Injection Profiling During Limited-Entry Fracturing Using Distributed-Temperature-Sensor Data

SPE Journal ◽  
2012 ◽  
Vol 17 (03) ◽  
pp. 752-767 ◽  
Author(s):  
Hai Hoang ◽  
Jagannathan Mahadevan ◽  
Henry Lopez
Keyword(s):  
New Technology ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Temperature History ◽  
Tight Gas ◽  
Fracture Characteristics ◽  
Fracturing Process ◽  
Limited Entry ◽  
Injection Rates

Summary Tight gas plays often have multiple lenses of producing formations. Multizone fracturing or limited-entry fracturing is a cost-effective method to complete and produce tight gas wells in these layered reservoirs. The rate and volume of fracturing fluid injected into the different layers have an important role in determining the fracture characteristics. However, because of the spatial restriction of downhole conditions, it is very challenging to obtain a specific injection rate for each perforated zone. Temperature variations in the wellbore, outside of the casing, are available with new technology such as distributed-temperaturesensor (DTS) fiber-optic cables. The main objective of this study is to relate the wellbore-temperature changes as measured by DTS data to the wellbore and fractured-interval injection rates during a multizone fracturing process. We develop a forward simulation model on the basis of mass and energy conservation for calculating the temperature profile and temperature history in the wellbore and in the rock surrounding the wellbore. The model allows for liquid flow into the fractured interval. Subsequently, the model is integrated with an inverse-estimation algorithm, which is used to estimate flow rates both in the wellbore and into the fractured interval. The estimation algorithm is based on a gradient search method. A distinguishing feature of this work is the development of a radial model used to represent the temperature evolution in the near-wellbore region. The higher order allows accurate calculation of the temperature in the wellbore while still capturing the fluid-flow and heat-transport aspects of the hydraulic-fracture propagation. Our estimation results show a good comparison between the calculated temperature profiles and those observed in the field with DTS. Also, the model is able to estimate a flow-rate history consistent with total field-injection volume. This work enables an accurate and quick interpretation of the wellbore DTS data to determine the interval injection rates during a hydraulic-fracturing process. Knowledge of accurate interval injection rates and the corresponding fracture characteristics can be useful in designing a better limited-entry completion that can optimize the fracture length by use of rate control and/or fluid diversion.

Determining Rig State from Computer Vision Analytics

2021 ◽  
Author(s):  
Crispin Chatar ◽  
Suhas Suresha ◽  
Laetitia Shao ◽  
Soumya Gupta ◽  
Indranil Roychoudhury
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Graphics Processing Units ◽  
New Technology ◽  
Three Dimensional ◽  
Cost Effective ◽  
Temporal Information ◽  
Sensor Data ◽  
High Tech ◽  
Drilling Rig

Abstract For years, many companies involved with drilling have searched for the ideal method to calculate the state of a drilling rig. While companies cannot agree on a standard definition of "rig state," they can agree that as we move forward in drilling optimization and with further use of remote operations and automation, that rig state calculation is mandatory in one form or the other. Internally in the service company, many methods exist for calculating rig state, but one new technology area holds promise to deliver a more efficient and cost-effective option with higher accuracy. This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning model that relies exclusively on videos collected on the rig floor to infer rig states, it is possible to move away from the existing methods as the industry moves to a future of high-tech rigs. Videos, in contrast to sensor data, are relatively easy to collect from small inexpensive cameras installed at strategic locations. Consequently, this paper presents machine learning pipeline that is implemented to perform rig state determination from videos captured on the rig floor of an operating rig. The pipeline can be described in two parts. Firstly, the annotation pipeline matches each frame of the video dataset to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Secondly, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from videos. The models are trained on a dataset of 3 million frames on a cloud platform using graphics processing units (GPU). Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional three-dimensional (C3D) model that used three-dimensional (3D) convolutions, and a two-stream model that uses optical flow to capture temporal information. The initial results demonstrate this pipeline to be effective in detecting rig states using computer vision analytics.

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

Teletinnitus Program at the VA Gulf Coast Veterans Health Care System

2014 ◽  
Vol 4 (1) ◽  
pp. 23-29
Author(s):  
Constance Hilory Tomberlin
Keyword(s):  
Health Care ◽  
Patient Satisfaction ◽  
Patient Care ◽  
Health Care System ◽  
Gulf Coast ◽  
New Technology ◽  
Cost Effective ◽  
Veterans Health ◽  
Potential Benefits ◽  
Care System

There are a multitude of reasons that a teletinnitus program can be beneficial, not only to the patients, but also within the hospital and audiology department. The ability to use technology for the purpose of tinnitus management allows for improved appointment access for all patients, especially those who live at a distance, has been shown to be more cost effective when the patients travel is otherwise monetarily compensated, and allows for multiple patient's to be seen in the same time slots, allowing for greater access to the clinic for the patients wishing to be seen in-house. There is also the patient's excitement in being part of a new technology-based program. The Gulf Coast Veterans Health Care System (GCVHCS) saw the potential benefits of incorporating a teletinnitus program and began implementation in 2013. There were a few hurdles to work through during the beginning organizational process and the initial execution of the program. Since the establishment of the Teletinnitus program, the GCVHCS has seen an enhancement in patient care, reduction in travel compensation, improvement in clinic utilization, clinic availability, the genuine excitement of the use of a new healthcare media amongst staff and patients, and overall patient satisfaction.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

Subsurface fracture surveys using a borehole television camera and acoustic televiewer

1987 ◽  
Vol 24 (4) ◽  
pp. 499-508 ◽  
Author(s):  
J. S. O. Lau ◽  
L. F. Auger ◽  
J. G. Bisson
Keyword(s):  
Rapid Assessment ◽  
Cost Effective ◽  
Fracture Aperture ◽  
Computer Data ◽  
Fracture Characteristics ◽  
Television Camera ◽  
Strip Chart ◽  
Data Record ◽  
Magnetic North ◽  
Apparent Orientation

Borehole television survey and acoustic televiewer logging provide rapid, cost-effective, and accurate methods of surveying fractures and their characteristics within boreholes varying in diameter from 7.6 to 15.3 cm. In the television survey, a camera probe is used to inspect the borehole walls. Measurements of location, orientation, infilling width, and aperture of fractures are made on the television screen and recorded on computer data record sheets. All observations are recorded on video cassette tapes. With the acoustic televiewer, oriented images of fractures in the borehole walls are recorded on a strip–chart log and also on video cassette tapes. The images are displayed as if the walls were split vertically along magnetic north and spread out horizontally. Measurements of fracture characteristics are made on the strip–chart log, using a digitizing table and a microcomputer, and the data recorded on floppy diskettes. In both surveys, an inclined fracture is displayed as a sinusoidal curve, from which the apparent orientation of the fracture can be measured. Once the borehole orientation is known, the true orientation of the fracture can be computed from its apparent orientation. Computer analysis of the fracture data, provides a rapid assessment of fracture occurrence, fracture aperture, and statistically significant concentrations of fracture orientations. Key words: borehole, television survey, acoustic televiewer logging, fractures, distribution, orientation, aperture.

scholarly journals Genotyping of GATA4 Gene Variant (G296S) in Malaysian Congenital Heart Disease Subjects by Real-Time PCR High Resolution Melting Analysis

2013 ◽  
Vol 32 (2) ◽  
pp. 152-157
Author(s):  
Nora Fawzi ◽  
Ramachandran Vasudevan ◽  
Patimah Ismail ◽  
Mazeni Alwi ◽  
Ahmad Fazli Abdul Aziz ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
High Resolution ◽  
Congenital Heart ◽  
High Resolution Melting ◽  
New Technology ◽  
Cost Effective ◽  
Study Cohort ◽  
Hrm Analysis ◽  
Melting Analysis

Summary Background: Congenital heart disease (CHD) is the most common birth defect; however, the underlying etiology is unrecognized in the majority of cases. GATA-binding protein 4 (GATA4), a cardiac transcription factor gene, has a crucial role in the cardiogenesis process; hence, a number of heterozygote sequence variations were identified as a cause of CHD. G296S heterozygote variant is the most frequently reported GATA4 gene sequence alteration. This study aims to investigate the role of G296S variant of the GATA4 gene in Malaysian CHD subjects. Methods: We have investigated 86 Malaysian CHD subjects with cardiac septation defects for the presence of the GATA4 gene heterozygote variant (G296S) by the new technology of high resolution melting (HRM) analysis. Results: Genotyping of G296S (c.886G>A) by HRM analysis shows that all the sample genotypes were of the wild GG type genotype and the heterozygote mutant GA genotype was totally absent from this study cohort. Conclusions: The results of our study showed that the G296S variant of the GATA4 gene was not associated with the development of CHD in Malaysian subjects. The use of HRM analysis proved a cost-effective, high-throughput, specific and sensitive genotyping technique which eliminates the need for unnecessary sequencing.

A Friction Stir Welding Platform of Thin Plates

2012 ◽  
Vol 628 ◽  
pp. 206-210 ◽  
Author(s):  
Jia Liang Zhang ◽  
Bei Zhi Li ◽  
Xin Chao Zhang ◽  
Qing Xia Wang
Keyword(s):  
Friction Stir Welding ◽  
Virtual Instrument ◽  
Production Efficiency ◽  
Cost Effective ◽  
Field Analysis ◽  
Sensor Data ◽  
Thin Plates ◽  
Increase Production Efficiency ◽  
Friction Stir ◽  
Welding Processes

Friction stir welding processes involve many variables. Engineers and operators often find it difficult to effectively design or control it. The objective of this work is to develop a friction stir welding platform of thin plates to improve welding quality and to increase production efficiency. The study is conducted by using finite element modeling and temperature field analysis technology to obtain optimization parameters, and using virtual instrument, multi-sensor data fusion to monitor the force of the stirring spindle. Experiment results show that the developed platform can reach the requirements of processing quality and is cost-effective.

scholarly journals Developing the flexible library and information workforce.

2013 ◽  
Vol 19 (64) ◽  
pp. 24-26
Author(s):  
Anne Goulding ◽  
Evelyn Kerslake
Keyword(s):  
Labour Market ◽  
New Technology ◽  
Cost Effective ◽  
Labour Markets ◽  
Labour Market Flexibility ◽  
Defined Media ◽  
Flexible Workforce ◽  
Forward Looking

Flexibility is a vaguely defined media buzzword connoting the progressive, forward-looking workplace. Employers report that increased labour market flexibility has made them more cost-effective, efficient, better able to deal with customer and employee demands and the implementation of new technology. But what is happening to those workers who make up the flexible workforce? For a while in the 1980s it seemed that flexibility could do no wrong; now, however, the shortcomings of flexible labour markets are becoming more apparent.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

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