bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Corrosion Classification Utilizing Machine Learning and Deep Learning Algorithms
Authors: Alquraini Ali H. , Al Sadah Hussain H. , Bayounis Ryyan A. , Al-Kadem Mohammad S.
Published: November 2024

One of the most critical elements in petroleum production engineering is downhole casing integrity. Thus, monitoring downhole casing corrosion is an important element as it ensures the safety and integrity of well assets. Corrosion logging is one important tool that provides valuable information on casing metal loss, that is used as part of a comprehensive monitoring program. In this paper, a new methodology that utilizes advanced Machine Learning (ML) and Deep Learning (DL) to classify downhole casing corrosion integrity status is presented. This method provides valuable additional information and insight that can improve safety.

The proposed methodology was to develop an intelligent system using ML & DL that automatically classifies casing corrosion and provides a predicted well downhole corrosion classification to engineers. Firstly, the proposed system actively fetches previously conducted downhole corrosion classification data. Secondly, an advanced pool of ML algorithms was created, and trained on fetched corrosion data. Thirdly, the ML pool evaluated and tested to be uploaded into the system. Finally, newly acquired data for unlogged or old log wells are fed to the advanced ML model to automatically classify downhole casing corrosion based on classes from low to high to engineer and notify them about wells with predicted high corrosion.

After finalizing the advanced ML system, it was evaluated on its performance to accurately classify downhole casing corrosion of well and provided system users with targeted classification results. In addition, performance of the system on classification, mitigation and mapping attributes were evaluated using ROC-AUC performance matrix which is a probability curve. After that, testing and evaluating the ML model showed a promising outcome scoring accuracy exceeding 85 % indicating the high efficiency of the model to accurately classify casing corrosion status instantaneously. The developed ML system enabled production engineers to proactively monitor downhole corrosion status reliably and securely. It's worth noting that by implementing such a system have yielded significant impact on our operation leading to both cost, time and recourses optimization. Moreover, the developed corrosion model optimized of thousands of casing corrosion logs conducted through classifying of downhole casing corrosion for unlogged ones, to better optimize resources and prioritize logging highly classified wells to be logged.

The proposed system leads to a fast and substantial improvement in acquiring a desired result field-wise in no time. Also, the system provides a detailed description and analysis of the downhole corrosion status to engineers. The developed downhole casing corrosion system has yielded promising results in prediction of wells with higher metal loss. This promotes safety by improving the existing comprehensive well integrity surveillance program.
Diethyl Ketone Based Waterflood in Carbonate Reservoirs-Boosting Fluid-Fuid Interaction and Oil Recovery
Authors: Yonebayashi Hideharu , Hiraiwa Takeshi , Yamada Tatsuya , Terajima Kohei , Yamamoto Kohei , Khlaifi Khuloud Tareq , Hoeth Laurien Wichers , Stalker Robert , Graham Gordon
Published: November 2024

Water-based enhanced oil recovery (EOR) has become more important in oil fields which have applied water injection because of lowering additional investment with use of existing facilities, minimizing carbon footprint, and keeping profit of sales gas by excluding a use for gas-based EOR purpose. Thus, several EOR options such as low salinity water (LSW), polymer, and hybrid methods have been studied in Middle East carbonates. This paper focuses on a new water-based option using dialkyl ketones as more polar solvents which have larger dipole moment compared with common ones such as dimethyl ether, ethanol, etc. The previous hybrid EOR studies highlighted an advantage of diethyl ketone (DEK) for boosting LSW EOR effectiveness in our objective offshore giant carbonate reservoir. The DEK advantage was proved from both aspects of core floods and fluid-fluid interaction (FFI) which forms micro-dispersion phase as an interfacial reaction of oil-water mixing. This paper pursued further potent advantage of adding DEK to non-diluted sea water (SW) injection. This option can save the cost for desalination water make-up process and facilities which are required for LSW based option.

Based on the compatibility and phase separation results (i.e., no solid precipitation for all types of fluids: SW, LSW, formation water, and crudes and no separation up to 2% DEK in SW), the injectant of SW-plus-DEK was designed at 2 wt% DEK concentration in non-diluted SW for fluid-fluid interaction (FFI) test and core flood evaluation. The FFI test revealed noticeable increment of micro-dispersion ratio (MDR), representing water content increment due to oil-water interfacial reactions, by adding ketones in both SW and LSW cases. The magnitude of DEK additive impact on MDR increment was more significant in non-diluted SW cases (i.e., 3.7 times of MDR from 2.0 v/v for pure SW to 7.3 v/v for SW-plus-DEK) compared with LSW cases (i.e., 2.6 times of MDR from 20.3 v/v for pure LSW to 52.6 v/v for LSW-plus-DEK). The core flood test (CF05) reached an oil recovery factor of 44% IOIP after a secondary mode of SW injection and achieved extra recovery of +15.6% in a tertiary mode of SW-plus-DEK. This oil increment was the largest among the previous core floods (CF01: tertiary pure-LSW injection, +3.0%; CF02: tertiary LSW-plus-DEK injection, +10.7%; CF04: tertiary LSW-plus-DMK injection, +14.4%). Furthermore, the oil recovery in the CF05 was clearly quicker as shown in the steeper recovery profile compared with those of other LSW based cases (i.e., CF01, CF02, and CF04). From both aspects of pursuing ultimate oil recovery and saving cost for desalination system, the non-diluted SW-plus-DEK injection was concluded as potentially the most attractive option.
Oil&Gas Decarbonization: Low-Carbon Fuels and Carbon Capture & Storage Offshore Power Barges, a Comprehensive Concepts Comparison
Authors: Anfray J. , Pactat A. , Gaouyat E. , Boscals-de-Reals T.
Published: November 2024

The objective of this paper is to present and compare two concepts to decarbonize Oil&Gas production facilities, particularly remote deep-offshore assets. The two offshore power barges concepts, called PGU (Power Generation Unit) are either based on low-carbon fuels or on local Capture & Storage to supply low carbon power & enable offshore facilities electrification.

One of the main challenges of the Net Zero ambition for the Oil&Gas industry (scope 1 and scope 2) is the reduction of GHG emissions related to the fuel gas consumption for power generation (>70% of the CO2 emissions), particularly for offshore remote sites where Power from Shore is not feasible. Centralized floating Power Generation Units (PGU) can be connected to existing platforms and/or FPSOs to supply low carbon electricity.

Either with an offshore PGU fed with low carbon fuels (green or blue ammonia). The PGU include all the functions to store, treat & convert ammonia into a blend of hydrogen and ammonia. Either with an offshore PGU with carbon capture and injection into a local CO2 storage (CCS). The PGU includes a compact backpressure combined cycle, a post combustion solvent based (MEA), a capture package designed for 95% CO2 removal. Some units are directly integrated in the structure of the hull to optimize weight & space.

325 MWe of net electrical power are generated. This low carbon power is exported from the PGU to existing offshore facilities equipped with single cycle gas turbines (<40% efficiency, 500 gCO2/kWh). This decarbonized power is exported through subsea electrical cables. The two concepts are compared in term of technical feasibility, maturity, GHG reduction and costs. A Life cycle analysis was performed to evaluate the environmental impact.

These low carbon power generation solutions (30 gCO2/kWh) enable to stop gas turbines on site. The two concepts result in similar floaters dimensions and Capex, with a higher Opex for the carbon free fuel due to fuel cost. The offshore CCS concept is seen as a shorter-term solution, applicable in the current decade, main challenge being the availability of an adequate CO2 storage to ensure a sustainable local reinjection. R&D focus on technology innovations to reduce costs. As an alternative, the offshore NH3 PGU concept is a longer-term solution for hard to abate sites, where CO2 reservoir availability can be an issue. A Life Cycle Analysis (LCA) shows that ammonia chain shall be green or fully sequestrated. R&D is driven by the need for new technologies, fully fuel flexible Gas Turbines to address multiple low carbon fuels scenarios. These developments shall be started now to enable this concept when the low carbon fuel market will be ready.

The novelty relies on the development and comparison of offshore power barges concepts delivering a minimum of 325MWe, either based on low-carbon fuels (ammonia) or CCS to power & decarbonize existing assets.
Case Study of Implementing Various Hydraulic Fracturing Software in the Challenging Operational Conditions of Saudi Arabia
Authors: Yudin A. , Nurlybayev N. , AlYaseen A. , Qadir W. , Al-Jalal Z. , Aljawad M.
Published: November 2024

A multitude of pseudo 3D and advanced 3D modeling software solutions are available for hydraulic fracturing optimization. These tools serve as indispensable aides for predicting fracture behavior, optimizing performance, and making informed decisions. This paper provides an overview and comparative analysis of the existing fracturing simulation techniques available and concludes practices for simulating the fracturing that helps achieving the optimum fracturing job execution.

The comparative analyses were performed by using pseudo 3D and advanced 3D modelling software packages. All simulations were conducted in unconventional (<0.01 mD) and tight gas sandstone (0.1 – 1.0 mD). Each simulation revealed fracture behavior nuances specific to these reservoirs, providing valuable insights for informed decision. For the execution and data acquisition study, reliability and troubleshooting efficiency were used among the main criteria. Applications were grouped by geological and operational conditions.

For the first-time users and experts, the selection process can be daunting. Each software package comes with its own algorithms, assumptions, and limitations. Understanding these nuances is crucial for accurate predictions and effective decision-making, as even experts face dilemmas. As they need to delve into complex reservoir scenarios, and grapple with questions like: Which simulator accounts for geomechanical effects most accurately? How does each tool handle fluid flow in heterogeneous formations? What computational trade-offs exist between pseudo 3D and advanced 3D models? Current research endeavors to bridge the knowledge or understanding gap. By meticulously comparing various fracturing software tools, aiming to: quantify performance, by evaluating predictive accuracy, computational efficiency, and scalability; probe assumptions, by uncovering hidden assumptions within each model; benchmark against field data, by validating simulations against more realistic fracture behavior; recommendations, by providing evidence-based recommendations for tool selection based on reservoir characteristics, operational constraints, and user expertise.

This paper offers the readers a comparative analyses study that seeks to empower engineers, geoscientists, and decision-makers by unraveling the intricacies of fracturing software tools. Through rigorous analyses, it paves the way from raw data to informed decisions.
Boosting Offshore Uptime: Accurate Real- Time Sea State
Authors: Røstad J. , Aarset M. F.
Published: November 2024

Accurate, real-time sea state information is critical for ensuring the safety, efficiency, and economic viability of offshore operations. Traditional methods, often relying on distant forecasts or data from irrelevant water bodies, fall short in providing the precision required for effective decision-making and risk mitigation.

This paper explores the significant advancements in sea state measurement technologies, focusing on innovative methods adopted by early technology adopters. By leveraging advanced radar systems, analytics, and integrated sensor networks, these pioneers obtain precise, actionable insights into wave conditions directly from the operational site. Such real-time, location-specific data enhances the safety and efficiency of various offshore activities, from vessel navigation and stability to predictive maintenance and dynamic positioning systems.

The paper also delves into the critical role of accurate sea state information in the offshore energy industry. It examines the technological advancements in data acquisition and analysis, the integration of real-time monitoring systems, and the benefits these innovations bring to offshore operations. Case studies and practical applications demonstrate the superiority of these modern methods over traditional practices, highlighting the tangible benefits of improved vessel positioning, operational uptime, and overall safety.

By documenting these advancements and their practical applications, this paper aims to encourage the broader adoption of cutting-edge sea state measurement technologies within the maritime industry. Transitioning from outdated methods to precise, modern techniques marks a significant step forward in enhancing the safety, efficiency, and reliability of offshore operations.
Unlocking Reservoir Potential: AI-Driven Integrated Well Placement Under Uncertainty in a Giant Onshore Field
Authors: Ismail A. M. , Almusalli Alya Majed , Ramatullayev S. , Su S. , Al Kindi Z. , Mustapha H. , Kloucha C. K.
Published: November 2024

The proposed paper aims to introduce an innovative, automated workflow for identifying and drilling high-performance infill wells in the large onshore reservoir in UAE, a region challenged by its complex geological setting and reservoir dynamics. The focus is on leveraging multi-disciplinary data integration and advanced automation techniques coupled with machine learning to enhance the precision and speed of well placement decisions under complex conditions.

Our methodology employs a multi-disciplinary approach that synthesizes various static and dynamic data to map drilling opportunities and risks. We utilize an automated opportunity index generation procedure to rapidly identify potential infill well locations. By integrating these maps with existing well data and "no-go zones", we develop an optimized well placement target map. The procedure also includes automated design and optimization of well trajectories that account for surface and subsurface constraints both for producers and injectors, resulting in a tailored well design and trajectory that enhances drilling accuracy and efficiency.

Implementing our integrated, automated infill well placement solution in one of the UAE's largest onshore reservoirs with more than 1000 wells produced substantial results. The pilot project successfully extended the production plateau by several years, resulting in significant incremental oil production. Our approach also facilitated notable operational cost savings and improved the efficiency of well placement processes, with the time required for placement decreasing from several weeks to just days—an efficiency gain of up to 80%. These improvements were achieved by optimizing the number and placement of new infill wells at strategic locations, and by removing underperforming wells, which helped streamline operations and reduce unnecessary expenditures. This methodology not only optimizes well placement under complex geological and operational conditions but also significantly contributes in cost-effective reservoir management and enhanced production efficiency.

This paper introduces a novel approach by integrating multi-disciplinary data and automated mapping techniques to optimize infill well placement in complex dynamic reservoir environments. By streamlining the identification of drilling opportunities and risks, our methodology enhances the precision and speed of decision-making processes, offering practical benefits such as reduced new infill well count, optimized oil recovery, and cost-effective reservoir management—an invaluable resource for practicing engineers navigating similar challenges in their field.
Enhancing Survey and Scoping Operation with Digital Twin Approach for the Comprehensive Mapping and Asset Registration in Central Sumatra, Indonesia
Authors: Nainggolan Humisar , Shah Ardian , Wirawan Andi , Shamanta Westya Fiqhi , Hadi Rizky , Widiasri Irma Savitri , _ Hardianto
Published: November 2024

There are more than 150,000 pieces of equipment in scattered locations and 80 on-plot facilities which causes: difficulties in identifying equipment, inefficiency of scoping and surveying, inaccurate planning. Triggered from those circumstances, an internal team has been developing a System for Comprehensive Mapping and Asset Registration with digital twin approach. It will help users to identify equipment accurately, to reduce time for scoping and surveying physically. Ultimately, it would resolve inaccurate planning issues because it could interface among those applications.

The method used was divided into 5 (five) phases: Phase 1 is the development of geospatial data for all equipment using GIS application. Phase 2 is the development of 360View application (a virtual model that replicates a physical object or system). Phase 3 is the development of facility equipment information database using company server. Phase 4 is the integration of those into one web-based application that can be accessed by all authorized users. Phase 5 is the development of standard operating procedures.

This Digital Twin initiative was started in early 2023 and has been implemented in 8 (Eight) gathering Station and 10 (Ten) Transmission Line during the year. It has succeeded in reducing emission about 300 KG CO2 print from fuel consumption used during physical scoping and surveying, feeding data for hydraulic pipeline simulation to support projects, well development, construction, and maintenance teams. This new approach also eliminates the possibility of human errors during scoping and planning phase because all information needed has been provided in one stop shopping application. In total, this application could reduce the cost by about USD 260,000 per year.

With the implementation of this application, it is significantly transforming habits from physical surveying and scoping became virtual mode without decreasing of the objectives of the activities. As stated in the above paragraph, it is improving safety aspects and reducing carbon footprint. Finally, it eliminates the possibility of human errors and become one stop shopping application. This application might be beneficial to be implemented into others Oil & Gas Company with similar operation.
Surfactant Flood in a Tight Carbonate Reservoir in North Oman
Authors: Wang Y. , Al-Naabi A. N. , Al-Hasani M. A. , Al-Naabi A. M. , Al-Sinani I. S. , Al-Jabri S. H.
Published: November 2024

Daleel has undertaken an extensive endeavor to establish the technical viability of chemical enhanced oil recovery (EOR) method in the Shuaiba carbonate reservoir, which aims to demonstrate the feasibility of surfactant injection through detailed lab work followed by field applications. The Upper Shuaiba carbonate formation presents challenging conditions, characterized by high formation water salinity (~180,000 ppm), tight matrix (permeability of 1-15md) with fractures, and reservoir temperature of ~85°C.

In light of the continuous gradual increase of the water production from the field and expected ultimate recovery factor from the ongoing water flood, an extensive work program has been conducted since 2010 to identify the most suitable and effective EOR technique for the Shuaiba carbonate reservoirs. This process began with lab tests to analyze fluid behavior and core flooding experiments for the selected surfactant. Subsequently, field trials were conducted including huff-and-puff test in a vertical well and then continuous injection in two horizontal water injectors for three months. Detailed results from both lab experiments and the first field trials were documented in a prior paper (Al-Sinani et al., 2016).

This paper discusses the result of the field trial conducted in March 2018, which yielded 40 kbbl of oil gain and a 5-10% reduction in water cut during two years. The field trial took place in a mature water flood pattern with 100-meter well spacing, involving a horizontal water injector and two adjacent oil producers. Over three months, surfactant was successfully injected at a concentration of 1.3%. A robust monitoring plan, including well-testing, surface wellhead sampling, and time-lapse production logging, was carried out during and after the chemical injection.

The field trial result, along with the previous reported initial lab and field data, paved the way for the expansion of surfactant EOR into a full block including 15 water injectors and 35 oil producers. The full-block surfactant flood was planned in two phases from 2023 to 2026. Phase I surfactant injection took place in a small sector with 3 injectors and 5 producers from Jan to Apr in 2023 to validate oil gain based on the dynamic model results, re-evaluate project economics, and assess any surface and subsurface potential risks for Phase II.

In this paper, an overview of the Phase I execution including challenges will be highlighted. Obvious water cut reduction was seen within 3 months after the surfactant injection, and then the water cut went back to the historical level. A clear oil gain above water-flood base line has been observed from the pattern of Phase I within 15 months but was assessed as below initial forecast. This paper will discuss the possible reason for the lower than the expected oil gain and the future plan of the surfactant flooding in Daleel fields.
Increasing Oil Recovery Efficiency with Lean Principles: A Case Study in Aquifer Pump-Off (apo) Meter Improvement in Oman
Authors: Mahrezi D. , Fazari M. , Naabi N. , Hinai A.
Published: November 2024

APO (Aquifer pump off) wells are used for water production to maintain the reservoir water that used for steam production. The availability of these wells is affecting directly to the steam production and the reservoir acceptance of the steam injection. All this process cycle is used for enhancing oil recovery (EOR) by steam injection to reduce the oil Viscosity. Moreover, there are different KPIs to ensure that this process more efficient like, Aquifer pump off well production KPI, water treatment plant KPI, steam production KPI and steam injection KPI.

Magnetic flow meter is used to measure the water production for each Aquifer pump off well to produce within the process limit and the daily production target.

The journey started to study the suitable flowmeter type selection, correct verification method, internal inspection to ensure that production operator and filed programmer are getting the correct measurements live and on time.
Smart Hull Monitoring Solution For Jackup Units
Authors: Surendran Sujesh , Wang Tiger
Published: November 2024

The maritime industry has long faced the challenge of ensuring the structural integrity of vessels operating in harsh environmental conditions. Jack-up units, which are critical for offshore drilling and construction, require rigorous maintenance and monitoring due to their unique operational environment and structural complexities. The existing monitoring systems often fall short in providing actionable insights and recommendations for repair and maintenance, leading to recurring issues and increased operational risks. This paper presents a detailed examination of the Structural Health Monitoring System (SHMS) developed by Xiamen Sango Marine Technology Co., Ltd and Capital Ship Solutions (CSS). The SHMS aims to address these gaps by integrating comprehensive hull structure monitoring, yielding assessments, and deformation evaluations specifically tailored for jack-up rigs and platform structure.
Integration of Pressure-Derived Permeability with Static Reservoir Modeling: An Adaptive Approach for Improved History matching
Authors: Tripathi Neelesh R. , Saad Ahmed M. , Shalaby Sharif M.
Published: November 2024

Integration of pressure-derived permeability thickness with other geological data plays a crucial role in estimating the apparent reservoir permeability, which is a key reservoir property required for reliable reservoir characterization as it governs fluid flow and greatly impacts decisions related to production, field development, and reservoir management. The geological model provides a representation of the subsurface reservoir, capturing the spatial distribution of lithology, porosity, permeability, and other geological properties. Analysis of pressure data provides valuable information on well condition, reservoir extent, and dynamic reservoir parameters. Integrating such data with the geological model is an enabler to better quantify and manage the uncertainty in the spatial 3D distribution of permeability away from well control.

This work proposes a methodology to build high-resolution geological models based on the available dynamic data, seismic data, and geologic interpretations. The geological model typically provides an estimate of intrinsic permeability, which depends on the pore structure and flow path within the rock matrix. In general, the 3D-modeled permeability honors core and well log data, which are sparse and at a significantly smaller scale compared to the reservoir size. Hence, it may not be representative of permeability farther away from the well locations, especially in carbonate reservoirs, which may have varying complex pore structures and widely varying porosity/permeability relationships. The proposed workflow incorporates pressure-derived permeability into the reservoir model prior to the matching.

The workflow iteratively modifies the permeability in the geological model to minimize the mismatch between the simulated and measured production and pressure data. The proposed workflow has been implemented on a synthetic reservoir model, showing an overall improvement in the history match quality for both deviated and vertical wells. The process enabled a more robust permeability adjustment and improved the integrated asset team's efficiency through guided iterations.

The presented methodology offers a systematic and adaptive approach leveraging pressure-derived permeability and geological information to adjust the intrinsic permeability. By integrating these datasets and enabling multi-disciplinary collaboration, the accuracy of the reservoir model is enhanced, leading to more reliable reservoir characterization, improved decision-making in reservoir management, and optimization of IRS resources and time.
Enhanced Vuggy Zone Recognition Through Merging AI-based Borehole Image Interpretation
Authors: Petrov A. , Egorov D. , Belayouni N. , Tamaazousti Y.
Published: November 2024

Recognizing vuggy zones in borehole imaging (BHI) presents significant challenges due to the intricate and heterogeneous nature of these geological features (fig.1). Traditional methods, which often rely heavily on subjective visual interpretation of BHI data, are prone to variability and inconsistencies. These limitations contribute to substantial uncertainties in geological modeling and subsurface characterization, impacting the reliability of reservoir assessments.

The integration of AI-driven interpretation techniques offers a transformative solution to these challenges. By leveraging advanced machine learning algorithms, we can systematically analyze high-resolution borehole imaging data, reducing the dependence on human interpretation and minimizing associated biases. These algorithms are capable of identifying subtle patterns and features indicative of vuggy zones that traditional methods might overlook or misinterpret.

AI enhances the precision and consistency of vuggy zone identification, facilitating a more rigorous quantification of uncertainties inherent in these interpretations. This not only strengthens the robustness of geological models but also leads to more accurate predictions of reservoir properties and behavior. As a result, critical decision-making processes in subsurface characterization—such as reservoir management, well placement, and production forecasting—are significantly improved. This optimization of resource extraction efforts directly translates to reduced operational risks and enhanced economic outcomes.
PDO Pumps Energy Saving Journey
Authors: Al Hashimy Z. , Al Shukaili A. , Al Araimi M. , Willcox P. , Al Zawaidi I.
Published: November 2024

Petroleum Development Oman (PDO) purpose is to "build a sustainable and low-carbon future to maximise value for Oman". In line with this, the Rotating Equipment function has begun an energy efficiency journey by saving electrical power, reducing fuel gas consumption and cutting Greenhouse Gas (GHG) emissions, thereby also reducing Operating expenditure (OPEX) and its Unit Operating Cost (UOC).

A study has been initiated to look for energy efficiency opportunities by checking the running parameters of equipment units compared to their design as some pieces of equipment are running inefficiently due to overdesign or process conditions have changed over the years. The study usually starts by analysing the losses from a machine using main parameters such as suction/ discharge pressure, flow, speed and efficiency. A forecast reaching 20 years ahead is included in this study to understand the required hydraulics in the future and the design required to accommodate the peak condition. After then verifying a technical proposal, a study is then undertaken to explore the best approach to re-rate the equipment by trimming the impellers, change or de-stage them, in the case of multistage machines.

To date, PDO has completed the re-rating of 20 pumps with a total yearly power saving of 7.4 MW, which is the equivalent of US$2.9 million per year and 38,000 Tonnes of CO2 emissions annually, the equivalent of US$2.3 million per year. Currently, we are executing five projects and four feasibility studies to verify the extent of such attractive savings. In this paper we will showcase the journey of PDO's pumps energy savings.
Optimization of Casing Running Operations Using Automated TRS Technology
Authors: Hasler D. , Nguyen D.
Published: November 2024

In integrated casing running operations, the common contributors to flat time have been identified in the processes of aligning the casing running tool (CRT) tool into the joint, making up the connection, and intermittently filling up the casing string with drilling fluid. The connection time from slips to slips can vary as a result of factors that include the driller's level of expertise, visibility of the CRT position, and the effectiveness of crew communication, but overall improvements have been achieved over the years through the use of mechanized tools that reduce makeup time. Over the years, the use of tools such as power tongs for expediting casing connections has evolved into a more advanced process that involves tying in a CRT to the top drive in parallel to the rig's operating system to automate casing string makeup.

Recognizing the areas where improvements were attainable through automation, Nabors Drilling Solutions developed integrated tubular running services (TRS) to seamlessly handle and install tubulars and execute the process efficiently and reliability. The mechanized casing running system uses a top drive to rotate and reciprocate casing strings to total depth and leverages cutting-edge technology to streamline and automate the casing installation process for optimal wellbore integrity.

Implementing an engineered solution that includes both mechanical and software enhancements to automate casing running helps bridge the gap between inexperienced and experienced drillers by executing steps of the casing running sequence without human involvement. This innovative approach delivers consistent casing connection times while eliminating the possibility of human error that can lead to misapplied torque, introduce the risk of joint damage to the casing or result in cross-threading incidents.

A field test conducted in West Texas demonstrated a 67% reduction in the time required for CRT alignment compared to the conventional method for running casing, proving that automation is both feasible and practical. This paper highlights the advancements achieved through automation and optimized processes in integrated casing running for improved performance and increased safety.
Optimizing Tubing Size for Effective Sand Transportation and Extended Life of ESP-Assisted Wells
Authors: Agarwal J. R. , Lenka M. , Ebrahim A. S. , Bourisli R. I. , Santhanagopalan S. , AI-Majdli A. , Sumarto B. A. , Mohammad R. , AlAmer R. , Vedapuri D.
Published: November 2024

The study examines the minimum sand transport velocities needed for different tube sizes to prevent ESP (Electric Submersible Pump) tripping and extend the lifespan of inclined wells. The methodology involves observing sand bed conditions at varying superficial liquid velocities (VSL) across different tube sizes, utilizing both OLGA modeling and experimental analysis. The studies were carried out using pure water, and the OLGA models were created based on completion schematics and fluid composition data. Sand transport phenomena were simulated with OLGA's sophisticated particle flow module, while Multiflash was used for thermodynamic fluid modeling. According to our findings, the minimum sand transport velocity is consistent across different tube diameters, specifically 2″, 2.5″, and 3″ tubes. Based on these findings, we suggest an optimal tube size of 2″ or smaller, considering a maximum flow rate of 1000 stbpd. Experimental measurements indicated a minimum sand transport velocity of 0.48 m/s, while steady-state models pointed to a requirement of around 0.4 m/s to prevent sand accumulation. Interestingly, our experiments revealed that OLGA underestimated the minimum sand transport velocity required to prevent sand accumulation. The study provides new insights into the dynamics of sand transport, emphasizing in particular the limitations of OLGA for coarser particles and inclined wells. The findings give engineers practical guidance for maximizing production and maintaining well integrity.
Leveraging Synergies from Oil and Gas Project Knowledge to Advance Hydrogen Project for South-East Asia's 1st Multifuel Refueling Station
Authors: Rashyid Mohd Amin Nor
Published: November 2024

This paper is written to share the best practice of capitalizing synergies from O&G project knowledge to foster and advance hydrogen projects through Darul Hana Hydrogen Refueling Station (HRS) Project. To keep pace with demand of energy transition towards sustainable sources, hydrogen has emerged as a versatile and clean alternative with vast potential across multiple sectors. This paper intends to demonstrate how knowledge and technologies captured within O&G sector by PETRONAS Project Team were leveraged, repurposed, and retrofitted to support smooth execution of hydrogen projects.

Upon its completion, Darul Hana HRS Project by PETRONAS will become the first Multi-Fuel Refueling Station in South-East Asia. The success of this project is clear evidence that collective synergies of knowledge and experience of the O&G industry and hydrogen are crucial factors in realizing the full potential of hydrogen as a key enabler of a sustainable energy transition.

By embracing the convergence of expertise from both sectors, stakeholders can unlock new opportunities and address complex challenges in advancing hydrogen technologies at much bigger capacity on a global scale.
A Biodegradable Anti-Agglomerant Leads to a Low-Viscosity, Transportable Hydrate Slurry in a Multiphase Flow
Authors: Hu S. , Nelson A. , Fan C.
Published: November 2024

This work presents a biodegradable anti-agglomerant (AA) that is not only environmentally acceptable (Yellow) but also able to deliver a low-viscosity, transportable hydrate slurry with dispersible hydrate particles. A high-pressure rocking cell was used to test the performance of the developed AA candidates. Compared to the nonbiodegradable AA candidates, the selected AA performed better in various scenarios. All tests included a gas mixture that forms Structure II hydrates, a clean oil sample from the field, a subcooling of approximately 15 °C, and steady-state and shut-in and restart scenarios. The AA demonstrated the ability to treat hydrate problems under water cuts of up to 50 vol% and salinities of up to 6 wt%. In addition, the product passed a series of stability and flowability tests under various temperatures and pressures. These tests aimed to qualify a chemical product for injection via a subsea umbilical line without causing any blockages or damage.
Accelerating Wellbore Domain Interpretations Using Generative AI and Knowledge Graphs
Authors: Rekik Karim , Zhou Zengpeng , Ouzineb Sohaib , Zened Olfa , Amour Myriam
Published: November 2024

The petroleum industry relies heavily on accurate wellbore domain interpretation for effective resource management and extraction. Traditionally, this process involves numerous complex and time-consuming steps, such as data normalization, unit harmonization, and depth matching, often requiring extensive expertise. This paper presents an AI-powered solution designed to automate the creation of wellbore interpretation workflows, significantly enhancing efficiency and accuracy.

Our solution integrates generative AI, large language models (LLM), and graph-based methods to streamline the workflow creation process. The methodology involves three key steps: knowledge gathering from extensive documentation, automatic workflow generation through natural language interactions with a chatbot, and execution of quality-checked workflows. By leveraging a comprehensive knowledge base built from training manuals, API references, and historical interpretation data, our system can intelligently suggest optimal workflows in response to user queries.

Tests conducted using data from the Groningen field demonstrate the effectiveness of this approach. The time required to create a lithology computation workflow was reduced from the typical 15-30 minutes to just 10 seconds using our AI-powered workflow advisor. This represents a time reduction factor of up to 180 for junior petrophysicists. The knowledge graph employed in this process showed a 93% accuracy in building workflows, ensuring reliability and precision.

This innovative solution not only saves time but also minimizes errors, enabling non-experts to perform accurate data interpretation. The AI-powered workflow advisor represents a significant advancement in the automation of wellbore interpretation tasks, demonstrating the potential of machine learning to revolutionize the petroleum industry. By automating tedious and complex processes, our solution contributes to faster, more reliable wellbore analysis, ultimately improving decision-making and operational efficiency.
Maximizing Drilling Performance with Real-Time Digital Twins, Automated Advisory and Cutting-Edge Digital Platform
Authors: AitAli R. , Arevalo P. , Grymalyuk S. , Bourechak T. , Belgacem H. , Chergui H. , Sellam A. , Zarra A. F. , Sardella C. , Tahri A. , Merah F. , Fattouch A. , Pietragallo L. , Venerio C. , Tricasi V. , Salinas A.
Published: November 2024

The integration of real-time digital twins with offset wells performance analysis is critical for optimizing drilling operations and reducing non-productive time (NPT) and invisible lost time (ILT). This paper presents a digital platform that combines innovative product solutions, communication protocols, and preventive measures to mitigate costly downhole issues and enhance drilling efficiencies. The digital twin of the wellbore uses physics-based models to continuously monitor torque and drag indicators, transient pressure regimes, and drilled cuttings bed concentration, enabling early identification of hole condition issues through automated alarms and mitigation advice.

The drilling automation application optimizes drilling parameters by combining rate of penetration (ROP) and vibration data (VSS) to produce parameters that ensure tool integrity and reduce maintenance costs. A cloud-based platform integrates real-time software and has over 85 solution-based applications to monitor operations, provide advanced analytics, and proactive workflows using hazard detection algorithms.

This paper presents case studies demonstrating the benefits of integrating digital twins with offset wells performance analysis. Results show significant improvements in drilling performance, including a 17% reduction in weight-to-weight (W2W) time, 22% increase in ROP, and 31% reduction in overall well drilling time compared to P10 and 37% compared to P50. The integration of digital twins and intelligent detection of trend deviations during drilling operations contributes to a robust drilling optimization and hazard prevention workflow, improving the accuracy of resulting models and performance trends. The combination of these well monitoring mechanisms contributes to the overall reduction of well construction time and cost.
Construction Barge Performance Management - Artificial Intelligence Based Software
Authors: Siddiqui Shahid Ahmed
Published: November 2024

In the case of Offshore Construction through barges and construction works in general, whatever is the contracting strategy, one thing is common to all, which is human performance, this drives efficiency and makes the basis of all the development of Cost.

There is a significant potential to enhance human efficiency. The project "CONSTRUCTION BARGE PERFORMANCE MANAGEMENT - ARTIFICIAL INTELLIGENCE BASED SOFTWARE" is development and implementation of AI/ML based software that analyzes components of human efficiency to identify training needs and establish targets for low performers to elevate their efficiency, thereby improving overall workforce efficiency and output. Huge data related to work requirements and workforce attributes is utilized by the software to suggest rational deployment of every worker at the most appropriate work location.

In complex offshore construction, the gap between average and high productivity among workers is often due to lack of an effective work front. The software is designed to streamline work fronts, optimizing sequencing to shorten critical paths, attain effective work front and then balance the workforce with effective work front so derived. It analyzes non-productive time (NPT), suggests improvements and automates and eventually makes management autonomous. This approach aims to elevate productivity, pushing human efficiency and output to new heights.

One of the major tools of this software is the point-based system to evaluate performance and skills. It generates points based on the skills and performance of the workforce to determine compensation. This system develops multi-skilled workers. The system identifies training needs and suggests appropriate courses. It also helps in identifying candidates for promotion by evaluating their performance and multiple skills, suggesting additional training or qualifications required for the next step in their career. Once the system is operated for one to two years it will have enough data to ensure fair compensation, identify training needs, fair promotions, improved behavioral based safety and effective counseling of workforce. Another important module of the software is deft handling of behavioral based safety data which aims to improve BBS and workers’ wellbeing. IoT cameras/sensors integrated with the system will be used in reducing HSE incidents and tracking and improving workers’ well-being.
Significance of Multiple CO2 Plume Management and Pressure Interactions during Geological CO2 Storage Operation based on the Canadian Aquistore CCS Project
Authors: Shokri Alireza Rangriz , Kamali Marziyeh , Movahedzadeh Zeinab , Narayanasamy Ranjith , Nickel Erik , Chalaturnyk Rick
Published: November 2024

Carbon capture and storage is a viable short-to-medium term strategy to reduce emissions and support a low-carbon economy. However, geological storage of CO2 in Giga-tonne scales requires many injection wells, leading to complex integration of multiple CO2 plumes. Based on a decade of CO2 storage operation at the Canadian Aquistore CCS project, this paper explores the potential CO2 plume commingling and subsequent pressure buildup during clustering of large-scale geological CO2 storage operations.

To assess the significance of multi-well CO2 injection on storage capacity and injectivity, we constructed a static regional model that characterizes the storage and sealing formations. To understand the potential repositories, traps, and sealing mechanisms, the regional model considered 23 wells in the region to build a robust structural framework, utilizing the geological information, interpreted 3D horizons, well tops, and reservoir properties populated based on 3D seismic porosity volumes. A section of the regional model was selected to match the Aquistore injection history and to evaluate the impact of multiple injection wells and CO2 plumes within the area of interest.

The dynamic reservoir model was calibrated to the injection data up to year 2020. The history matched simulation model was then used to predict the injection history for the next three years, until 2023. The simulation forecast was compared against actual field data in the same timeframe to assure the simulation model is capable to predict the CO2 storage operation. Different CO2 injection scenarios were employed for dynamic reservoir simulations for the next 20 years with varying injection rates. The injection pressure was constrained to a value below 95% of fracturing pressure determined from an earlier mini-frac test. We monitored the commingling of the CO2 plumes from different wells under a range of reservoir and operational uncertainties. To address the regulatory concerns, the degree of pressure interference between each adjacent CO2 injectors was used to observe the influence of each CO2 injection well before and after the integration of individual CO2 plumes. Lastly, enhanced operational and adaptive pressure management strategies for the CO2 injection wells were discussed to mitigate the potential subsurface risks, maintaining the injection rates and storage capacities within safe limits.

Utilizing eight years of actual field data from the Aquistore CO2 injection site is an asset to better understand the CO2 plume evolution and the associated regional pressure buildup in saline aquifers. The modeling results provide valuable insights into the safe and long-term management of multiple CO2 plumes, and showcase the importance of clustering of large-scale geological CO2 storage projects for operators and regulators.