Analysis of Oil and Gas Distributed Acoustic Sensor Data

Monitoring the behaviour and nature of downhole fluid flow has a great economic value for the oil and gas industry. By analysing the oil production of each well, the oil production can be maximised and optimised whilst reducing the cost. Recent developments enable optical fibres to be used to record distributed sounds as if from an array of microphones along the full length of a several kilometer long well. We have developed novel signal processing and pattern recognition algorithms to analyse the recorded sounds which enable us to most of accurately estimate the flow speed of the fluid. We have demonstrated this accuracy with water, oil and gas fluid flow

Analysis of Oil and Gas Big Data Using Artificial Intelligence

Monitoring in-well flow is essential for the oil and gas industry to manage the oil field. The flow surveillance identifies the well condition allowing optimisation of the quality and volume of oil or gas production and saving costs for the oil companies. The well operators need to know the type of fluid in the pipe, the combination and ratio of each fluid in multi phase flow regimes (e.g. gas, oil, water), the settings of Inflow Control Valves (ICVs) which control the flow rate in the main pipe and from several side branching pipes coming from different underground reservoirs. The well operators also need to know the speed and direction of the fluid flow at each point down the well. Distributed Acoustic Fibre optic Sensors alongside or inside the well pipe are used to collect acoustic data as a function of time from effective acoustic sensors spaced by about a metre or less along thousands of kilometres of oil and gas well. The size of collected sound data from sensors is more than a Terabyte which can be analysed successfully using Artificial Intelligence Machine Learning algorithms

Acoustic Sensors to Measure Speed of Oil Flow in Downhole Pipes

This study was conducted to estimate the downhole speed of flow in oil wells and determined the flow direction by analyzing acoustic data recorded by fibre optic distributed acoustic sensors. The signals generated from acoustic data are in the time versus distance domain that are then normalized and differentiated with respect to distance. A 2D Fast Fourier Transform is used to convert time to frequency and distance to wave-number for subsequent calculation. A Gamma correction function was employed to enhance an intensity of the signal in the frequency wevenumber domain. Also, decaying function was successfully applied to enhance the signals with a very low frequencies. We developed a novel method called integration along the radius in polar coordinate to measure the speed of sound and calculating the speed of oil flow. We compared the performance of our method with a Radon transform and proved our method outperforms an existing methods in both processing time and accuracy. The data sets used in this study are recorded from real oil and gas pipes which means there is no controlled environment and there are lots of noisy signals as a result of unpredicted events under the sea. The result of this study is applicable in Oil and Gas production energy industry, Hydraulic fracturing and shale gas extraction energy industry, Borehole water supply industry, Gas pipeline transportation energy industry and Carbon Dioxide Sequestration industry

Fluid Flow Velocity Measurement in Active Wells Using Using Fiber Optic Distributed Acoustic Sensors

Real time monitoring of the behaviour of fluids along the whole length of fluid filled well pipes is important to the oil and gas industry as it enables well operators to maximize oil and gas production and optimize the quality of oil and gas produced, whilst reducing the cost. Flow speed measurement is one of the key approaches in fluid flow monitoring in wells. In this paper, three methods are designed, developed and demonstrated to estimate the speed and direction of flow at a range of depths in real world oil, gas and water wells using acoustic data set from distributed acoustic sensors that attached to the wells. The developed methods are based on a new combination of several techniques from signal processing, machine learning and physics. The Terabyte size acoustic dataset are recorded from each well as a two-dimensional function of both distance along the pipeline and time. The aim of the developed methods is estimating flow speed at each point along over 3000 meters pipelines and increasing the accurately and efficiently of the flow speed calculation compared to the existing method. The methods developed in this paper are computationally inexpensive, which make them suitable for real time well monitoring

Convolutional Neural Networks to Classify Oil, Water and Gas Wells Fluid Using Acoustic Signals

Identifying the fluid type and predicting the amount of each fluid in the fluid mixture within the well pipes are important for oil and gas production energy industry and borehole water supply. Therefore automating this process will be very valuable for the oil industry because it maximises the quality and quantity of extracted oil and reduces the cost. The current study contributes to our knowledge by addressing this important issue using machine learning algorithms. The presented paper investigates the classification a lgorithms that identify the fluid type in oil, water and gas pipes using acoustic signals. The datasets analysed in this study are collected from real oil, water and gas well pipes under the sea where there is no controlled environment and data contains lots of noisy signals due to unpredicted events under the sea. Data is recorded during 24 hours from Distributed Acoustic Sensors which is attached alongside the 3500 m of three well pipes: oil, water and gas. The acoustic dataset are in time-distance domain and are converted to frequency-wave number domain using 2D fast Fourier transform. The outcome of 2D fast Fourier transform is sampled and fed into Artificial Neural Networks and Conventional Neural Networks algorithms to classify each fluid type. Both algorithms are trained on three datasets (oil, gas and water) and tested on another dataset. The result of this study shows Artificial Neural Networks and Conventional Neural Networks algorithms classify the fluid type with the accuracy of 79.5% and 99.3% respectively when applied on the test dataset

The Effect of Antimicrobial Photodynamic Therapy with Radachlorin and Toluidine Blue on Streptococcus Mutans: An in Vitro Study

Objectives: Dental caries and periodontal diseases are caused by infection of teeth and supporting tissues due to complex aggregate of bacteria known as biofilm, firstly colonized by streptococci. The main purpose of this in vitro study was to evaluate the antimicrobialeffects of toluidine blue O (TBO) and Radachlorin® in combination with a diode laser on the viability of Streptococcus mutans.Materials and Methods: Bacterial suspensions of Streptococcus mutans were exposed to either 0.1% TBO associated with (20 mW, 633 nm diode laser, continuous mode, 150 s) or 0.1% Radachlorin® and laser irradiation (100 mW, 662 nm diode laser, continuous mode,120 s). Those in control groups were subjected to laser irradiation alone or TBO/Radachlorin® alone or received neither TBO/Radachlorin® nor laser exposure. The suspensions were then spread over specific agar plates and incubated aerobically at 37°C. Finally, the bactericidal effects were evaluated based on colony formation.Results: Potential bacterial cell killing was only observed following photosensitization with TBO and 3 j/cm2 laser exposure (p<0.05), whereas Radachlorin® showed significant reduction in dark condition compared to laser exposure (p<0.05).Conclusion: TBO-mediated photodynamic therapy seems to be more efficient than Radachlorin ® in significantly reducing the viability of Streptococcus mutans in vitro

Deep analysis of EIT dataset to classify apnea and non-apnea cases in neonatal patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates' EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features

Effect of dietary wood betony, Stachys lavandulifolia extract on growth performance, haematological and biochemical parameters of common carp, Cyprinus carpio

A 6 week study was conducted to assess the effects of wood betony (WB), Stachys lavandulifolia extract on growth performance, hematological and biochemical parameters of common carp, Cyprinus carpio. Different levels of the WB extract (0, 2, 4 and 8 % weight per weight, W/W, 0WB, 2WB, 4WB and 8WB) in the diet were used. The results showed that final weight and weight gain were significantly improved by WB (p0.05). There were no significant differences in hemoglobin, hematocrit, mean erythrocytes of hemoglobin, mean erythrocyte volume, mean hemoglobin erythrocyte concentration and white blood cell (WBC) counts (p>0.05), while, red blood cells (RBC) counts showed significant declining trend by increasing the level of the plant extract from control to 8WB (p<0.05). Significant elevation in the levels of total protein, albumin and globulin and albumin/globulin ratio by increasing WB concentration in the diet were observed (p<0.05). Diet enriched by WB could decrease serum level of triglycerides and cholesterol in comparison with the control (p<0.05). Based on the results of this study, it could be concluded that feeding common carp with WB can improve growth and some immunity characteristics as well as lipid metabolism

Improving Data Transmission in Fiber Optics by Detecting Scratches on the Fiber End Face

Optical fiber sustains scratches, pits and other types of defects on the end face during the polishing process. Hence, fiber end face inspection is a significant process for fiber manufacturers when analysing the performance of a fiber. In order to identify the defects present on the fiber end face, a novel model is presented in this paper. Our model combined filtering methods to enhance the contrast of the images so scratches can be successfully detected. However, because the photos have been taken with different gains and exposures, they can not be processed with standard image processing techniques. We developed a method to analyse the defects intensity that could be located under different gains and exposures. We established that the images taken with the high gains and exposures performed well for optical fiber defect recognition