Yonebayashi, Hideharu (Inpex Corporation) | Sasaya, Kazuyo (Inpex Corporation) | Watanabe, Takumi (Inpex Corporation) | Inamura, Takashi (Inpex Corporation) | Kobayashi, Atsushi (Inpex Corporation) | Iwata, Takao (Inpex Corporation)
As a part of laboratory Health, Safety and Environment (HSE) management system, the working environment control is applied to eliminate exposure hazards for workers. This control is a continuous effort in our laboratory as the working environment management system. Volatile organic compounds (VOC)s are ones of very common exposure hazardous factors in petroleum R&D laboratory. To better working environment control, the working environment measurement additionally to the chemical risk assessments is conducted at first to assess the concentration of VOCs in accordance with the guideline of domestic act. The measurement design is optimized on the basis of actual chemical use in the monitoring objective laboratories. The chemical records has been tracked in the chemical inventory management system. The measurement is conducted by two methods to assess both of average and the maximum VOC concentrations in the objective laboratory. Based on the measurement results, the objective laboratories are classified into three ranks. If necessary, counter actions will be taken: for instance, ventilation system improvement as building management, and consideration of substitute. Furthermore, the working record what types of chemical used and how long hours to handle them are linked to the health management system in which the workers who handle solvents must take a semi-annual special medical check. Further potential improvements were debated by adopting the process safety management in laboratory phase, and installing flexible exhaust system.
The working environment management is important for protecting employee's health. The system is not independent and linked to other HSE management systems. Therefore, a well-organized grand design is worthy as total management system which includes each management system for waste, inventory, procurement, building maintenance, and so on. Because this paper discussed a practical example of HSE management system from both of detailed and high level. The discussion should be useful for considering HSE in laboratory.
IJMS is PDO in-house developed solution made to support and enhance road safety in land transport operations. With more than 300 million kilometers driven per year in PDO, in harsh environment, driving is still the highest potential injury risk. IJMS has directly influenced the enhancement of road safety in PDO O&G operations. It became a role model for other O&G companies in Oman. The system enabled achieving 97% of drivers' compliance since its launch in January 2016. It supported PDO efforts in managing the risk in land transport, and to optimize its and contractors drivers' performance and behavior.
In the first chapter, the main theory and definitions were presented including road safety initiatives and best practice globally and in O&G industry that are related to IJMS.
Second chapter describes the system modules and their purposes. IJMS is modular system with integrated modules, which enables further development and enhancement of IJMS.
In the third chapter, main data and results achieved by using IJMS are presented and discussed. In addition, further actions for managing IJMS results are presented in this part.
Finally, the conclusion includes discussion about benefits and further plans of the system development.
Zou, Jian (CNOOC Ltd, Tianjin Branch) | Han, Xiaodong (CNOOC Ltd, Tianjin Branch) | Liu, Yigang (CNOOC Ltd, Tianjin Branch) | Wang, Qiuxia (CNOOC Ltd, Tianjin Branch) | Zhang, Hua (CNOOC Ltd, Tianjin Branch) | Liu, Hao (CNOOC Ltd, Tianjin Branch) | Wang, Hongyu (CNOOC Ltd, Tianjin Branch) | Han, Chao (China University of Petroleum (East China))
Thermal recovery method with horizontal wells has been conducted in Bohai Oilfield for almost ten years. The horizontal section length of the horizontal well is about 300 m. For thermal wells, monitoring the real-time temperature data downhole is of importance for analyzing the temperature distribution and variation rules along the wellbore, and consequently improving the produced degree of the horizontal wells.
Different kinds of high-temperature monitoring technologies are summarized and the high-temperature optical fiber is selected for temperature monitoring of the offshore thermal wells. The steam injection tubing with functions of temperature monitoring is designed by using the optical fiber. In the horizontal section, the optical fiber is installed inside the steam injection tubing, goes outside of the tubing through a Y-joint, and connects to the surface along the annulus. Thus, the optical fiber could monitoring temperature of both the horizontal section and the annulus. Two target well are selected for project design and put into field application during the steam injection process. The monitoring results show that the high-temperature optical fiber works normally for about one month while the steam injection temperature is about 350°C. Besides, with the real-time temperature monitoring, the upward movement of the steam in the annulus is also observed and controlled by adjusting the Nitrogen injection parameters in the annulus.
This is the first time that the optical fiber technology is applied in offshore thermal wells, which would be important for verification of wellbore parameter calculation and analysis of the casing variation when heated during the steam injection process. The successful application of the optical fiber in offshore thermal well would provide a guidance for the subsequent offshore thermal exploitation.
Giunta, Giuseppe (Eni SpA, Development Opertions & Technology) | Nielsen, Keld Lund (Eni SpA, Development Opertions & Technology) | Bernasconi, Giancarlo (Politecnico di Milano) | Bondi, Luca (Politecnico di Milano) | Korubo, Barry (NAOC JV)
Efficiency and safety are primary requirements for oil & gas fluid filled transportation system. However, the complexity of the asset makes it challenging to derive a theoretical framework for managing the control parameters. The current frontier for a real time monitoring exploits the "digital tansformation", i.e. the acquisition and the analysis of large datasets recorded along the whole asset lifecycle, which are used to infer "data driven" relations and to predict the evolution of the asset integrity. This paper presents some results of a research project for the design, implementation and testing of a "machine learning" approach to vibroacoustic data recorded continuously by acquisition units installed every 10-20 km along a pipeline.
In a fluid transportation system, vibroacoustic signals are generated by the flow regulation equipment (i.e. pumping, valves, metering), by the fluid flowing (i.e. turbulence, cavitation, bubbles), by third party interference (i.e. spillage, sabotage, illegal tapping), by internal inspection using PIGs operations), and by natural hazards (i.e. microseismic, subsidence, landslides). The basic principle of machine learning is to "observe", for an appropriate time interval, a series of descriptors, in this stage related to vibroacoustic signals but that can be integrated with other physical data (i.e. temperature, density, viscosity), in order to "learn" their safe range of variation or, when properly fed to a classification procedure, to obtain automatically a discrete set of operational status. The classification criteria are then applied to new data, highlighting the presence of system anomalies.
The paper considers vibroacoustic signals collected at the flow stations of an oil trunkline in Nigeria. The vibroacoustic signals are the static pressure, the acceleration and the pressure transients recorded at the departure and at the arrival terminals. More than one year of data is available. Derived smart indicators are defined, which are directly linked to the asset parameters: for instance, the cross-correlation of the pressure transients at adjacent measuring locations permits to estimate the fluid channel continuity (correlation value), the sound velocity (time of correlation peak), and the sound attenuation (amplitude versus frequency amplitude decay). A portion of the data during normal operation is used for training and tuning a reference model. After that, new data are compared with the model, and anomalies are automatically detected. Two kind of errors are raised: i) sensors; ii) alerts. Sensor errors are referred to missing or corrupted sensors data. Alerts are raised when the measured physical quantities are not coherent with the functional and known service behaviors of the transport system.
The system model is not static over time, and in fact it can be updated by the operators’ feedback, that can tag false alarms and thus, automatically, re-define the set of operational scenarios of the upstream system. The medium-long term construction and update of data driven models is effective for predictive maintenance, automatic anomalies detection, optimization of operational procedures. Moreover, the new policy of data management and the opportunity of gaining awareness by interconnecting the monitoring experience of different assets leverages the introduction of new technologies (cloud, big data), new professional figures (smart data scientist), new operational and business models.
Ng, Mui Ted (Eftech Drilling Solutions) | Lum, Terry (Sabah Shell Petroleum Co. Ltd.) | Yeap, Fabian (Sabah Shell Petroleum Co. Ltd.) | Abdul Talib, Sa'aid Hazley (Eftech Drilling Solutions) | Zainal Abiddin, Mohamad Sukor (Eftech Drilling Solutions) | Hooi, E-Wen (Eftech Drilling Solutions)
As the search for hydrocarbon offshore runs deeper and farther from land, it is best we gear ourselves to embrace what it takes for delivering highly deviated deepwater development wells to push the frontiers of petroleum extraction. This paper discusses the monitoring and optimizations of deepwater wells operations in Malaysia by the Shell Malaysia Exploration and Production Real Time Operation Centre (SMEP RTOC).
The scopes of monitoring and optimizations discussed in this paper include: Hydraulics management in narrow pressure margin drilling, including modelling, optimization, measurement and monitoring of equivalent circulating density (ECD). Engineering support in pre-drill study for Managed Pressure Drilling (MPD) application. Mitigation of drilling vibration in highly deviated wells, especially stick-slip vibration. Hole cleaning modelling, monitoring and optimization. Drilling roadmaps and database are archived by RTOC for future wells reference. Drilling operations performance tracking and benchmarking. 24/7 Top Tension Riser, Hawser and mooring lines tension monitoring.
Hydraulics management in narrow pressure margin drilling, including modelling, optimization, measurement and monitoring of equivalent circulating density (ECD).
Engineering support in pre-drill study for Managed Pressure Drilling (MPD) application.
Mitigation of drilling vibration in highly deviated wells, especially stick-slip vibration.
Hole cleaning modelling, monitoring and optimization.
Drilling roadmaps and database are archived by RTOC for future wells reference.
Drilling operations performance tracking and benchmarking.
24/7 Top Tension Riser, Hawser and mooring lines tension monitoring.
Deepwater drilling operation costs are typically significantly higher than shallow water and land rig operations. This is partly due to higher rig rates in deepwater operations. In this case any reduction in Invisible Lost Time (ILT) and Non-Productive Time (NPT) may result in significant cost reduction. Like a safety net that catches anomalies that slipped the first line of defense, RTOC monitoring has the facilities and trained capabilities to oversee and optimize the operations in totality both during real time and post run. An investment in RTOC services in exchange of more cost efficient and safer operations will be justified in the paper.
Real Time Operation Centre (RTOC) is becoming more indispensable over the years. Hawk-eyeing operations in areas where attention is diluted by other tasks on the rig, could probably be the insurance needed in every future oil and gas drilling. The methods, procedures and processes in well operations will definitely advance with time and further developments and innovations in this subject will be closely followed within the industry.
ENSURING IMPROVED ASSET INTEGRITY by Realtime Corrosion Monitoring and Steam Trap Monitoring
Monitoring of corrosion in a process pipelines have always been of paramount importance to ensure the integrity of plant assets. Similarly, steam traps play a very important role in ensuring steam quality, thereby the integrity of critical assets in the plant. It is common observation that many of the steamtraps become non-functional over a period of time and, more importantly, dangerously go unnoticed. While these are vital in ensuring asset integrity, and need continuous monitoring, it is also a highly demanding and challenging activity in the field, and a dream of many Integrity engineers to perform such asset monitoring remotely, that too, in realtime. Many vendors have been researching on this, and focusing on devising improved technology to ease the burden on such asset monitoring.
This paper intends to touch upon these two aspects of monitoring Asset Integrity – Realtime Corrosion monitoring and Realtime Steam Trap monitoring – as implemented in ADNOC-LNG. The paper shall highlight the importance of digitalization in the Asset Integrity Management - Pipeline Corrosion and Steam Trap monitoring - by means of implementing wireless technology and making the data available in remote workstations in realtime.
Corrosion Monitoring: to move ahead from the conventional Corrosion management to the Wireless Ultrasonic Thickness gauging technology
Steam Trap Monitoring: to remotely monitor the healthiness of Steam Traps with a combination acoustic and temperature instruments.
Corrosion Monitoring: The installation at ADNOC-LNG covers 20 locations in OAG unit (Offshore Associated Gas unit, which has been identified as highly corrosion prone). The procedure involves installing UT sensors at the identified CMLs (Corrosion Monitoring Locations). These are easily installable onto the piping, and each sensor has a measurement footprint of about 1-2 cm2, which is similar to the manual ultrasound inspection method. The technology of ultrasound is well proven and has been used by Integrity engineers for manual inspections. These sensors employ wireless communication, and are powered by battery packs, which last through turnarounds. Doing away with the needs of power and signal cable, simplifies the installation process.
Steam trap monitoring system (20 locations identified in LNG Train-3 Utilities) also employs wireless acoustic and temperature sensors, which are installed on the steam trap piping. From the acoustics and based on the skin temperature measurements, the system identifies the health of the steam traps and determines which are Failed shut, or blow through.
Corrosion Sensors: These UT sensors continue to give the wall thickness measurements of the exactly same point, over a period of time, which can help analyze the early onset of corrosion; unlike the manual UT measurements, where the repeatability and reproducibility of the readings are a challenge, as it is highly unlikely that the consecutive measurements taken after a gap of several months are exactly at the same location, and also it is highly person dependent. The corrosion data is transmitted over wireless, and made available to the desktop workstations of the Integrity Engineers.
Steam traps: Even though steam traps are audited in the plants on a regular basis, such surveys give the performance of the traps for that brief period of time (snapshot information), whereas, continuous steam trap monitoring provides information on the health of the traps on a continuous basis, that too, made available to the desktop workstations of the Operations/Maintenance engineers. It thus enables them to take early decision, and avoid costly failures of equipment/piping etc., and also, the avoid the loss of precious energy by ensuring timely maintenance.
Wireless technology is easily scalable and hence, further additional sensors can be installed across the plant, without much capital outgo. With the thrust on Digitalization, our efforts should be focused on leveraging the benefits of technology.
Gaining access to real-time production and injection data is critical for any upstream oil and gas production operation, as it greatly improves and optimizes production efficiency, while reducing costs. This paper highlights how a well-known oil and gas producer with over 800+ onshore and offshore wells used a digital flow assurance solution to improve real time injection and production knowledge by leveraging digital technologies, expertise, and an open, connected data environment (CDE) to deliver business outcomes and a competitive advantage.
The solution interfaces with other software systems to avoid unnecessary replication of data retrieval. Additionally, direct hardware interfacing to multiple systems for data retrieval and systems control were previously located in separate silos. The oil and gas producer used the flow assurance solution as a central source for managing and monitoring data from all such interfaces across a CDE. The web-enabled, real-time system is used for performance monitoring of well stimulation, treatment design, scale inhibitor squeeze performance, scale monitoring and prediction, water chemistry, monitoring chemical and corrosion, and more.
The organization's goal of using the solution was to ensure that production flowrate targets are achieved and that flow was continuous. The solution provided the user with the tools and information it needed within one central source. For example, the input data was converted into actionable information by a calculation/logic engine, enabling them to identify potential problems more efficiently and provide a faster resolution without affecting production. Summary dashboards provided real-time stimulation information as well as overall performance, which can be viewed from a regional perspective or right down at the well asset level. Scorpion charts allowed users to ascertain which stimulation jobs were the best performers and most cost efficient. Stimulation was performed on a well using varying mixtures of acid solutions to increase or restore production by improving the flow of hydrocarbons.
When a well initially exhibits low permeability, stimulation is used to start production from the reservoir. On operational wells, stimulation is used to further encourage permeability and flow from a well that has slowed down or become under productive. The reporting package within the solution is able to show which jobs are performing badly, and incurring more costs, and identify patterns or relationships that might affect similar assets, job types, geology, and regions within the project more easily for action to be taken.
The solution adopted by the user highlights the importance of flow assurance anywhere in the entire cycle as any breakdown in the process would lead to costly monetary losses to the organization due to the unscheduled downtime and loss of production that could incur. Their digital solution takes the unpredictability out of flow assurance and presents it as an effective and cost-efficient process across the company, with the focus on analysis and proactivity as opposed to data mining and reacting.
As part of the digital transformation in oil and gas industry, well construction move toward new efficient methods using digital twins of the wells. This paper will highlight how the drilling operations are monitored, how a digital twin of the well is utilized and how learnings are implemented for future wells.
A Digital Twin is a digital copy of assets, systems and processes. A Digital Twin in drilling is an exact digital replica of the physical well during the whole drilling life cycle. Its functionality is based on advanced hydraulic and dynamic models processing in real time. By utilizing real-time data from the well, it enables automatic analysis of data and monitoring of the drilling operation and offer early diagnostic messages to detect early signs of problems or incidents.
In the current study various actual operational cases will be presented related to different wells. This includes using digital twin during drilling under challenging circumstances such as conditions when using MPD techniques. Also, various diagnostic messages which gave early signs of problems during running in the hole, pulling out of the hole and drilling will be presented. High restrictions were detected using comparisons of real-time values and transient modelling results. These will be discussed.
Different real cases have been studied. Combining digital RT modelled and real-time measured data in combination with predictive diagnostic messages will improve the decision making and result in less non-productive time and more optimal drilling operations.
Alabi, Oluwarotimi (RAB Microfluidics R&D Company Limited) | Wilson, Robert (RAB Microfluidics R&D Company Limited) | Adegbotolu, Urenna (RAB Microfluidics R&D Company Limited) | Kudehinbu, Surakat (RAB Microfluidics R&D Company Limited) | Bowden, Stephen (University of Aberdeen)
Oil condition monitoring for rotating and reciprocating equipment has typically been laboratory based. A technician or engineer collects a sample of lubricating oil and sends this to a laboratory for chemical analysis. After the laboratory has performed the analysis the results are sent to the engineer to make decisions on the health and/or condition of the machinery. This process can take up to 6 weeks, and consequently analysis may end up being performed only quarterly with little likelihood of critical failures being pre-empted. The slowness of oil condition monitoring analyses performed in laboratories has led engineers to substitute for real-time monitoring methods such as vibration analysis and thermography. Nevertheless, the chemical composition of the lubricating oil remains the gold standard for the diagnosis of machine health. The automation of methods for analysing the chemical composition of lubricating oil in real-time would provide engineers with data on the immediate condition of a particular piece of machinery, allowing the early diagnosis of incipient faults.
In this paper, we present a microfluidic technique that can perform real-time continuous monitoring of the chemical composition of lubricating fluid from rotating and reciprocating equipment. Results from this technique both in laboratory and field environments are comparable to conventional laboratory measurements. The microfluidic technique exploits the flow of fluids within micrometre-dimensioned channel, permitting liquid-liquid diffusive separation between otherwise miscible non-aqueous fluids. It can be shown that several fluids e.g. methanol, hexane etc. can selectively extract target components in lubricating oil. Following an extraction, these components can be quantified using a combination of optical techniques, e.g. UV/Vis, Infrared etc. This microfluidic technique has been demonstrated for a range of lubricating oils with several acid, alkaline detergent, asphaltene/insoluble content. This technology can potentially revolutionise the way oil analysis is carried out, automating and making the process rapid and in real-time.
Alkadi, Nasr (Energy Innovation Center, BHGE) | Chow, Jon (Measurement and Sensing, BHGE) | Howe, Katy (Energy Innovation Center, BHGE) | Potyrailo, Radislav (GE Research) | Abdilghanie, Ammar (Energy Innovation Center, BHGE) | Jayaraman, Balaji (Oklahoma State University) | Allamraju, Rakshit (Oklahoma State University) | Westerheide, John (Energy Innovation Center, BHGE) | Corcoran, John 6 (Measurement and Sensing, BHGE) | Di Filippo, Valeria (Energy Innovation Center, BHGE) | Kazempoor, Pejman (Energy Innovation Center, BHGE) | Zoghbi, Bilal (Energy Innovation Center, BHGE) | El-Messidi, Ashraf (Measurement and Sensing, BHGE) | Zhang, Jianmin (Energy Innovation Center, BHGE) | Parkes, Glen (Measurement and Sensing, BHGE)
This paper presents our progress in developing, testing, and implementing a Ubiquitous Sensing Network (USN) for real-time monitoring of methane emissions. This newsensor technology supports environmental management of industrial sites through a decision support system. Upon detection of specific inputs, data is processed before passing it on for appropriate actions