interaction tests measured micro-dispersion ratios (MDR), which represent water content increment by the oil-water interfacial reactions, to screen the most positive oil for low-salinity effect. To boost the reaction in LSW, three additives: diethyl ketone (DEK), dimethyl ketone (DMK), and copper chloride were independently evaluated using the screened positive oil. The boosting effectiveness was investigated by MDR improvement and core flood tests.
The larger micro-dispersion phase was formed as switching from FW and SW to more diluted LSW in the fluid-fluid interaction tests. The STO-L2 was screened as the most positive oil because of the highest MDR. The two-staged MDR increase indicated the existence of threshold salinity (i.e., between 4-5%SW and 2-3%SW) for triggering the radical oil-water interfacial reaction. To clearly capture booster's impacts, the subsequent tests used 1%SW as a base LSW. The compatibility analysis determined 2.0 wt% of DEK and DMK as dosing ratio to LSW. The additive boosting impacts on fluid-fluid interaction revealed a noticeable MDR increase compared to that of pure LSW case. The boosting potential of oil recovery was evaluated by three core flood tests using the identical reservoir core (i.e., reused after solvent cleaning and aged again for avoiding a common core-nature uncertainties). Thus, common secondary SW injection achieved similar oil recovery among three tests. The tertiary untreated LSW injection revealed +3%IOIP additional oil recovery after the secondary SW injection. The largest oil recovery boosting occurred by the tertiary LSW+DEK injection as +10.7 %IOIP (three-times higher than that of pure LSW case). Finally, we found a linear correlation between MDR and oil recovery increment.
A Skin Factor Model of Sliding Sleeve and its Production Optimization
Authors: Binwang LI , Zhiming WANG , Quanshu ZENG 
Published: February 2024

Abstract
In order to effectively alleviate the problem of premature water breakthrough in horizontal wells, sectionalized variable-density perforation completion, stinger completion, ICDs and sliding sleeve based on control valve completion technology have been proposed. The sliding sleeve based on control valve completion technology can realize infinite adjustment and real-time control of the sliding sleeve opening to balance inflow effectively.
In order to study the interference of the sliding sleeve completion to the flow near the wellbore, a calculation model for the skin factor of sliding sleeve is established in this paper. Based on numerical simulation, the flow area is divided according to the pressure distribution characteristics during the flow process of the sliding sleeve completion. At the same time, the linear flow boundary and the radial flow boundary are determined by combining the geometric boundary and roundness. Finally, the skin factor is determined according to the flow difference between the fluid passing through the sliding sleeve completion and the open hole completion, and it is compared and verified with the theoretical prediction value.
The research shows that the flow in the near-wellbore of the sliding sleeve can be divided into six different zones according to the distribution characteristics of the pressure equipotential line. The radius of disturbance in the near-wellbore predicted by roundness is within 6% error of the numerical simulation results. By simulating the flow difference during constant pressure production, the error between the skin coefficient obtained by inverse calculation and the theoretical prediction value is within 12%. It was also observed that the water-cut decreased by 6.4% with the application of sliding sleeve completion compared to the normal screen pipe completion after 2 years of production.
The proposed model is robust and reliable. The skin factor model in this study can be applied not just to the optimal design of water control completion parameters, but to provide theoretical guidance for the real-time regulation of the sliding sleeve opening during production.
Extending Pulsed Neutron Logging Application in Reducing uncertainty in Low-Porosity, Complex Lithology Exploratory Field – A Detailed Case Study
Authors: Kozlowski M. , Eyuboglu S. , Adedamola A. , Aydemir P. , Armutlu T. 
Published: February 2024

AbstractThe assessment of oil and gas saturation behind casing by using Pulsed Neutron Logging technology (PNL) is not new to the industry. However, the assessment of low-porosity reservoirs has always presented challenges to the application of PNL. This is further complicated by the presence complex unknown lithology with rugose borehole architecture. In this paper, a detailed case study is discussed highlighting the job planning, logging execution, and integrated post-processing interpretation workflow with the obtained results.In the subject well, limited open hole data was available and the rugosity of the wellbore posed some challenge to the conventional porosity measurements. After the review of the available data, a multidetector pulsed neutron tool (MDPNT) was deployed. Capture data, inelastic data as well as spectral gamma ray data were acquired. The statistical uncertainty of the acquired data was assessed real time to determine the number of inelastic passes required with the associated logging speed. Special hole size correction models were applied to correct for the impact of rugosity in the tool measurement.Detailed mineralogical evaluation was done by comparing the capture elemental yields against the inelastic yields to ascertain the appropriate mineral model. Spectral gamma ray data was used for clay typing and also to confirm any possible presence of organic carbon/kerogen. Oil saturation was computed from carbon-oxygen measurement with detailed borehole corrections applied to correct for the effect of hole rugosity. Porosity data was computed from the pulsed neutron porosity indicators – the inelastic and capture ratios. Porosity was predominantly below 5% with mixed carbonate observed across the oil zone. Clay typing showed varied mix of various clay types with prevalent presence of chlorite and montmorillonite. Despite the very low porosity and low salinity, accurate hydrocarbon saturation was computed which was used perforation planning.The value of pulsed neutron logging in newly drilled exploratory well was demonstrated in this case study. The importance of proper job planning, detailed environmental corrections and real-time assessment of statistical and saturation uncertainty was showcased, in order to achieve results with high confidence despite the low-porosity challenge.
Practical Implementation of Combining Wellbore Survey Tools for Optimized Wellbore Position Accuracy
Authors: Houbiers M. , ElGizawy M. , Ledroz A. , Bang J. , Lowdon R. , Aklestad D. 
Published: February 2024

Abstract
Techniques involved in combining Measurement While Drilling (MWD) and gyro survey tools have been developed previously, and it has been demonstrated that the advantages for wellbore position uncertainty can be significant. However, the implementation of these techniques on a practical level is not well understood. In this paper, we outline the practical implementation of combining MWD and gyro position surveys to take advantage of the improved wellbore survey accuracy both during planning and execution of drilling operations and afterwards.
Worldwide, multiple wellbore survey tools are run to ensure a well is drilled safely and in the correct place. The combination of survey tools normally involves an MWD and a gyroscopic tool, often run where there is magnetic interference from an offset well, for additional accuracy, or survey QA/QC requirements. Both survey tools are valid, assuming they pass their respective QA/QC parameters. For sections where the surveys overlap, a linear combination may be used to build the definitive survey of the well. This yields a new wellbore position with potentially significantly increased accuracy.
To assess the improvement in combining wellbore survey tools, an error model needs to be built describing the combined error terms, weighting functions, and their application. The ability to build region-specific error models for a given combination of survey tools will be discussed. Requirements for the overlapping survey data and practical steps to ensure safe and robust implementation of the combined survey position are presented. Moreover, the application of a combined survey delivered to the rig in real time (if this is necessary) will be investigated. Finally, operational workflows, survey data, and error model handling, QA/QC of the new combined survey, and limitations of the method will be discussed.
An adequate practical implementation will allow combined surveys to be modelled and applied to planned and existing wells, resulting in improved well placement accuracy and wellbore spacing, more safe space for a well to be drilled, and hence drive drilling efficiencies and maximum production.