bluuebunny/crossref_metadata_2025 huggingface dataset:
Enabling Methane Emission Detection and Quantification in Robots
Authors: Schillaci Guido , Pencelli Manuel , Fiorucci Marta , Bonacchi Luigi Bono , Gori Valentina , Meloni Enrico , Paladini Lorenzo , Mattioli Erika , Russo Lorenzo , Salusti Lorenzo , Cioncolini Stefano , Politano Andrea , De Magistris Giovanni , Porciani Daniele
Published: November 2024

Methane is a potent greenhouse gas, contributing significantly to global warming and climate change. Methane emissions from natural gas systems have gained increasing attention due to their environmental and economic implications. The detection and quantification of methane leaks and emissions are crucial for minimizing their impact on the environment and ensuring the safety and efficiency of natural gas infrastructure. In recent years, advancements in sensor technology have provided new opportunities for accurate and cost-effective methane leak detection and quantification.

This paper aims to provide an overview of the current state-of-the-art in methane leak detection and quantification using sensors, focusing on the types of sensors, sensing principles, and applications. A particular focus is given to sensors that can be installed on mobile robots and drones. Traditional methods for methane leaks detection and quantification have limitations in terms of spatial and temporal resolution, cost, and accessibility. Robots equipped with gas and methane leak detection sensors have emerged as promising solutions to address these challenges.

This work contributes to the development of more effective strategies for reducing methane emissions and mitigating climate change. In particular, this paper explores the adoption of advanced sensors - such as Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors and optical gas imaging (OGI) cameras - and their integration into robots. We discuss the advantages of using these platforms for methane detection, including their ability to access hard-to-reach areas, real-time data acquisition, and high spatial and temporal resolution. Furthermore, we present an application in which we integrated methane and gas leak detectors on the payload of a legged robot, and we introduce a testing architecture for assessing the quality of standalone and mobile sensors for methane detection and quantification.

This manuscript is structured as follows. First, we review the state-of-the-art on sensors for methane leaks detection and quantification. We deepen the discussion by reviewing the most relevant robotic solutions integrating gas and methane leak sensors, highlighting their advantages and limitations. Therefore, we present our current work on integrating a methane leak detection sensor on a legged robot. We introduce also our current activities and architecture for testing OGI cameras. We conclude the paper by showing the opportunities that our solutions can bring, and by highlighting challenges and limitations of current sensing technologies to overcome.
Artificial Intelligence Driving Automation to Enhance Drilling Operations—First Deployment Offshore Africa Case Study
Authors: Zarra A. F. , Medea A. , Bianchini L. P. , Borra S. , Gravante E. , Szemat-Vielma W. , Banjo M. , Mouzali N. , Botnan E.
Published: November 2024

Artificial-intelligence (AI) driven systems are becoming the new standard in the drilling domain. This is the first field application performed offshore in Africa with the main objective of enhancing drilling operational efficiency and bottomhole assembly (BHA) integrity by advising the optimal drilling parameters, operational procedures, and drilling dysfunction mitigations.

This smart system, driven by AI, seamlessly integrates the drilling operations planned engineering and procedures with the real-time operational data to monitor and control the operations execution using a data-driven approach. By design, this system enables the coordination and integration of the data and teams through a digitally orchestrated process, while the AI is primarily in control of the operations. The system's current and next steps can be easily visualized and overridden by the driller if and when needed.

For this first application in Africa, close coordination and collaboration between the operator, the technology and service provider, and the rig contractor was a key element from the early stages of the project. A rig survey determined the most appropriate locations for the required equipment on the rig. Once the system was deployed and commissioned, the calibration was performed on the top-hole drilled sections, while the driller received hands-on training with the utilization of the system. The system seamlessly loaded the digital drilling program and recalibrated the operational parameters, which included the rate of penetration (ROP), revolutions per minute, weight on bit (WOB), and flow rates, using the real-time data. At the time of writing, a total of three wells, each with three sections were drilled using this application, resulting in continuous ROP improvement up to 2.5X factor on the last well. The combination of science-based models with real-time data running on the edge device at the rig enabled a step-change in the way AI-driven systems can drive automation, thereby enhancing the drilling operations.

In this paper we will briefly describe the reasoning from the operator the decision to continue with this digital transformation of the operations process, and the technical requirements and process for the first successful deployment in Africa, with an overview of the results achieved. Moreover, we will also describe a novel workflow for a fully autonomous operation that combines several smart applications enabling a seamless digital collaboration between the office and the rig.
Remal Project: Utilizing AI & Data Analytics to Predict Sand Movement on Gas Plant in the UAE
Authors: Alkhoujah K. , Al Ali A. , Al Ghassani M. , Al Mansoori M. , Al Ameri M. , Bhatta Y. , Dieterle A.
Published: November 2024

Shah Gas field (SGF) is located within the Empty Quarter desert premises, where the sand dunes are prominent with varying heights up to 40 m. If the sand blows are severe, the sand would cover the access road and block the access and disrupt plant activities. SGF team regularly patrol to check several facilities including Main Asphalt roads, Gatch Roads, Gas Gathering Pipelines Corridors, Well Pads boundary, Main Plants boundary and the major product pipelines corridor to inspect and report of any sand accumulations that require to be cleared. Accurate detection and forecasting of sand accumulation is crucial for scheduling sand cleanup activity and successful oil transport. As it requires huge manual effort and is not cost-effective to measure the true value of sand accumulation at different geographical areas. This paper will present a successful solution to effectively predict and clear sand in a timely manner and at a lower cost in across the Shah Gas Plant and Access Roads area. The invention has been developed as a part of ADNOC Sand Accumulation (Phase 2) for ASG organization in collaboration with Siemens.
Integrated Techno-Economic and Life-Cycle Assessment of Subsurface Energy-Storage Technologies for Renewable Energy
Authors: Tayyib D. M. , Sekar L. , Okoroafor E. R.
Published: November 2024

The reliance on renewable energy in the modern energy mix is expanding due to two primary factors: the need to decrease dependence on fossil fuels and the imperative to lower carbon emissions. Integrating renewable energy sources into the grid is crucial for mitigating greenhouse gas emissions. However, a significant challenge associated with increasing reliance on renewables is the inadequate methods for effective storage of this energy, which is essential for minimizing curtailment and maintaining grid stability by balancing supply and demand. This study identifies the technical and economic feasibility of two potential storage technologies: subsurface heat storage and subsurface hydrogen storage. As a first step this study used the average energy curtailment data from the Energy Reliability Council of Texas (ERCOT) to demonstrate the applicability of these energy storage technologies. This study also quantifies the technical and economic feasibility of each technology using reservoir simulation by determining the subsurface withdrawal efficiency. It also determines the optimal storing option in terms of estimated efficiency, levelized cost of storage, and GHG emissions. Results showed that around 29-57% of the curtailed energy can be recovered through subsurface H2 storage, whereas around 6-17% can be recovered through synthetic geothermal storage. The levelized cost is between $102-115/MWh for H2 storage and $19-73/MWh for geothermal heat storage. The conclusion is that subsurface H2 storage can be a more promising technology for mitigating renewable energy curtailments from both technical and economic standpoint.
Ultra Mobile Solar Hybrid Solution for Mobile Rig Camps in Abu Dhabi
Authors: Grizi Ameen , Shadid Feras , Shamma Hassan , Al Hamlawi Imad , Baptista Luis , Mohammed Tariq
Published: November 2024

This paper presents the implementation and performance evaluation of a mobile solar-hybrid power system deployed by an onshore oil & gas operator in the UAE. The system uses an ultra-mobile, containerized solar solution to power mobile accommodation camps, traditionally powered by conventional diesel generators. Since November 2023, the operator has been using this system to power a mobile rig camp in a prolific field southwest of Abu Dhabi. The system comprises two 75 kWp solar containers (including inverters), a control panel (energy management system and data logger), and two diesel generators (one prime and one standby), with capacities varying according to load requirements over the operation duration.

The energy management system, developed in-house by the provider, maximizes solar utilization, and ensures efficient operation of all energy sources. The system has provided substantial benefits over its nine-month operation, primarily reducing CO2 emissions by 47% compared to diesel-only generators. This equates to approximately 262 tons of CO2 or around 100,000 liters of diesel saved. The containerized design enables rapid transport and installation, with mobilization times of under four hours. The hybrid system also lowers fuel consumption and maintenance costs, improving the economic viability of running the mobile rig camp.

Real-time monitoring is crucial to the system's safe and efficient operation, providing high reliability and uptime without requiring excessive onsite personnel. Compared to the previous diesel generator setup, the hybrid system's reliability is a significant advantage, eliminating frequent shutdowns caused by manual generator switchovers. Overall, the deployment of the ultra-mobile containerized solar-diesel hybrid power system has been successful, demonstrating significant CO2 emissions reduction and improved operational efficiency and cost-effectiveness. The lessons learned from this project highlight the importance of sustainable energy solutions for off-grid applications in the oil and gas sector.
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

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

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

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

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

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

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

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

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.
Technology Selection in CO2 Recovery in Relation to CO2 Breakthrough Progression in Upstream Facilities
Authors: Nandi Bashistha , Punja Aditya , Al Kaabi Khawla Obaid
Published: November 2024

The paper intends to share insights on importance of appropriate technology selection for CO2 recovery package in upstream oil and gas facilities in relation to initial to final breakthrough concentration and share knowledge gained during various concepts of EOR projects. Paper also intends to share end user's need to further optimize OEM's design and any development requirement to suit the OPCO's need.

The paper would like to first highlight valuable knowledge gained during concepts of EOR projects while dealing with multiple CO2 recovery package vendors. Then showcase merits and demerits/limitations on various selected technologies (such as 2-stage Amine absorption Vs Hybrid technologies comprising of membrane followed by selective Amine absorption) for a certain initial to final CO2 breakthrough concentrations including CAPEX and OPEX level comparison.

The paper will conclude by highlighting that after CO2 re-injection for EOR projects, the breakthrough concentration of CO2 and H2S (mol%) will be the determining factor for technology selection for CO2 recovery package. As breakthrough differs in various reservoir zones, so one set of technology selection cannot be made standard practice.

Following conclusions can be made:

If CO2 concentration is above about 10% and no H2S breakthrough is expected, then 2 stage membrane process can be selected. If CO2 concentration is lesser than 10% and H2S breakthrough is significant then 2 stage MDEA is a better proposition till the limitation of AMINE package, then switch over to Membrane technology can be evaluated as 2nd phase. If CO2 concentration is above 10% and H2S breakthrough is expected, then 2 stage membrane process clubbed with selective Amine absorption unit (to remove H2S) shall be selected. If initial CO2 concentration is lesser than 10% for a significant time period, then Only 2 stage MDEA process shall be considered and scope for future assessment shall be considered in overall design.

End user finds it extremely difficult to deal with the significant delta P that each stage of membrane requires. Hence urgent need is envisaged for further development of low pressure drop module to reduce the ever-increasing OPEX. Also envisages further development need for removal of H2S through membrane technology along with CO2.
Assessing Caprock Competency - A Comprehensive Geomechanical Study and Analysis in a Field in Middle East
Authors: Nathan Kumar , Kamarudin Muhamad Aizat , Mohammed Mustafa Jawad , Wang Arthur Goh Ji , Amat Hadi , Choon Tan Boon
Published: November 2024

When a well reaches its end of productive life, a well abandonment need to be safely executed to ensure the well does not pose any environmental or safety hazards. The process of well abandonment involves a thorough assessment and planning phase, which includes evaluating the well's condition and the abandonment procedure to ensure compliance with regulatory requirements and environmental standards. This process also involves isolating zones where oil and gas formations are present to prevent hydrocarbons from leaking to the surface and to avoid cross-contamination.

This study aims to analyze the caprock integrity of Field G which consists of both clastic and carbonate reservoir. It addresses the critical need for sustainable development and abandonment practices that ensure long-term environmental and reservoir containment. The created caprock model and workflow eliminates the necessity of performing conventional full abandonment of existing well and drilling new well to access the deeper oil-bearing reservoirs in Field G. The study consists of comprehensive analysis of various logs and core data. Synthetic logs were created through numerical methods, followed by the computation of static and dynamic geo-mechanical properties. Triaxial and uniaxial tests were meticulously planned and executed on core plugs extracted from four different wells across Field G, serving as calibration data for the geo-mechanical model. A UCS model specific to Field G was developed and compared with established correlations in Middle East. Additionally, drilling data including FIT and LOTs were utilized to construct the pressure profile of Field G. The created model and workflow are then used to assess the caprock competency. Multiple competent caprocks were identified by studying the geo-mechanical properties of the rock. The UCS model that were generated specifically for Field G gives accurate prediction of the strength of such caprocks.

This comprehensive caprock assessment has not only enabled us to make informed decisions regarding well deepening to appraise deeper reservoirs but has also facilitated partial abandonment of certain wells, allowing for production from shallower reservoirs without the necessity of drilling new wells. Additionally, the generated UCS model and correlation can be used as a reference for nearby fields to predict the rock strength in the absence of core data. This study provides valuable insights for the oil and gas industry regarding the assessment of caprock competency, aiding in informed decision-making during both development and abandonment stages. Such insights enable the industry to attain more reliable and cost-effective outcomes during abandonment processes, thereby contributing to sustainable resource management practices.
Improve Fuel Gas Thermal Efficiency of Once Through Steam Generator (OTSG) for Higher Ultimate Recovery
Authors: Al Fazari M. , Yazeedi Z. , Hinai A. , Subhi H. , Al Hajri S.
Published: November 2024

Petroleum Development Oman LLC (PDO) is Oman's premier oil and gas producer that operates several fields at "A". As "A" Thermal fields contain heavy oil, PDO plans to significantly increase oil recovery by increasing the steam injection which is having high energy intensity.

The main objective of this paper is to demonstrate how Lean Six Sigma (LSS) approach has been utilized in improving the Thermal efficiency of the Once Through Steam Generators (OTSG) package and reduce the emissions and improve on cost efficiency.

The OTSGs are designed to generate saturated steam of a nominal 80% quality, the heat input for which is produced by combusting natural gas fuel. It is contributed of around 40% of total thermal development and key enabler for thick oil production. Currently, these generators have an average thermal efficiency of around 75%, where design efficiency is between 82% to 86%.

Several lean and six sigma tools and techniques were used to define the thermal efficiency improvement opportunities including; A SIPOC (Suppliers, Inputs, Process, Outputs, Customers), cost of poor quality (COPQ), RASCI, stakeholder analysis, communication Plan, detailed process mapping, Process Capability analysis, graphical and statistical analysis, Effort and Impact matrix, failure modes and effects analysis (FMEA), control plan, and leader standard work (LSW) to sustain Improvements.

The utilized set of Lean Six Sigma techniques and statistical tools led to reducing the fuel gas consumed in These OTSGs by 17% and eliminated wastes and preventive maintenance cost with an expected savings of around 5% per year.

A structured action closure plan has been developed and completed for this project resulted in implementing the counter measures and prepare the required fixing reports and acquire necessary approvals. Below are the results of the selected OTSG pilot.

Implementation of quick win counter measures by running the OTSGs on stabilize mode, improve the gas fuel burning efficiency by 3%. While piloting the implementations in the selected OTSG, another pilot has been done for another OTSGs (two) with an additional of 2% improvement in fuel gas Thermal efficiency from 83% to 85%.
Enhancing Edge-Based SRP Production Optimization Algorithm with Fast Loop Mitigation
Authors: Hyder Zeshan , Yermekova Maya , Kemp Carl , Srivastava Saket , Gambaretto Agustin , Pazniak Yury , Pivtoratskaia Svetlana , Agarwal Ambica , Ambade Amey
Published: November 2024

Sucker rod pumps (SRPs) stand as the foremost artificial lift (AL) technique globally, with advancements dating back to the development of the wave equation in the 1960s. Leveraging edge-based technologies, a workflow has been devised, building upon the foundation of existing Pump-Off Controller (POC) capabilities. This workflow seamlessly integrates machine learning (ML) based dynamometer card classification for real-time event detection with forward-thinking logic to autonomously optimize SRP operation setpoints. Operating within an Industrial Internet of Things (IIoT) framework, high-frequency dynamometer cards and pump data are analyzed.

The workflow incorporates two distinct sets of controls tailored for SRPs. The first set, Fast Loop Mitigation controls, utilizes outputs from an ML algorithm to promptly process and classify surface and downhole dynamometer cards in real time (Bruns, Sharma and Whitley 2022). These mitigations are specifically designed to identify and address common SRP issues such as flatlining, fluid pound, gas interference, and tagging as they arise.

The second set of controls, known as the Production Optimization (POPT) Algorithm, collects operational data and monitors it through a dynamically shifting time window (Gambaretto, et al. 2024). This window provides a relevant snapshot of operational history, enabling the algorithm to synthesize trends into performance indicators. These indicators are then utilized to forecast the most optimal pump operating setpoint for enhanced efficiency and productivity.

Results from testing underscore the significant benefits achieved through the synergistic application of both control systems. On tested wells, inferred production saw a notable average increase of 15%, while runtime exhibited an average uptick of 3%. Concurrently, cycling decreased by an average of 29% by maintaining optimal pump fillage (Gambaretto, et al. 2024). Moreover, stable operating conditions were observed throughout.

This approach represents a paradigm shift, integrating the mitigation of short-term potential pump damaging issues, with the long-term POPT algorithm for maintaining optimal well production. By leveraging the strengths of both algorithms, it offers a holistic optimization strategy that addresses immediate challenges while aligning with long-term objectives, thereby providing a comprehensive solution for well optimization.
Petronas’ Journey in Developing Risk Based Maintenance Approach for its Offshore Pedestal Cranes
Authors: Basri H , Ekhwan A , Hun KP , Natarajan J
Published: November 2024

Over the last three years, PETRONAS has spent approximately USD 36 million on the maintenance of a total of 107 pedestal cranes. This has emerged as the main contributor for the overall static equipment maintenance costs. In the pursuit of operational efficiency and cost optimization, PETRONAS is pioneering a shift from traditional time-based maintenance to a Risk-Based Maintenance (RBM) approach. The RBM methodology aims to enhance safety and reliability while optimizing overall expenditures.

The proposed methodology systematically divides the pedestal crane into key systems/components, subjecting each to a comprehensive risk assessment. Risk is calculated as the product of Probability of Failure (POF) and Consequences of Failure (COF) for individual component. PETRONAS has crafted a methodology for determining POF and COF, considering factors such as historical failure data, remaining design life, compliance with maintenance programs, crane category as per API 2C, usage of the crane, location of the crane and prior derating instances.

A customized scoring system, harmonized with PETRONAS ‘s risk matrix, quantifies the POF and COF for each component, offering a refined evaluation of risk specific to the crane's configuration and operational context. This approach guides the formulation of a maintenance program, with higher-risk components receiving more focused and frequent action plan.

The integration of continuous evaluation into the maintenance process is crucial. Thus, post each maintenance cycle, risk assessments are dynamically re-evaluated, determining subsequent inspection schedules. This adaptive approach ensures that maintenance efforts align with the evolving risk profile of the crane, enhancing responsiveness to changing operational conditions over the years. Incorporating a systematic and data-driven RBM methodology for offshore pedestal cranes, this initiative epitomizes PETRONAS's commitment to innovation and efficiency. By shifting from a time-based to a risk-based, PETRONAS anticipates substantial reductions in overall maintenance costs, improved operational efficiency and heightened safety in offshore lifting operations.
Innovative Cold Start-Up Solution of BOG Compressors without Flaring at LNG Terminal
Authors: Ghosh Samiran , Deo Atul
Published: November 2024

In October 2018, TotalEnergies and the Adani Ports and Special Economic Zone Limited (APSEZ), both as Co-Sponsors, signed an agreement to jointly develop a 5 metric ton per annum (MTPA) Nominal Capacity Liquefied Natural Gas (LNG) Regasification Terminal at Dhamra Port. Dharma LNG Terminal Private Limited (DLTPL), a wholly owned subsidiary of Adani Total Private Limited (ATPL), whereby ATPL is a 50-50 joint venture between TotalEnergies and APSEZ. Dhamra Port, located on India's eastern coast, is a modern seaport managed by The Dhamra Port Company Limited (DPCL).

The Project's location by Dhamra Port, a deep seaport with draft of up to 18 meters, is designed to handle large ships capable of accommodating super cape-size vessels up to 180 000 DWT and various other types of cargo. It is strategically placed between Haldia and Paradeep, close to resource-rich areas like Orissa, Jharkhand and West Bengal. Commissioned in April 2023 as the first terminal in the eastern region of the country, the terminal has been continuously operating since then.

The terminal is a marvel of modern engineering, boasting an array of sophisticated equipment essential for the efficient handling and processing of LNG. This includes unloading arms, full containment LNG tanks, Boil-Off Gas (BOG) compressors, Recondenser, High Pressure LNG pumps, LNG vaporizers, metering stations, dispatch facilities, LNG truck loading facility etc.