Real-time Pressure Data Research Articles

Data Mining Algorithms for Operating Pressure Forecasting of Crude Oil Distribution Pipelines to Identify Potential Blockages

The implementation of data mining has become very popular in many fields recently, including in the petroleum industry. It is widely used to help in decision-making processes in order to minimize oil losses during operations. One of the major causes of loss is oil flow blockages during transport to the gathering facility, known as the congeal phenomenon. To overcome this situation, real-time surveillance is used to monitor the oil flow condition inside pipes. However, this system is not able to forecast the pipeline pressure on the next several days. The objective of this study is to forecast the pressure several days in advance using real-time pressure data, as well as external factor data recorded by nearby weather stations, such as ambient temperature and precipitation. Three machine learning algorithms—multi-layer perceptron (MLP), long short-term memory (LSTM), and nonlinear autoregressive exogenous model (NARX)—are evaluated and compared with each other using standard regression evaluation metrics, including a steady-state model. As a result, with proper hyperparameters, in the proposed method of NARX with MLP as a regressor, the NARX algorithm showed the best performance among the evaluated algorithms, indicated by the highest values of R2 and lowest values of RMSE. This algorithm is capable of forecasting the pressure with high correlation to actual field data. By forecasting the pressure several days ahead, system owners may take pre-emptive actions to prevent congealing.

Press Casting Quality Prediction and Analysis Based on Machine Learning

In an industrial mass production pattern, quality prediction is one of the important processes when guarding quality. The products are extracted periodically or quantitatively for inspection in order to observe the relationship between process variation and engineering specification. When these irregularities are not instantly detected by lot sampling inspection, lot defectives are produced, and the defective cost increases. Failure to identify defects during sampling inspection leads to product returns or harm to business reputation. Press casting is a common mass production method in the metal industry. After the metal is molten at a high temperature, high pressure is injected into the mold, and then it is solidified and formed in the mold. Thus, pressure stability inside the mold is one of the key factors that influences quality. The melting point of aluminum alloy is normally around 650 °C, but there was no sensor that could withstand this high temperature. To combat this, we developed a high temperature resistant sensor and installed it into pressure casting mold grounded on the principles of fluid mechanics and experts’ suggestions in order to realize the impact of pressure change on the mold. To our limited knowledge, it was a seminal study on predicting mold’s casting quality via in-mold pressure data. We propose a press casting quality prediction method based on machine learning. By collecting the in-mold pressure data in real time. Savitzky-Golay Filter is used for data smoothing, and first-order difference is taken to extract the time interval of an actual injection of molten metal in-to the mold. We extract the key data that influence the quality and employ XGBoost to establish a classifier. In the experiment we prove that the method achieves good accuracy of quality prediction and recall of defectives for in-mold pressure. Via this model, we not only can save large amount of time and costs, but also can carry out maintenance warnings in advance, notify professionals to stop produce defective products, reduce the shipping risk and maintain reputation so as to strengthen its competitive edge.

Real-Time Underground Plastic Pipeline Water Leakage Detection and Monitoring System

The leaking of the water pipeline increasingly influences the quality of life and threatens the water supplier in developing countries. Proper method is required to effectively detecting the source for replacement to reduce the loses. Therefore, a water leakage detection with monitoring system is developed to detecting the precise leaking location of a buried plastic pipeline. The real-time pressure data obtained from the pressurized pipeline are transferred to the monitoring system with GUI (graphical User Interface), developed using LabVIEW software. In the experimental execution, a leaking pipeline is designed. Then, few pressure sensors are installed on the pipeline as a primary segment for the detection process. To prevent a false non-leak alarm and malfunction of the pressure sensors, the pressure threshold value and malfunction alarm is set using LabVIEW. Cross-correlation method is implemented that increase the accuracy of the leaking distance estimation. The processing and control unit in this article are manipulated using LabVIEW software and NI myRIO-1900 respectively.

Application of Deep Learning Network in Bumper Warpage Quality Improvement

Based on the context of Industry 4.0 smart manufacturing and for the prediction of injection molding quality of automobile bumpers, this study proposes a deep learning network that combines artificial neural networks and recognizable performance evaluation methods to better achieve the prediction and control of product quality. A pressure sensor was used to monitor and collect real-time pressure data in the mold cavity of the bumper. The quality indicators reflecting the molding quality were selected, and the correlation between these indicators and the molding quality was evaluated using recognizable performance evaluation methods and Pearson’s correlation coefficient. The standard z-score was used to filter out the abnormal data in the experimental data, and the bumper critical length warpage was converted into different quality levels, and the bumper critical length warpage was defined as either “qualified” and “unqualified” in order to improve the prediction accuracy of the model. Through the experimental study of this research, the monitoring and control of bumper injection molding parameters was completed to control and improve the molding quality of the bumper.

MOVEMENT STARTEGIES ADOPTED BY INDIVIDUALS WITH MEDIAL KNEE OA IN RESPONSE TO PRESSURE-BASED AUDITORY FEEDBACK GAIT RERAINING

Purpose: Structural progression of medial knee osteoarthritis (OA) is associated with an increased knee adduction moment (KAM), a surrogate measure of the compartmental knee loads. The KAM is thus a target for mechanical intervention. Based on real-time pressure data and individualized training thresholds, the use of plantar pressure-based auditory feedback to shift weight medially was found to reduce the KAM in individuals with medial knee OA. However, its potential mechanisms remain unclear. Thus, this study aimed to elucidate movement strategies adopted by individuals with medial knee OA in response to pressure-based feedback gait retraining and compare movement strategies of substantial and partial responders of the intervention.

Design of a rapid diagnostic model for bladder compliance based on real-time intravesical pressure monitoring system

ObjectiveThe diagnosis of bladder dysfunction for children depends on the confirmation of abnormal bladder shape and bladder compliance. The existing gold standard needs to conduct voiding cystourethrogram (VCUG) examination and urodynamic studies (UDS) examination on patients separately. To reduce the time and injury of children's inspection, we propose a novel method to judge the bladder compliance by measuring the intravesical pressure during the VCUG examination without extra UDS. MethodsOur method consisted of four steps. We firstly developed a single-tube device that can measure, display, store, and transmit real-time pressure data. Secondly, we conducted clinical trials with the equipment on a cohort of 52 patients (including 32 negative and 20 positive cases). Thirdly, we preprocessed the data to eliminate noise and extracted features, then we used the least absolute shrinkage and selection operator (LASSO) to screen out important features. Finally, several machine learning methods were applied to classify and predict the bladder compliance level, including support vector machine (SVM), Random Forest, XGBoost, perceptron, logistic regression, and Naive Bayes, and the classification performance was evaluated. Results73 features were extracted, including first-order and second-order time-domain features, wavelet features, and frequency domain features. 15 key features were selected and the model showed promising classification performance. The highest AUC value was 0.873 by the SVM algorithm, and the corresponding accuracy was 84%. ConclusionWe designed a system to quickly obtain the intravesical pressure during the VCUG test, and our classification model is competitive in judging patients’ bladder compliance. SignificanceThis could facilitate rapid auxiliary diagnosis of bladder disease based on real-time data. The promising result of classification is expected to provide doctors with a reliable basis in the auxiliary diagnosis of some bladder diseases prior to UDS.

Health, Safety, and Environment Operational Excellence through Full Utilization of Intelligent Field

Summary Working in the oil industry comes with unique challenges and risks, and so extra precautions and safety measures coupled with strict environmental compliance must be applied. Contrary to the common belief that strict safety enforcement could hinder smooth operations, the deployment of new technologies and enhanced solutions of processes has enabled operational excellence (OE) and improved safety performance. In this paper, we demonstrate health, safety, and environment performance improvement through implementing two main initiatives: The first category has initiatives that require less intervention or personnel; for example, the deployment of cableless pressure sensors or permanent monitoring systems in key wells to ensure continuous real-time pressure data to monitor reservoir pressure. The second category has initiatives that mitigate traditional health, safety, and environment risks; for example, through use of multiphase flow meters (MPFMs) to collect accurate and continuous flow measurements instead of traditional well testing. Optimizing operations costs while maintaining a high-level of safety is achieved through a dedicated team working in a state-of-the-art Production Operations Surveillance Hub (POSH), which enables the monitoring of wells in real time, making production optimization decisions, and ensuring a high level of well integrity via close monitoring of wells and assets.

Automated pressure transient analysis: A cloud-based approach

Pressure transient analysis provides essential information to evaluate the dimensions of injection induced fractures, permeability damage near the wellbore, and pressure elevation in the injection horizon. For injection wells, shut-in data can be collected and analyzed after each injection cycle to evaluate the well injectivity and predict the well longevity. However, any interactive analysis of the pressure data could be subjective and time-consuming. In this study a novel cloud-based approach to automatically analyzing pressure data is presented, which aims to improve the reliability and efficiency of pressure transient analysis.There are two fundamental requirements for automated pressure transient analysis: 1) Pressure data needs to be automatically retrieved from field sites and fed to the analyzer; 2) The engineer can automatically select instantaneous shut-in pressure (ISIP), identify flow regimes, and determine the fracture closure point if any. To meet these requirements as well as to take advantage of cloud storage and computing technologies, a web-based application has been developed to pull real time injection data from any field sites and push it to a cloud database. A built-in pressure transient workflow has been also proposed to detect any stored or real-time pressure data and perform pressure analysis automatically if the required data is available.The automated pressure transient analysis technology has been applied to multiple injection projects. In general, the analysis results including formation and fracture properties (i.e. permeability, fracture half length, skin factor, and fracture closure pressure) are comparable to results from interactive analysis. Any discrepancies are mainly caused by poor data quality. Issues such as inconsistent selections of ISIP and different slopes defined for pre and after closure analyses also contribute to the divergence. Overall, the automated pressure transient analysis provides consistent results as the exact same criteria are applied to the pressure data, and analysis results are independent of the analyzer's experience and knowledge.As data from oil/gas industry increases exponentially over time, automated data transmission, storage, analysis and access are becoming necessary to maximize the value of the data and reduce operation cost. The automated pressure transient analysis presented here demonstrates that cloud storage and computing combined with automated analysis tools is a viable way to overcome big data challenges faced by oil/gas industry professionals.

A low-outgassing-rate carbon fiber array cathode**Project supported by the National Natural Science Foundation of China (Grant No. 61671457).

In this paper, a new carbon fiber based cathode — a low-outgassing-rate carbon fiber array cathode — is investigated experimentally, and the experimental results are compared with those of a polymer velvet cathode. The carbon fiber array cathode is constructed by inserting bunches of carbon fibers into the cylindrical surface of the cathode. In experiment, the diode base pressure is maintained at 1×10−2 Pa–2×10−2 Pa, and the diode is driven by a compact pulsed power system which can provide a diode voltage of about 100 kV and pulse duration of about 30 ns at a repetition rate of tens of Hz. Real-time pressure data are measured by a magnetron gauge. Under the similar conditions, the experimental results show that the outgassing rate of the carbon fiber array cathode is an order smaller than that of the velvet cathode and that this carbon fiber array cathode has better shot-to-shot stability than the velvet cathode. Hence, this carbon fiber array cathode is demonstrated to be a promising cathode for the radial diode, which can be used in magnetically insulated transmission line oscillator (MILO) and relativistic magnetron (RM).

Maximizing the value of pressure data in saline aquifer characterization

The injection and storage of freshwater in saline aquifers for the purpose of managed aquifer recharge is an important technology that can help ensure sustainable water resources. As a result of the density difference between the injected freshwater and ambient saline groundwater, the pressure field is coupled to the spatial salinity distribution, and therefore experiences transient changes. The effect of variable density can be quantified by the mixed convection ratio, which is a ratio between the strength of two convection processes: free convection due to the density differences and forced convection due to hydraulic gradients. We combine a density-dependent flow and transport simulator with an ensemble Kalman filter (EnKF) to analyze the effects of freshwater injection rates on the value-of-information of transient pressure data for saline aquifer characterization. The EnKF is applied to sequentially estimate heterogeneous aquifer permeability fields using real-time pressure data. The performance of the permeability estimation is analyzed in terms of the accuracy and the uncertainty of the estimated permeability fields as well as the predictability of breakthrough curve arrival times in a realistic push-pull setting. This study demonstrates that injecting fluids at a rate that balances the two characteristic convections can maximize the value of pressure data for saline aquifer characterization.

Using wavelet denoising in automatic online efficiency estimation of a hydraulic excavator

A hydraulic excavator has a number of devices attached, the operating speeds of which are important performance indicators for performance evaluation and end-of-line inspection. This paper proposes an online and low-cost detection method to estimate the operating speed based on the peak detection of the main pump outlet pressure. A pressure sensor is installed on the main pump discharge point of a hydraulic excavator to collect real-time pressure data during operation. Then the wavelet method is deployed to denoise the obtained pressure data so that two special peak points are captured as start and end points for accurately estimating the operating time and speed of excavator devices. The proposed system is used for end-of-line inspection and performance evaluation of hydraulic excavators. Experimental results from 510 groups of data show that the proposed method can achieve a high success rate of 95.1%, and a high detection efficiency that is 60% faster than manual measurement.

Evaluating Pressure Ulcer Development in Wheelchair-Bound Population Using Sitting Posture Identification

Pressure ulcers are a common complication among wheelchair-bound population. They are resulted from prolonged exposure to high pressure, which restricts blood flow and leads to tissue necrosis. In this work, a continuous pressure monitoring system is developed for pressure ulcer prevention. The system consists of 64 pressure sensors on a 40×50 cm2 sheet. Real time pressure data and corresponding maps are displayed on a computer simultaneously. Furthermore, a posture detection procedure is proposed for sitting posture identification. Having information about the patient’s postural history, caregivers are capable of a better decision about repositioning and treating the patient.

Processing and analysis of transient pressure measurements from permanent down-hole gauges

With permanent down-hole gauges (PDGs) widely installed in oilfields around the world in recent years, a continuous stream of transient pressure data in real time is now available, which motivates a new round of research interests in further developing pressure transient processing and analysis techniques. Transient pressure measurements from PDG are characterized by long term and high volume data. These data are recorded under unconstrained circumstances, so effects due to noise, rate fluctuation and interference from other wells cannot be avoided. These effects make the measured pressure trends decline or rise and then obscure or distort the actual flow behavior, which makes subsequent analysis difficult. In this paper, the problems encountered in analysis of PDG transient pressure are investigated. A newly developed workflow for processing and analyzing PDG transient pressure data is proposed. Numerical well testing synthetic studies are performed to demonstrate these procedures. The results prove that this new technique works well and the potential for practical application looks very promising.

Production-Induced Stress Change in and Above a Reservoir Pierced by Two Salt Domes: A Geomechanical Model and Its Applications

Summary Production decreases the pore-fluid pressure and increases the effective stress acting on the load-bearing-grain framework that makes up the reservoir. As a result, the reservoir deforms and compacts, and because it is connected to the rocks around it, there will be deformations and displacements in these rocks also. Well known geomechanical effects of production include surface subsidence, wells damaged by shear, and time shifts in 4D seismic. Less well known is how the changes in the stress field itself should be taken into account in operations—e.g., to design infill wells and to plan production stimulation by hydraulic fracturing or waterflooding of the reservoir. We present a geomechanical model for the initial stress field and production-induced stress changes in and around a steeply dipping hydrocarbon reservoir penetrated by two large salt domes. The model integrates 3D seismic and geological understanding, geomechanical data from wells and analogues, and depletion patterns from fluid-flow (dynamic) simulation. Our model results confirm published models of principal-stress orientation in rocks pierced by salt domes. The depleted-model results show stress changes up to several MPa in magnitude compared with the preproduction stress state, but only limited changes in the stress orientations. The model highlights the influence of structural dip and time-dependent salt/sediment interaction on stress changes. We then describe the application of the model in wellbore stress analysis for infill wells and in a water-injection scheme that has, we believe, been severely impacted by injection-induced fractures propagating in the reservoir from the injector wells toward the producer wells. We explain how the latter application uses a 3D flow-simulation model coupled to a dynamic fracture-propagation model. The geomechanical model provides key input: stress magnitude and stress orientation. Results are validated against a more conventional analysis of real-time pressure data. In both applications, the integration of geomechanics in 3D static and dynamic models improved insight into the rock response to drilling and waterflooding, thus helping to optimize production.

Optimization of Production Performance With ICVs by Using Temperature-Data Feedback in Horizontal Wells

Summary Horizontal wells with intelligent completions have been used widely in many field cases. With inflow-control valves (ICVs), production can be optimized either on individual wells or at large reservoir scale. A critical step in this procedure is to understand the inflow profiles along horizontal wellbores. To develop control and optimization strategies, permanent monitoring systems such as pressure gauges and temperature sensors have been deployed with downhole control valves to provide temperature or pressure data in real time, and to translate downhole-flow conditions from these data. The relationships between temperature and inflow distribution have been developed before in intelligent-well models, and these models are very useful in locating water or gas entries and obtaining better production performances. In this work, we present a new idea—using temperature feedback to directly regulate flow-control valves to achieve an approximately uniform flow distribution. Instead of detecting water or gas flow with different thermal properties of fluids, we aim to regulate flow distribution through temperature behavior to achieve optimal flow conditions. Early-time temperature and pressure data are used to estimate permeability and initial flow profiles along the well; then, developed models are applied to generate a desired temperature profile that yields an evenly distributed flow rate along the wellbore. ICVs are operated on the basis of the guidance from the initial temperature data, and the temperature is monitored as it approaches the desired temperature profile. Through this procedure, we will produce a well at an optimal condition by choking down the flow rate at higher-permeability locations along the well. This can increase oil-flow rate and delay early water breakthrough. This paper explains the details of the procedure. Two examples are used to illustrate the application of the method; one is for a waterdrive reservoir, and the other is a water-injection case. The results show significant improvement in well performance in both examples.

Development of Flexible Gas Fuel Engine System

In this study, the authors aim to develop a small gas engine system for biomass gas by modifying the control system of a conventional spark ignition engine. Before developing a control algorithm, combustion experiments with various component fuels assumed to have the components of real biomass gases, such as fermentation and pyrolysis gases, were carried out. It was clarified that the relationship between dimensionless combustion duration and equivalence ratio can be expressed by a first-order linear function regardless of fuel composition. Indicated thermal efficiency can also be expressed by combustion duration and volumetric efficiency when the coefficient of variation of indicated mean effective pressure is lower than 5% and pumping loss decreases against volumetric efficiency linearly, and the relationship between combustion duration and MBT can also be expressed by a first-order function. By using these relationships, a gas engine control algorithm, which can define the target values of the equivalence ratio of a premixture and ignition timing that realize a high thermal efficiency for fuel compositions automatically by analyzing in-cylinder gas pressure data in real time, is developed in order to use gaseous fuels produced from biomass resources effectively. Thus, a biomass gas fuel engine system is developed by applying the algorithm to an automobile gasoline engine, hardware modifications of which are only the fuel supply system and flywheel. The engine system is connected to a gasification plant using wood chips and an operation test is carried out. As a result, the engine system can set an optimum premixture condition and an ignition timing, which realizes a stable and high-thermal-efficiency operation automatically.

Building A Bridge Between Aesthetics And Acoustics With New Technology: A Proposed Framework For Recording Emotional Response To Sung Performance Quality

The paper describes a framework for the incorporation of an interactive-multimedia computer application into a four-year multimethod research project that investigates the psychoacoustic interpretation of vocal performance quality. Initial research, based on the analysis of semi-structured interviews with people from a wide range of backgrounds, followed by an extensive questionnaire survey, indicates different perceptual features as salient for individual listeners of singing performances. A synthesis of these reportedly significant perceptual features is now in the process of being critically evaluated by individual expert listeners using a novel experimental procedure that embraces the application of new multimedia technology. The new technology is designed to act as a real-time monitoring system along a `like/dislike' continuum of perceived quality, whilst also collecting real-time pressure data that is outside the listener's conscious awareness. The paper will discuss the overall research methodology, and the implementation and application of this technology, as well as possible implications for the teaching of singing.

Comparisons of wind-tunnel and full-scale surface pressure measurements on low-rise pitched-roof buildings

Full-scale surface pressure measurements recorded on a range of agricultural buildings provided the basis for a series of full-scale/model-scale comparisons using 1:100 scale models. Mean, rms, maximum and minimum pressure coefficients and real time pressure data were recorded for comparison with full-scale data. This paper focuses on the mean pressure results for four low-rise buildings. Pressure distributions at mid-building-length for winds perpendicular to the ridge line are compared. Effects of the model-scale wind profile on the pressure distributions are also included. The results show that very good agreement is possible between full-and model-scale data. This agreement is shown to hold as the wind direction changes about the transverse wind direction. However, where separation and recirculation occur on the windward roof slope, the model-scale significantly underestimates the suctions. This effect appears to be roof pitch dependent, there being a greater effect at 15° than at 10°. Consequently, the pressure distribution on the model can be altered significantly across the entire roof.