The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions. This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.
Zhu, Jun (Vertechs Energy Group) | Zhang, Wei (Vertechs Energy Group) | Zeng, Qijun (Vertechs Energy Group) | Liu, Zhenxing (Vertechs Energy Group) | Liu, Jiayi (PetroChina Southwest Oil & Gas Field Company) | Liu, Junchen (PetroChina Southwest Oil & Gas Field Company) | Zhang, Fengxia (PetroChina Southwest Oil & Gas Field Company) | He, Yu (PetroChina Southwest Oil & Gas Field Company) | Xia, Ruochen (PetroChina Southwest Oil & Gas Field Company)
Abstract In the past decade, the operators and service companies are seeking an integration solution which combines engineering and geology. Since our drilling wells are becoming much more challenging than ever before, it requires the office engineer not only understanding well construction knowledge but also need learn more about geology to help them address the unexpected scenarios may happen to the wells. Then a novel solution should be provided to help engineers understanding their wells better and easier in engineering and geology aspects. The digital twin technology is used to generate a suppositional subsurface world which contains downhole schematic and nearby formation characteristics. This world is described in 3D modelling engineers could read all the information they need after dealt with a unique algorithm engine. In this digital twin subsurface world, the engineering information like well trajectory, casing program, BHA (bottom hole assembly) status, are combined with geology data like formation lithology, layer distribution and coring samples. Both drilling or completion engineers and geologist could get an intuitive awareness of current downhole scenarios and discuss in a more efficient way. The system has been deployed in a major operator in China this year and received lot of valuable feedback from end user. First of all, the system brings solid benefits to operator's supervisors and engineers to help them relate the engineering challenges with according geology information, in this way the judgement and decision are made more reliable and efficiently, also the solution or proposal could be provided more targeted and available. Beyond, the geology information from nearby wells in digital twin modelling could also provide an intuitional navigation or guidance to under-constructed wells avoid any possible tough layers via adjusting drilling parameters. This digital twin system breaks the barrier between well construction engineers and geologists, revealing a fictive downhole world which is based on the knowledge and insight of our industry, providing the engineers necessary information to support their judgement and assumption at very first time when they meet downhole problems. For example, drilling engineers would pay extra attention to control the ROP (rate of penetration) while drilling ahead to fault layer at the first time it is displayed in digital twin system, which prevent potential downhole accident and avoid related NPT (non-production time). The integration of engineering and geology is a must-do task for operators and service companies to improve their performance and reduce downhole risks. Also, it provides an interdisciplinary information to end user for their better awareness and understanding of their downhole asset. Not only help to avoid some possible downhole risks but also benefit on preventing damage reservoir by optimizing the well construction parameters.
AlHomaid, Fahad (Saudi Aramco) | Jain, Arun (Saudi Aramco) | Gunther, R. Ken (Saudi Aramco) | Bugubaia, Abdulrahman (Saudi Aramco) | Ramabhoopal, K. (Emerson) | Dickerson, Paul (Emerson)
Abstract As part of the Saudi Aramco Digitalization initiative, SMART (Sales Gas Monitoring, Analysis and Reporting Tool), a digital twin of the Master Gas System (MGS) pipeline network was deployed to provide operational and maintenance insights, meet delivery agreements, predict survival times, and load forecasting. This paper shares the process of building the virtual model, tuning, validation, testing and results. In addition, several major challenges faced during the deployment and resolutions shall be discussed.
Abstract Accurate understanding of the physics of the wellbore and knowledge of production rates are essential as they serve as a key input to modelling and therefore affect results and the decisions made based on those. This paper presents a methodology to create a physical representation of the wellbore and to compute production rates from monitoring parameters utilizing physics-rooted models. When connected to real-time measurements, the process enables continuous production surveillance and integration into a digital oilfield solution. The approach is validated against data from literature and a real North Sea offshore field. This work consists of an integrated methodology using a mechanistic approach to replicate the physics of the wellbore. The process utilizes transient heat transfer calculation between a deviated wellbore and formation. Black oil models are used to determine the properties of the produced fluids, which may comprise mixtures of gas/oil/water. Basic fluid properties and static information including wellbore design are required for the initial model setup. The dynamic input comprises choke downstream pressure, choke valve setting, pressure and temperature at wellhead and downhole. Dynamic data may come from either SCADA (supervisory control and data acquisition) for near real-time calculation, or manual readings. The methodology is validated with two quality data points from various fields used by other authors such as (Hasan & Kabir, Fluid Flow and Heat Transfer in Wellbores, 2002), including an onshore and an offshore well. Moreover, the process is also tested against the publicly available historical dataset of the Norwegian offshore field Volve, which was in production from 2008 to 2016. This allows simulation of daily production rates throughout entire well life cycles. The simulation of the real field cases achieves an average error MAPE (mean absolute percentage error) of 11.75 % for the liquid rate. The novelty of this approach is the ability to run a digital twin of a wellbore based on data that is already acquired as part of standard well monitoring operations. Using the process as a VFM (virtual flow meter) can increase accuracy of production allocation and quality control (QC) physical MPFM (multiphase flowmeter). Having such a model-based approach offers significant potential for cost savings, for instance reducing OPEX (operating expenditure) by stretching physical metering cycles and lowering CAPEX (capital expenditures) by saving metering infrastructure.
Rincon, Jose (Shell Exploration & Production Company) | Greenlee, Ivan (Shell Exploration & Production Company) | Hamerski, Russell (Shell Exploration & Production Company) | Rayborn, Joseph (Shell Exploration & Production Company) | Schexnayder, Jacob (Shell Exploration & Production Company) | Tran, Elena (Shell Information Technology International)
Abstract In 2009, the Vito field was discovered in more than 4,000 ft of water approximately 150 miles offshore from New Orleans, Louisiana. The project produces from reservoirs nearly 30,000 feet below sea level. This paper outlines the approach taken to develop the Vito facilities operating model (Operating Model) and prepare for start-up and ramp-up. This paper is part of a Vito Project series at OTC 2023, and the other papers are listed in the references. In 2015 the project faced significant financial hurdles and went through a refresh of the concept design to reduce cost and simplify while maintaining safety as a top priority. This re-design resulted in reduced redundancy and operational flexibility including reducing host personnel on board (POB) capacity to 60 personnel. To prepare to start-up and operate the facility with this simplified design, a proactive approach was adopted across four key areas: Operating Model, maintenance strategy, digital building blocks, and start-up ramp-up (SURU) planning. An Operating Model was developed to enable efficient execution of maintenance and operations activities with the reduced POB. This model leverages multiskilling of onsite personnel and enabling digital technologies. The limited POB necessitated development of a maintenance strategy that is both lean and comprehensive with high utilization of the base crew. With local performance standards as the foundation of the strategy, a Reliability Centered Maintenance (RCM) study was performed to validate and further optimize the defined strategy, which relies heavily on asst specific maintenance tasks and onshore sparing due to minimal redundancy and space on host for spares. Digital Twin technology has been leveraged to create a virtual mirror image of the Vito facility that is a central repository of equipment data and documentation. This enables virtual planning of work which reduces POB needs by providing the ability to perform walkdowns, take measurements and identify access issues without being on site. Augmented reality technology supplements this by streaming the viewpoint of offshore staff directly to onshore teams to perform troubleshooting, diagnose issues, and inspect without requiring physical presence. To manage well start-up and to optimize long term recovery, a start-up ramp-up model was implemented with a focus on proactive actions to optimize start-up. This led to long term lease of onshore caverns to eliminate requirements for temporary unloading equipment during start-up. A phased commissioning and start-up plan was developed with gas buyback brought on ahead of well start-up to pressure up vessels and commission compressors. Additionally, a real time simulator was built to test procedures, train personnel and walkthrough the start-up plan.
Abstract Thermal Insight, visualized in Figure 1, establishes a new frontier for operators to increase reliability and uptime of SPS systems by avoiding cold restarts. The Digital Twin is a live diagnosis tool that provides critical information about the actual cool down time of critical locations in the Subsea Production System (SPS), in the event of an unplanned shut down. Based on verified Computational Fluid Dynamics (CFD) tools, Thermal Insight adopts a holistic full field view and feeds back the actual cool down time in predetermined stringent locations to the operator. This facilitates well informed decision making, opposed to immediate shut down and cold restart following conservative cool down requirements and philosophies. The inner workings of the Digital Twin are a Reduced Order Model (ROM) set of differential equations based on a-priori CFD simulations which reveals the cool down time in critical locations, and not only in the location of installed hardware (PTT sensors). The CFD results are used to correlate the real time temperature readings from the Pressure/Temperature Transducer (PTT) to cool down times in the most stringent parts of the SPS, limiting the number of cold restarts to when it is really needed. This paper will outline the CFD model verification work along with the Digital Twin deployed onto an example scenario based on actual field data from an operator.
Abstract A digital twin is proposed to quantify the loads and mitigate the risk of rupture during the recovery operation of a flexible riser with severe structural weakness (30% of broken wires in the outer tensile armor layer). The allowable axial tension is compared with the loads expected during operation to establish safe operating limits. The digital twin is divided into a local model and a global model. The local analysis uses a finite element model to investigate how stresses on tensile armor wires change when the number of broken wires increases. It considers all pipe layers and two configurations, 1) straight and 2) with a curvature expected during the recovery operation. The FPSO motion performance is calibrated by offshore field measurements and improved RAOs for the global analysis are provided based on the measured motions. Finally, the allowable axial load from the local analysis is compared with the expected loads from the global analysis. The local model is simulated stepwise with 0 - 28 broken wires and indicated a reduction of 83% in allowable axial load with 25 broken wires. The results were combined with global simulations considering different sea states to provide insights about how close the loads might be to the allowable limit during operation and what loads the riser was subjected to since the last broken wires were detected. This analysis allowed the identification of the most critical steps in operation and helped to design the procedure to achieve ALARP risks. Finally, operating limits were discussed to keep the estimated load inside the established limits.
Zhang, Shanli (Technology Centre for Offshore and Marine) | Zhang, Chi (Technology Centre for Offshore and Marine) | Santo, Harrif (Technology Centre for Offshore and Marine) | Cai, Minbo (Technology Centre for Offshore and Marine) | Si, Michael Boon Ing (Technology Centre for Offshore and Marine) | Cao, Jixing (National University of Singapore) | Quek, Ser Tong (National University of Singapore)
Abstract A development of physics-based digital twinning of a generic jack-up platform is presented in this paper. Due to lack of field measurement data, a generic large-scale jack-up model was designed, fabricated and tested in TCOMS ocean basin at 1:30 scale under different configurations, with the objective to provide high-quality datasets to validate the proposed digital twin methodologies. The framework and the performance of the digital twin are demonstrated using a realistic and representative basin-scale model as a proof-of-concept. Fundamental to any physics-based digital twins is the establishment of numerical models capable of reproducing consistent behaviors and responses of the physical assets. For this digital twin development, a full order model (FOM) and a reduced order model (ROM) are established. In view of uncertainties associated with the physical asset and numerical modelling, e.g., foundation fixities, leg stiffness, leg-hull connection stiffness and hydrodynamic coefficients, model updating or system identification is performed using the ROM to identify the parameters with relatively large uncertainties. A mapping between the parameters and the associated responses of the FOM and the ROM is subsequently established. After the model updating is completed with the identified parameters, good agreement in terms of the structural responses between the model test and numerical results can be achieved. Both the FOM and ROM are able to reproduce structural responses with good accuracy when compared to physical measurements. The ROM, being a linear structural model based on modal responses, is unable to account for larger non-linear effects due to spudcan fixities, if any. Nevertheless, the ROM is suitable for fatigue evaluation considering fast computational speed and validity of the piecewise linear constraints as assumed for the foundation. The FOM, being less computationally efficient, is suitable for strength evaluation and able to account for any non-linear structural behaviors. The results of boundary displacements from the global dynamic response analysis can be mapped to a detailed local joint model to derive the hotspots stress for a more accurate fatigue evaluation. The digital twin framework for fatigue and strength evaluations based on measured wave loading is demonstrated for a better structural integrity management. As an emerging technology, digital twin will provide visibility of structural health condition to facilitate the transition from preventive to predictive and reliability-centered maintenance strategies. Although the digital twin framework presented in the paper makes use of a representative jack-up at model-scale, the proposed methodology can be potentially applied to full-scale operating jack-ups.
Amin, Muhammad Rizky (ADNOC Onshore) | Baruno, Agung (ADNOC Onshore) | AitAli, Rachid (Baker Hughes) | De Vreugd, Joost (Baker Hughes) | Forshaw, Matthew (Baker Hughes) | Mahmoud, Mohamed Yehia (Baker Hughes)
Abstract Despite the market up-cycle efficient use of CAPEX for upstream well construction projects remain a key topic for E&Ps. Therefore, non-productive-time and invisible-lost-time (NPT & ILT) reduction continue to be paramount. Wells of such complexity require the introduction of new drilling automation technology to meet the challenge associated with inadequate hole cleaning, vibrations and connections practices resulting of the implementation of the i-Tral real-time monitoring used to mimic manual torque and drag parameters through artificial intelligence and machine learning allowing extrapolation, alarming and therefore early warning onset of friction and stuck-pipe, continuously supply required flowrate and visualize cuttings concentration along the wellbore and real-time multi parameters optimization for drilling dysfunction and penetration rate increasing tool longevity, trips for tool failure hence reduction in section delivery time. This paper details the features of the i-Trak torque and drag, Dynamic management and hole cleaning models capable of drive efficiencies to execute effectively and consistently each operations while meeting the goals set in the AFE. The technology was first deployed in Abu Dhabi (UAE) in xx Field, a producing conventional oil field located onshore and operated by ADNOC ONSHORE. In total 27083ft was achieved combining Well-I and Well-II where the objectives behind these 2 horizontals wells were to maximize reservoir contact, improve productivity and accelerate delivery while minimize construction costs by implementing mitigations in addressing concerns which helped in streamlining the drilling operations while reducing any potential risks. These wells were drilled in collaboration by ADNOC and Baker Hughes and considered to be the first maximum reservoir contact (MRC) project in this area. For primary mitigation, the output of cuttings mass existing in the wellbore (static and mobile) can give a clear indication of the amount of cuttings downhole which corresponds to 50% of the criteria analysis for an inclination range. Secondary mitigation is from the high-resolution torque and drag samples plotted real-time and compared against theoretical pre-well plan broomstick model for different frictions factors measuring the deviations to concisely convey the end users to apply the required procedures to alleviate risks of well constructions operations. Finally, the integration of dynamic management provides a unified user interface which combines ROP and VSS measurements in a single place to minimize maintenance and repair costs while driving higher ROP's.
A digital twin is essentially a digital representation of a physical system such as a well, pump, compressor, or a series of connected items. Sometimes, machine-learning algorithms can assist in analyzing large amounts of data within domains such as preventive maintenance. The value proposition of a digital twin is to have a complete overview of all fluid streams in the production and injection network to enable automation of production capacity planning subject to current and future constraints. The digital solution must be versatile, maintainable, accurate, and with a quick turnaround time to address dynamic changes in market demand as well as the supply side down to the individual wells. Integrated asset models (IAM) have been around for the past two decades or so.