|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Yan, Shi (School of Civil Engineering, Shenyang Jianzhu University) | Wu, Jianxin (School of Civil Engineering, Shenyang Jianzhu University / Shandong Electric Power Engineering Consulting Institute Co., Ltd.) | Wang, Xuenan (School of Civil Engineering, Shenyang Jianzhu University) | Zhang, Shuai (School of Civil Engineering, Shenyang Jianzhu University / CSCEC Jinan Architectural Design Institute Co., Ltd)
In order to apply a PZT-based pipeline structure damage detection technology in engineering, in this paper, a PZT wave-based active detection technology as theoretical foundation was used, combining with the characteristics of pipeline structure cracking, to develop a new type of portable detection system, which is based on virtual instrument (VI) technology. The developed system was validated through testing, and the results indicated that the system is stable and reliable, enabling to identify different crack damage states of pipeline structures in real-time and online. The proposed damage detection system can be used in pipeline structures with the low cost, portable, rapid diagnose and high-precision characteristics.
Pipeline structure has been widely used in petroleum, chemical, electric power, and natural gas industries, etc. However, due to environmental impacts or man-made disoperation pipeline will have cracks, corrosion and other defects, which will cause a great threat to the pipeline system safe operation, especially in the event of an accident, will cause huge economic losses and environmental pollution. To avoid possible accidents and ensure the safe use of pipeline structures, periodic safety inspections or long-term health monitoring of the piping system are of particular and great importance. Due to characteristics of long distance and large area of pipeline structures, applications of commonly used nondestructive testing (NDT) technologies are greatly restricted. At present, a new non-destructive testing method - the use of a piezoceramic active wave sensing detection technology for pipeline structures is gradually developed and good results are achieved (Song, Gu and Mo, 2008; Song, Gu and Mo, 2007; Song, Gu and Mo, 2006; Du, Kong, Lai and Song, 2013; Gazis, 1959; Alleyne and Cawley, 1996; Yan, Sun, Song, Gu, Huo, Liu and Zhang, 2009; Silk and Bainton, 1979; Lowe, Alleyne and Cawley, 1998; Park and Payne, 2011).
Piezoceramics, (such as Lead Zirconate Titanate, PZT), is a kind of intelligent material with sensing and driving dual characteristics. It is simple in manufacture, high in strength, resistant to moisture, heat and frequency response, etc. Due to a unique piezoelectric effect, the PZT material can be used as both a sensor element and an actuator component. The basic principle of the active detection technology applying the PZT wave based method is using the piezoelectric effects of piezoceramics to manufacture transducers which are arranged in the detected structures in a form of array for transmitting and receiving detection signals, thereby establishing an excitation and sensing channel. Based on the received data combining with a special damage detection algorithm, a structural damage identification and diagnosis can be realized by analyzing the signal difference between the healthy structure and the damaged one. The principle is shown in Fig. 1.
Digital twins are nothing but the 3D digital replica of a physical thing. They have been in existence since the days computer-aided design became mainstream during the 1990s. However, they remained standalone replicas for the next 20 years until augmented reality (AR) became prominent in the gaming and entertainment industries. As TechNewsWorld notes, AR—often referred to as mixed reality—is an immersive and "interactive experience of a real-world environment where computer-generated perceptual information enhances real-world objects." The technology expands our physical world by adding a digital layer and generating the AR.
A state-of-the-art time-domain electromagnetic tool is presented that is capable of quantifying four barriers individually, and inspecting a fifth barrier qualitatively. The working physics and salient features of the tool and its underlying technology are described.
The new tool uses time-domain electromagnetic (TEM) or pulsed eddy current (PEC) technology, which has set the benchmark for individual quantitative tubular corrosion evaluation in multi-annular well systems (multiple concentric tubulars) in recent years. Time-domain electromagnetic tools widely used in the industry are currently capable of quantifying the individual metal thickness/loss in up to three barriers. The new tool employs three highly sensitive sensors to provide high-resolution analysis of the inner barrier, while providing sufficient radial depth of investigation for up to five barriers.
The above features and advantages of the new tool are supported by modeling and fixture test results. Additional modeling is shown to compare and contrast the resolution and radial depth of investigation of the three sensors. Case studies from actual wells are also presented that illustrate how three sensors enhance the performance of this technology. Corrosion evaluation of multi-barrier systems is a major component of well integrity management because it can provide timely and cost-effective information for planning well repairs if needed. The ability of the new tool to inspect more barriers is important because it gives the operator better information for more proactive well integrity management.
The novelty of the tool is in its ability to exploit the information-rich wideband pulsed excitation using three sensors that enhance the sensitivity to multiple barriers.
This paper presents results from a flow test performed by TechnipFMC (TFMC) in December 2019 at the DNVGL Materials Laboratory in Oslo. A replica subsea multiphase meter (MPM) that includes a Venturi was used firstly to calibrate an Acoustic Sand Detector (ASD) for the X-mas Tree (XT) specific geometry, and secondly to monitor the impact of sand over the life of a typical high-rate subsea gas well.
The replica subsea MPM body was built to mimic the subsea installation on a 5-inch TFMC Subsea 2.0 XT. Several sensors were then mounted on the test rig – including ASDs and Wall Thickness Monitors (WTM) on the MPM block. Phase 1 testing was conducted over a range of air velocities with various sand particle sizes and with water injection to assess ASD sensitivity. In Phase 2, high air velocity and simultaneously high sand loading were applied to evaluate the response of erosion monitoring sensors and the mechanical robustness of the system.
It was shown that the sensitivity of the ASD mounted on the MPM block was satisfactory and that furthermore, standardized calibration curves can be generated, thereby allowing a more accurate measurement of the sand mass flow rate for the specific XT geometry. The capability of WTMs placed at the meter Venturi to monitor long-term erosion was also demonstrated.
To our knowledge, such a complete flow test with sand has never been performed on a full-scale subsea MPM geometry. The knowledge produced has allowed the verification of the accuracy of CFD erosion models, ASD calibration and the potential for development of an Erosion Monitoring System (EMS) using inputs from a range of sensors integrated into a single ROV-retrievable XT mounted metering system.
Kumar, Asheesh (Centre for Long Subsea Tiebacks, Department of Chemical Engineering, The University of Western Australia) | Di Lorenzo, Mauricio (CSIRO Energy, Australia) | W. E. Norris, Bruce (Centre for Long Subsea Tiebacks, Department of Chemical Engineering, The University of Western Australia) | Lupeau, Alexandre (Multiphase Metering & Measurement Systems, OneSubsea, Norway) | Solheim, Harald (Multiphase Metering & Measurement Systems, OneSubsea, Norway) | M. Aman, Zachary (Centre for Long Subsea Tiebacks, Department of Chemical Engineering, The University of Western Australia)
Online pipeline management systems provide real-time and look-ahead functionality for production networks. However, they are limited by a dearth of data to inform their predictions. This represents a barrier to a true, high-fidelity ‘digital twin’ where greater integration with new sensor technologies is needed to bound model predictions and improve their reliability. In this work, we present a novel MEG (Mono-ethylene glycol) sensing system from OneSubsea, the AquaWatcher v2.0, and validate it in our newly-constructed HyJump flowloop.
The HyJump flowloop has a unique subsea jumper-like geometry, with three low points and two high points and is equipped with a MEG sensor - mounted on the second low point. The sensor features an open-ended microwave frequency probe mounted flush to the pipe wall which measures the apparent permittivities of the liquid phases in the vicinity of the probe tip. It can determine the MEG concentration or water salinity by processing the measured permittivities, and has further shown that it may be able to detect hydrate deposition. Experimental work was performed to test the performance of this novel equipment while enabling a more accurate calculation of the overall mass balance in the flowloop.
An experimental campaign was conducted where, in each measurement, the jumper low points were loaded with aqueous solutions of MEG at mass fractions between 10 and 30 wt%. The entire loop was then pressurized with Perth city natural gas to 1200 psi. The pipe wall temperature was controlled with a cooling jacket in the range of 25.2 °F to 35.6 °F. These conditions simulate transient shut-down and restart operations with a high probability of hydrate formation. Results illustrate that the MEG content readings measured by the sensor were consistently accurate within a 5% relative deviation with respect to the nominal values. Further, flow restrictions due to hydrate deposition were assessed in their severity through differential pressure measurements, where it was observed that the measured MEG content oscillates significantly during hydrate sloughing-type events.
The HyJump flowloop facility offers a unique testbed for new subsea sensors, enabling performance evaluations with internal fluids at subsea conditions. The deployment of these novel sensors in the field will both improve the performance of integrated pipeline management solutions and assist operators in optimizing MEG injection dosages to enable higher fidelity hydrate management in subsea pipelines.
In recent years, oil and gas Drilling and Workover (D&WO) operations have grown significantly and as a result produced huge amount of data. Today, modern rigs transmits more than 80,000 real-time data points every day. The quality of the generated data is directly proportional to the value it can provide in predicting drilling issues and discovering hidden knowledge. One of the challenges with this high frequency overwhelming amount of data is the concept of time.
Data providers in a single site may have different time references. As every data point is stamped with the time of the computer running the data generation software, having different reference to the time makes it impossible to integrate the different types of data being generated. Additionally, it significantly affects the accuracy of critical Internet of Things (IoT) and machine learning models that rely on precise timestamps within a narrow window for detection, alerts and action.
In this paper, a full framework is introduced to address the time synchronization challenge using the industrial standard and adopting Wellsite Information Transfer Specification Markup Language (WITSML). The framework tackles the problem using three integrated steps. The first step is identifying time drift from each data source. The second step is fixing the time drift by intelligently correcting the data based on trends of the drift. The final step is deploying an IoT device to ensure GPS level time. The paper will demonstrate how such a framework can help in detecting potential satellite communication failures.
The solution improves drilling data quality and provides accurate early warning signs of operational issues. Additionally, it enables the use of fully synchronized real-time data sources to perform analytics and engineering calculations to further advance drilling automation.
Ramirez, Ricardo (The University of Texas at Austin) | Soukup, Ian Michael (National Oilwell Varco) | Tapia, Rafael (National Oilwell Varco) | Cardona, Carlos A. (National Oilwell Varco) | Boudreaux, Michael Sandford (National Oilwell Varco) | Peterson, Jacob Christian (National Oilwell Varco)
Robust sensing frameworks such as high-speed inline drilling sensors provide a better understanding of drillingstring dynamics for a complete picture of the drilling processes. This understanding comes mostly from having a robust data analytics platform where clear understanding of the downhole phenomena is represented using either physics models or digital twins. Most of the current analytical, based on a comprehensive review of technical per-review papers, have been carried out using cloud computing resources. The proposed data processing approach provides a suitable methodology where analysis of multi-channel heterogeneous sources, such as the outputs of high-speed inline drilling sensors, are processed within a given window of time. Feature selection is attained using linear discriminants which provides an accurate assessment of the type of patterns the machine learning framework needs to use as a reference. Linear discriminants have shown feature selection provides a clear path towards the synthesis of real-time deterministic synthetic sensors.
Measurements for torque and bending share same units and use similar evaluation of the physical phenomena. There are additional measurement devices in the sensing suite those provide rotational speed, acceleration, pressure and temperature. The data this heterogeneous framework provides needs to be properly aligned using the system's embedded clock as a reference. A machine learning framework tuned to the features found using linear discriminants can discern and predict signal behaviors. At this point, it is possible to provide feedback in case of abnormal activity while generating relevant key performance indicators (KPI) suitable for drilling optimization and torque modeling.
This paper offers two major novel ideas. Firstly, evaluation of machine learning approaches using linear discriminants for real-time deterministic systems has not been documented as part of any automation effort in the oil & gas vertical. Secondly, the use of inline drilling sensors data makes it an important milestone towards the implementation of control frameworks using surface instrumentation data based on a highly desirable set of data points and assured data quality. For readability purposes, the paper has been organized in three sections: 1) description of data flows considering current machine learning (ML) methodologies used in O&G and evaluation of their approach towards deterministic behaviors; 2) comparison of current vs ideal sensor architectures and their influences on data analytics approaches; 3) description of approach used for time series analysis assuming an on-sensor analytics implementation.
Oil and gas industry have evolved towards digitalization and data are fully utilized for decision making, cost optimization, improve in efficiency, and increase productivity. Upstream sector in oil & gas produce a huge number of operation and production data in a real-time platform. It is tedious process that somehow impractical and inefficient to quality check and analyze all available data manually (Subrahmanya et al., 2014). By using machine learning algorithm, this can be improved to automate data quality check at scale. On top of that, imputation can also be implemented to substitute on missing data and future forecast in real-time.
In a case of this study, a huge data was collected from more than 30,000 tags/sensors in real-time. The real-time data were collected up to seconds and quality check need to be done up to each data collected. Firstly, each equipment tags/sensors had been checked and arranged with P&ID drawing. Then, API was developed with the real-time platform. In this project, percentile of machine learning was applied and developed to quality checked the operation and production time-series data at scale. Lastly, the process was customized to other offshore platforms in the field. In addition to automated data quality checking, machine learning algorithms were also used to calculate missing information based on the underlying relationship between data points. These approaches would reduce time needed to maintain quality and reliable data for further analysis and usage.
As a result, percentile in machine learning successfully automate the process of data quality check for more productivity and efficiency. The percentile was applied to understand, validate, and monitor data at scale. Anomalies were detected in real time that allows operators to analyze further on any possibility in faulty, damage, or loss. All the outliers, missing or wrong data were also recorded and visualized in a dashboard. The model also provides additional statistic to define stale and bad data on top of automated define parameters. These features have improved efficiency of data acquisition and preparation.
As conclusion, the model assists operator in monitoring daily operation and production data efficiently. Data quality and reliability is the key factor in asset management to ensure operator trust on produced data. The quality checked data could be utilized for further analysis, troubleshooting, and decision making.
Skutin, Vasilii (TGT Diagnostics) | Abasher, Doha (TGT Diagnostics) | Izawa, Toshihide (ADOC) | Watanabe, Kimihiko (ADOC) | Nihei, Shotaro (ADOC) | Kuramata, Hideaki (ADOC) | Iwasaki, Satoshi (ADOC) | Matsuda, Hiroki (ADOC) | Nakata, Nakata (ADOC) | Alobeidli, Abdulla (ADNOC)
This paper will describe pre and post workover well diagnostic and the successful water shut-off that has resulted. It also talks about the post workover survey. The well was recently completed to a new targeted reservoir and suffered from high water cut (80%) at the start of production. Proper diagnostics identified the source of water and helped in planning a successful remedial job. After water shut off the water cut dropped to zero. Post workover evaluation confirmed production from the targeted interval.
Comprehensive diagnostics are crucial to designing a proper remedial job. A conventional PLT survey will describe the flow characteristics and parameters inside the wellbore only. During the job under consideration, two additional services were deployed; the Spectral Noise Logging and the High Precision Temperature sensing. Their combination with the PLT allows for the assessment of behind casing flow and the identification of active streaks of the formation. The quantification of flow rate behind casing is also achieved by modelling the temperature response from the high precision sensor.
The first survey was conducted with the conventional PLT to identify the source of water. Subsequently, a high precision temperature and spectral noise log were recorded under flowing, transient, and shut-in conditions. A crossflow behind casing from the upper unperforated intervals was identified. The main liquid production is coming from the water-bearing formation that is located 15 ft above the targeted reservoir. The cement evaluation surveys that were run previously confirmed the presence of a cement channel above of the perforated interval. Based on these conclusions a remedial job was designed and conducted successfully. Another HPT/SNL survey was conducted post workover. It confirmed the absence of any further flow behind casing and that all liquid production is happening from the targeted reservoir. Surface test data showed a rapid decrease in water production to 1% post workover.
This paper highlights the importance of complementing the current practice of running conventional PL surveys with two additional services: the Spectral Noise Logging and Temperature simulation/modelling using a High Precision Temperature measurement.
This paper presents a novel approach of continuously measuring drilling fluid rheology and density by use of sound signals. A unique apparatus is built with a series of pipe sections designed to exact pre-calculated dimensions to achieve equivalent standard shear rates as stipulated in the American Petroleum Institute (API) Recommended Practice 13D for measuring the rheology of oil-well drilling fluids (from 3 to 600 RPM). Acoustics waves are passed through the fluids of interest and their interaction is recorded and analyzed to deduce the density and rheological properties of the fluids.
The concept of resonance as demonstrated by the Barton's pendulums are the basis of the methodology. Sound signals are known to exhibit a damping effect when passing through various media. Pairs of sensors are employed in this set-up and their signal response are first characterized and calibrated with fluids of known properties. Electric current is converted into acoustic signals by piezoelectric sensors mounted of the flowline which are then emitted through the fluids desired to be measured without interrupting the flow. A matching sensor receives these damped signed and reconverts them back to electromotive potentials for recording by a data acquisition unit. The signals are then analyzed by applying statistical techniques to interpret and obtain the fluids physical properties.
Owing to the nature of the task, the goal of accurately achieving simultaneous measurement of density and viscosity is attained by applying an ensemble machine learning algorithm, known as Multivariate Random Forest. Pure chemicals and fluids of known properties form the training group on which the predictive model is built for subsequent testing on new mud samples flowing through each section. The pipe sections generate shear rates covering the standard range adopted in oilfield reports. Results from each pair of sensors are analyzed and compared with dial readings from rotational viscometers; these have shown to be within a narrow band of error.
As a result of this work, the voltage outputs are sent continuously and in real-time to a processing computer that converts the values to dial readings at standard shear rates, while not disrupting the flow. This can aid in the better monitoring and surveillance of the entire fluid system of the well, which is highly beneficial to well control. The system can also be arranged to acquire gel strengths or how the fluid behaves after a fixed period of rest. Improvements can be made on the current procedures for fluid characterization which have remained relatively static for many years. This work engages the disciplines of rheology, acoustics and machine learning, creating a mechanism for continuous and real-time drilling fluid surveillance critical to the enhancement of safe development of petroleum resources.