bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid Drilling Dynamics Downhole Measurements
Authors: Boualleg Riadh , Li Denis , Johnson Ashley , Mishra Amitabh , Aguiar Romulo , Cherel Audrey , Dow Blaine , Gjertsen Ole Jacob 
Published: November 2024

Abstract
In the continuous pursuit of enhancing the efficiency and safety of drilling operations, the optimized placement of downhole sensors has emerged as a pivotal area of innovation. This paper presents an optimized configuration for sensor placement, meticulously designed to advance the understanding and control of drilling dynamics. By integrating high-resolution sensors capable of measuring three-axis acceleration, revolutions per minute, weight on bit (WOB), and torque, an optimized approach to monitor and analyze a drilling system's performance can be established.
The methodology emphasizes steering efficiency, system integrity, and scenario-specific relevance, catering to a wide range of operational challenges. Several key scenarios are addressed, including high-frequency torsional oscillations (HFTO) detection and mitigation, backward whirl detection indicative of motor failure, motor micro stall conditions, reamer deployment strategies, comprehensive performance optimization, and stick/slip detection for preventing twist-offs. Based on this, the sensors are positioned at the most sensitive spots within the bottom hole assembly (BHA), ensuring a precise characterization of the excitation and distribution of critical stresses.
Combined analysis of field data and 4D (3D in time domain) drilling dynamic simulations (Chen et al, [2015]), has affirmed the efficacy of the recommended sensor placement strategy in improving drilling efficiency and reducing operational hazards. The simulations particularly highlighted the capability to infer measurements from sensor data across different BHA components. It was also determined that WOB and torque on bit measurements from elsewhere in the BHA could be confidently inferred at the drill bit through extrapolation.
Field data analysis further revealed that capturing burst acceleration data at the drill bit, integrated with a digital solution, effectively identifies HFTOs, facilitating their mitigation. Additionally, positioning a torque sensor just above the steering unit, where significant bending occurs, can prove instrumental in predicting HFTO-related fatigue life with considerable precision.
Leveraging field data alongside advanced 3D dynamic simulations has led to the identification of an optimized sensor positioning. This versatility across numerous drilling scenarios, ensures the integrity of the drill bit while seamlessly integrating with the steering mechanism.
Novel Multiscale Full Field Simulation Applied to the Rapid Calibration of Geologic Models
Authors: Alghawi Yaser M. , Ahmadov Turgut V. , King Michael J. 
Published: November 2024

Abstract
High resolution simulations of geologic models provide precise representations of heterogeneity, flood fronts and pressure response, but at a significant computational cost, while coarse "network" models provide improved computation efficiency and can capture the pressure and rate connectivity between injection and production wells. We introduce a novel workflow for combining these two scales of simulation and demonstrate its ability to capture subsurface uncertainty applied to the dynamic calibration of the Brugge reservoir model realizations. A novel coarse partition of the reservoir volumes is presented based upon distance to the nearest well in terms of Diffusive Time of Flight (DTOF), which is obtained from the pressure diffusivity Eikonal equation. Connectivity within the reservoir is evaluated using novel pressure transient flow-based upscaling of transmissibility. The workflow is applied to selected realizations of the static Brugge model to quantify dynamic uncertainty and for calibration. The combination of coarse grid design and flow-based upscaling preserves many of the important features of the geologic models. The ensemble of Brugge models are chosen to contrast the different choices of control parameters used in generating the realizations. Commercial uncertainty and optimization software is used to calibrate region pore volume, inter-region transmissibility, and well connection factor multipliers for the coarse network models using the rate and pressure historical data for the first 10 years of the field life. The workflow is demonstrated through the application using a commercial flow simulator, to assist in technology transfer, but the underlying algorithms should be applicable to any commercial flow simulation package.
Automated Pattern Water Injection Optimization Using Streamline Simulation
Authors: Alameri Felwa , Bethapudi Bhanu Priyadarshini , Motlagh Masoud 
Published: November 2024

Abstract
Introducing a reservoir with multiple complexities such as faults and challenging geological characteristics, a study was catered to focus on optimizing water injection in an onshore field in the United Arab Emirates using the Pattern Flood Management (PFM) methodology. The primary goals are twofold: to maximize the utilization of available water injection resources for meeting production targets and to enhance hydrocarbon recovery. The scope encompasses a comprehensive assessment of current production and injection practices, studying the optimal distribution of water injection, evaluating injection rates for individual wells, and demonstrating the added value at both field and reservoir unit levels.
The study was carried out during 2023, where two sub-reservoirs were chosen to assess optimum water injection scheme by utilizing the available water injection with the aim to meet the required oil sustainable rate targets in the short term and increase recovery. The methodology commences with an exhaustive sensitivity screening, followed by optimization utilizing the full field model. The sensitivity runs delve into various parameters' impact on PFM optimization results, with a particular emphasis on optimizing oil production. Key parameters such as Constraint Handling undergo thorough evaluation, identifying options that provide flexibility. Areal analysis is conducted to pinpoint short-term injection optimization opportunities. Subsequent optimization using the full field model underscores the criticality of selecting suitable parameters and candidate injector groups.
The recommended approach for optimizing production is the implementation of pattern injection PFM over GPMAINT guide rate, which has been proven to be more effective. This strategy has led to a substantial economic gain, fully compliant with the Shareholder's guidelines, amounting to 48 MMUSD. Additionally, introducing more flexibility in injection rate limits beyond the Shareholder's guideline could potentially yield an even greater economic value, estimated at 150 MMUSD per well with higher injection than Shareholder's guideline and 650 MMUSD with increased injection volume. Moreover, areal analysis has indicated the possibility of additional injectors in the north-western flank of one of the sub-reservoirs, warranting further study to ascertain the number of wells and their precise locations.
This paper showcases the outcomes of one of the sub-reservoirs that were studied, emphasizing on the importance of optimizing water injection by utilizing up-to-date technologies such as PFM. PFM technique is a powerful tool in managing the optimum water injection volumes and schemes in order to reach an ultimate goal reflecting production mandates required whilst not jeopardizing the health of the reservoir.
Challenges and Lesson Learned from Multiphase Flow Metering Deployment During Different Production Phases in Tight Carbonate Reservoirs
Authors: Almurayshid R. , Aramco Saudi , Kaipov Y. 
Published: November 2024

Abstract
The application of Multiphase Flow Meters (MPFM) is being widely used in various fields with different flowing conditions. It is imperative to validate MPFMs for specific reservoir fluids and production profiles to ensure accurate measurements or production rates. This paper discusses a field-based data analysis from MPFMs performance in tight carbonate reservoirs during different phases of field development. The proposed methodology encompasses a comprehensive approach to validate MPFM's performance throughout the production life cycle of studied wells. The production profile of tight carbonate reservoirs changes significantly over time compared to traditional reservoirs due to their rapid decline. The selected MPFM should provide the required accuracy of measurement for gas and liquid rates, and water-cut throughout the entire production cycle. The MPFMs in this study were tested under different conditions to represent early and late stage of production under natural flowing conditions. The MPFMs flow measurement accuracy was benchmarked against a test separator by flowing the well at different choke sizes. The PVT model was built based on the equation of state to convert flow rates from operating to standard conditions. The comparison tests were conducted on six different wells representing different phases of the production life cycle. Two wells were measured during the early stage of production. This period begins with a 100% water-cut followed by gas breakthrough, and is characterized by a high water-cut and rapidly changing salinity. The end of the early stage period is marked by the maximum expected gas rate and stable condensate-gas ratio (CGR) of the studied well.
Another three naturally producing wells were previously tested during extended well testing operations, representing the late stage of production. The fluid properties during this period are constant, and the flow was stable. It has a low water-cut, medium gas rate, and reduced CGR due to depletion effect.
This Paper discusses a holistic approach to compare two metering technologies (MPFM and Testing Separator) for tight carbonate reservoirs including a comparative study and field-based verification tests. The advantages and disadvantages of both technologies will be highlighted in detail. addressing different flow conditions at multiple phases of the production life cycle.
De-Risking Al Bashair Overpressure in the Khazzan Field, Oman - Learning from First Well Control Event
Authors: Al Anboori A. , Al Toubi S , Al Saadi S , Al Lawati M. H. , Abdeen M. Z , Volk H. 
Published: November 2024

Abstract
The giant Khazzan gas field, onshore Oman has been on production since 2017 primarily from the Cambro-Ordovician paralic Barik Formation (avg porosity 6 pu). It covers an area of 2800 km2 and was originally interpreted to be a flat structure with a simple pore pressure and fracture gradient (PPFG). However, step-out Well A encountered the first well control event in Khazzan due to anomalous overpressure (1.56 SG, ~10100 psi) just below the Barik in the Al Bashair formation. The kick was successfully killed but with high NPT due to long killing operation which led to a differentially stuck pipe fish and ultimate side-track. The well control incident flagged limitations of offset wells and triggered a detailed investigation to derisk the Al Bashair overpressure given that all wells are required to TD in Al Bashair or deeper. An investigation was conducted to get to the root cause of the incident. Subsurface analysis was also carried out to identify the source of the overpressure in this well and predict potential overpressures likelihood in future wells in Block 61. The subsurface work involved fluid analysis (kick gas samples were acquired); mudlogging and log analysis, 2D pressure modelling, and geological and structural interpretation using a combined regional wells/seismic database from the Khazzan high to the Ghaba and Fahud salt basins (beyond Block 61). Understanding the kick controls enabled the creation of a Combined Common Risk Segment (CCRS) map for the Al Bashair overpressure. The investigation identified Well B, located outside of Block 61 and some 40 km from Well A that had encountered surprisingly identical overpressure in the Al Bashair. Both wells are located in a structurally complex area, that is next to a major bounding fault, the presence of structural closure and salt across the fault. Both wells also show unprecedented high CGR in the Al Bashair kick gas compared to the rest of Block 61 formation fluids, indicating a different source of kick gas. In this paper we investigate a list of possible overpressure scenarios: simple faulting, centroids, compaction disequilibrium, deep faults and reservoir cracking. In summary, the best-fit working model is that the Al Bashair isolated sands are oil charged through fault systems, and overpressure is created by volume expansion during oil-to-gas cracking. A CCRS map was finally generated to highlight areas of elevated risk to overpressure based on structural complexity. Important learnings came out from this first well control event in Block 61. The kick also required the successful first use of stress caging in Block 61. An integrated approach with a multidisciplinary team was undertaken: well control, well engineering, geomechanics, fluids and subsurface.
Best Practices for Designing High-Speed Centrifuge Capillary Pressure Experiments
Authors: Efara Mohamed S. , Markovic Strahinja , Ryabukhin Anton , Al-Shalabi Emad W. , Alameri Waleed 
Published: November 2024

Abstract
Centrifuge experiments are essential for studying capillary pressure and relative permeability in underground geological formations for hydrocarbon production and gas storage applications. Despite the negligible changes in centrifuge equipment design over time, inconsistencies in fluid saturation and pressure interaction characterization are typical. This is due to the variations in test procedures, interpretation, and history-matching across different laboratories or when compared with other standard experiments. This work proposes considerations and best practices for designing a centrifuge experiment. This work utilizes qualitative and quantitative analyses to assess centrifuge tests including the drainage and the imbibition. The study starts with evaluating hardware, trade-offs, and pre-test calibration. Afterward, it delves into the effects of calibration factor (CF) and core-holder deformation due to centrifugal forces on saturation calculation presenting CF calculation methods. Design considerations for rotational speeds and best practices as a function of centrifuge equipment design, and common misconceptions. Other issues discussed include production data analysis and methods for improved processing of the pixel data obtained from high-speed cameras, end-point saturation values validation methods for accurate residual fluid saturations, mass and volumetric calculations, NMR T2 cumulative amplitude, and manual image processing.
The findings of this study highlighted and quantified the impact of poor experimental design on centrifuge results based on three carbonate core samples. The findings of this study showed that inadequate pre-test calibrations and high noise levels in high-speed images predominantly influence estimates of residual saturations. These two factors resulted in cumulative errors in residual saturations varying from 1.5-10%. We further assessed the reliability of end-saturation validation techniques using multi-step capillary pressure centrifuge test results obtained for Indiana Limestone core samples, among which saturations calculated from manual pixel analysis and volumetric method were the most reliable. The design of centrifuge experiments is a topic that has received relatively little attention in the literature. This work offers comprehensive guidelines derived from extensive SCAL experience, presented as a critical review. The findings presented in this work can be used to design an accurate experiment and gain reliable capillary pressure data, which are essential for subsequent pore-scale modeling, numerical simulations, as well as production optimization and enhanced oil recovery (EOR) strategies.
Radio Frequency Identification Device (RFID) Technology Challenges the Conventional Approach for Critical Lower Completion Isolation
Authors: Munro C. , Zaki S. 
Published: November 2024

Abstract
Eliminating intervention operations during lower completion deployment whilst mitigating risk and associated costs has become a significant challenge in the Oil & Gas industry. Demonstrating innovative solutions which provide alternatives to conventional ball drop or dart-based systems for reservoir isolation has been a must for many operators. This paper focuses on the adoption of an intervention-less Radio Frequency Identification (RFID) Reservoir Isolation Valve (RIV) for critical lower completion installations.
Utilizing standard isolation methods such as balls/seats, darts, and indeed e-line conveyed intervention plugs has been shown to introduce complexity, risk and cost implications on the types of operations required to isolate the lower completion. These complexities can manifest themselves in the form of increased intervention time along with associated technical challenges in circulating balls/darts onto seats, depending on the chosen isolation method. To mitigate these risks, an RFID remotely activated Reservoir Isolation Valve (RIV) was developed, which was shown to alleviate the concerns by providing multiple built-in downhole contingencies considered to be standard across the platforms.
Following a thorough review of the operation requirements along with the associated historical challenges compared to conventional isolation methods, the intervention-less RFID RIV solution was developed, manufactured, tested, and subsequently deployed in a horizontal well. The technology is installed in the open position and is activated by specifically engineered RFID tags encased in a transportation carrier which allows it to be introduced to the flow stream and pumped to the toe of the well, even in highly deviated/horizontal reservoir sections. Once the RFID tag has reached the onboard electronics in the RIV, it passes a pre-programmed command to the RIV instructing it to close after a pre-determined time interval and isolate the lower completion. The programming logic also allows for built-in "timer" contingencies in the unlikely event that the RFID tags do not reach the tool as intended.
The RIV was deployed successfully and provided "on-demand" reservoir isolation at the first attempt by circulating the RFID tags to depth. Subsequent operations, such as liner hanger/OH (open-hole) packer setting and the remote opening of the sand screens, were then performed against the closed liner system enabled by the RFID RIV.
This paper details the novel approach taken by the application of RFID isolation systems and explains the benefits demonstrated when an RFID RIV was installed as part of a lower completion system for a critical application. What drove the success of this trial was the focus on risk mitigation compared with conventional systems designed to provide positive toe isolation, which ultimately provided a reservoir barrier and means to complete as per program.
Machine Learning Driven Problem Detection for Production Optimization - Well Cease to Flow Prediction
Authors: Reddicharla Nagaraju , Alnaqbi Khawla Rashid Sulaiman Rashid , Venkata Subba Ramarao Rachapudi , Utama Indra , Ali Mayada Ali Sultan , Kumar Amit , Gönczi D. , Toumi O. , Sidaoui A. , Abdalla Ramez 
Published: November 2024

Abstract
Early detection of a liquid loading has always been a challenging task. Typical existing best practices are the "cease to flow" problem occurs when a well is unable to remove the liquids produced from the wellbore. These liquids accumulate at downhole, forming a static column that creates back pressure against the formation pressure, leading to a decline in production until the well stops producing.
Primary technical challenges include the complexities of reservoir dynamics, variability in well and fluid properties, and environmental factors. Additionally, procuring labeled training datasets for machine learning models and interpreting these models specifically for well cease-to-flow or liquid loading issues are significant obstacles. Initial models used raw features such as allocated volumes for each production phase, real-time information, well test data (wellhead pressure, flowline pressure), and historical problematic events.
To ensure early detection of well cease-to-flow events, two distinct machine learning models were developed. These models assess the potential occurrence of such events using documented historical data. Events were categorized into three classes (healthy/normal, pre-event, and cease-to-flow conditions) based on reported flow-down code events, encompassing the entire history of these codes for accurate model labeling. The multiclass classification model was constructed to identify issues related to well cease-to-flow, with the goal of estimating the well cease-to-flow class. The primary aim of this model is to detect events preemptively, validated through blind testing on completions not included in the model's training set.
This solution has been successfully tested and implemented in a giant mature oil field. The chosen optimal model has a positive recall of 75%, meaning that out of 100 instances of well cease-to-flow events, 75 are accurately predicted. Conversely, the negative recall is 96%, indicating that out of 100 instances classified as "non-well cease-to-flow," 96 are correctly identified. This highlights the model's effectiveness in minimizing false alarms (4%) while successfully forecasting most problematic events.
Adopting a standardized approach significantly enhances profitability by optimizing intervention costs and maximizing incremental oil production (by reducing downtime and deferred production volumes). Improved opportunity selection optimizes workover schedules, reservoir monitoring, business plans, and intervention schedules, thereby enriching the knowledge base across the entire asset. Regular re-training of the machine learning models is crucial to accommodate the variability and evolution of field and reservoir conditions over time.