Digital Oil Field: Real Time pH Monitoring and Automatic Neutralization System Using Smart Phone/Watch/PC and Microcontroller in Hydrogen Sulfide Formations

2020 ◽  
Author(s):  
Hussein Basim Alhandhal
Keyword(s):  
Hydrogen Sulfide ◽  
Real Time ◽  
Ph Monitoring ◽  
Smart Phone ◽  
Oil Field

A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

2018 ◽  
Author(s):  
Kyle Plunkett
Keyword(s):  
Organic Chemistry ◽  
Augmented Reality ◽  
Real Time ◽  
Real World ◽  
Smart Phone ◽  
Practical Method ◽  
Virtual Expert ◽  
Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

ZERON 25

Alloy Digest ◽  
1995 ◽  
Vol 44 (3) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
Hydrogen Sulfide ◽  
Mechanical Strength ◽  
Physical Properties ◽  
Tensile Properties ◽  
Heat Treating ◽  
Oil Field ◽  
Localized Corrosion ◽  
Oil Gas

Abstract ZERON 25 is an alloy developed to combat severe oil field duties where oil, gas, water are contaminated with high levels of chlorides and hydrogen sulfide. The alloy has good mechanical strength and resistance to localized corrosion. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: SS-584. Producer or source: Weir Material Services Ltd.

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

Study and Comparison of Vibration-Based Intrusive and Non-Intrusive Sand Monitoring System

2013 ◽  
Vol 701 ◽  
pp. 440-444
Author(s):  
Gang Liu ◽  
Peng Tao Liu ◽  
Bao Sheng He
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Oil Production ◽  
Oil Wells ◽  
Oil Field ◽  
Sand Production ◽  
The Status

Sand production is a serious problem during the exploitation of oil wells, and people put forward the concept of limited sand to alleviate this problem. Oil production with limited sanding is an efficient mod of production. In order to complete limited sand exploitation, improve the productivity of oil wells, a real-time sand monitoring system is needed to monitor the status of wells production. Besides acoustic sand monitoring and erosion-based sand monitoring, a vibration-based sand monitoring system with two installing styles is proposed recently. The paper points out the relationships between sand monitoring signals collected under intrusive and non-intrusive installing styles and sanding parameters, which lays a good foundation for further study and actual sand monitoring in oil field.

scholarly journals The Necessity of Mobile Phone Technologies for Public Health Surveillance in Benin

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yaovi M. G. Hounmanou ◽  
Murielle S. S. Agonsanou ◽  
Victorien Dougnon ◽  
Mahougnon H. B. Vodougnon ◽  
Ephraim M. Achoh ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Smart Phone ◽  
Cross Sectional Study ◽  
Health Surveillance ◽  
Medical Professionals ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
The Public ◽  
Phone Calls

A cross-sectional study was conducted in March 2016 to assess the need of mobile phone technologies for health surveillance and interventions in Benin. Questionnaires were administered to 130 individuals comprising 25 medical professionals, 33 veterinarians, and 72 respondents from the public. All respondents possess cell phones and 75%, 84%, and 100% of the public, medical professionals, and veterinarians, respectively, generally use them for medical purposes. 75% of respondents including 68% of medics, 84.8% of veterinarians, and 72.2% of the public acknowledged that the current surveillance systems are ineffective and do not capture and share real-time information. More than 92% of the all respondents confirmed that mobile phones have the potential to improve health surveillance in the country. All respondents reported adhering to a nascent project of mobile phone-based health surveillance and confirmed that there is no existing similar approach in the country. The most preferred methods by all respondents for effective implementation of such platform are phone calls (96.92%) followed by SMS (49.23%) and smart phone digital forms (41.53%). This study revealed urgent needs of mobile phone technologies for health surveillance and interventions in Benin for real-time surveillance and efficient disease prevention.

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

Digital Workflow to Enhance Reservoir Management Strategies for A Complex Oil Field Through Real Time and Advanced Engineering Monitoring Solution

2021 ◽  
Author(s):  
Julieta Alvarez ◽  
Oswaldo Espinola ◽  
Luis Rodrigo Diaz ◽  
Lilith Cruces
Keyword(s):  
Real Time ◽  
Reservoir Characterization ◽  
Management Strategies ◽  
Reservoir Management ◽  
Gas Production ◽  
Oil Field ◽  
Oil Reservoirs ◽  
Production Operation ◽  
Digital Workflow ◽  
Operational Decisions

Abstract Increase recovery from mature oil reservoirs requires the definition of enhanced reservoir management strategies, involving the implementation of advanced methodologies and technologies in the field's operation. This paper presents a digital workflow enabling the integration of commonly isolated elements such as: gauges, flowmeters, inflow control devices; analysis methods and data, used to improve scientific understanding of subsurface flow dynamics and determine improved operational decisions that support field's reservoir management strategy. It also supports evaluation of reservoir extent, hydraulic communication, artificial lift impact in the near-wellbore zone and reservoir response to injected fluids and coning phenomenon. This latest is used as an example to demonstrate the applicability of this workflow to improve and support operational decisions, minimizing water and gas production due to coning, that usually results in increasing production operation costs and it has a direct impact decreasing reservoir energy in mature saturated oil reservoirs. This innovative workflow consists on the continuous interpretation of data from downhole gauges, referred in this paper as data-driven; as well as analytical and numerical simulation methodologies using real-time raw data as an input, referred in this paper as model-driven, not commonly used to analyze near wellbore subsurface phenomena like coning and its impact in surface operation. The resulting analyses are displayed through an extensive visualization tool that provides instant insight to reservoir characterization and productivity groups, improving well and reservoir performance prediction capabilities for complex reservoirs such as mature saturated reservoirs with an associated aquifer, where undesired water and gas production is a continuous challenge that incorporates unexpected operational expenses.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

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