Inline Drilling Fluid Property Measurement, Integration, and Modeling to Enhance Drilling Practice and Support Drilling Automation

2021 ◽  
Author(s):  
Sanjit Roy ◽  
Saiyid Z. Kamal ◽  
Richard Frazier ◽  
Ross Bruns ◽  
Yahia Ait Hamlat
Keyword(s):  
Real Time ◽  
Drilling Fluid ◽  
Measurement Data ◽  
Lessons Learned ◽  
Valuable Insight ◽  
Drilling Process ◽  
Time Data ◽  
Fluid Property ◽  
Real Time Data ◽  
Fluid Properties

Abstract Frequent, reliable, and repeatable measurements are key to the evolution of digitization of drilling information and drilling automation. While advances have been made in automating the drilling process and the use of sophisticated engineering models, machine learning techniques to optimize the process, and lack of real-time data on drilling fluid properties has long been recognized as a limiting factor. Drilling fluids play a significant function in ensuring quality well construction and completion, and in-time measurements of relevant fluid properties are key to automation and enhancing decision making that directly impacts well operations. This paper discusses the development and application of a suite of automated fluid measurement devices that collect key fluid properties used to monitor fluid performance and drive engineering analyses without human involvement. The deployed skid-mounted devices continually and reliably measure properties such as mud weight, apparent viscosity, rheology profiles, temperatures, and emulsion stability to provide valuable insight on the current state of the fluid. Real-time data is shared with relevant rig and office- based personnel to enable process monitoring and trigger operational changes. It feeds into real-time engineering analyses tools and models to monitor performance and provides instantaneous feedback on downhole fluid behavior and impact on drilling performance based on current drilling and drilling fluid property data. Equipment reliability has been documented and demonstrated on over 30 wells and more than 400 thousand ft of lateral sections in unconventional shale drilling in the US. We will share our experience with measurement, data quality and reliability. We will also share aspects of integrating various data components at disparate time intervals into real-time engineering analyses to show how real-time measurements improve the prediction of well and wellbore integrity in ongoing drilling operations. In addition, we will discuss lessons learned from our experience, further enhancements to broaden the scope, and the integration with operators, service companies and other original equipment manufacturer in the domain to support and enhance the digital drilling ecosystem.

Season Cycling Gas Storage in Stogit Fields. A Real-Time Data Transmission System

2021 ◽  
Author(s):  
Graciela Eva Naveda ◽  
France Dominique Louie ◽  
Corinna Locatelli ◽  
Julien Davard ◽  
Sara Fragassi ◽  
...  
Keyword(s):  
Natural Gas ◽  
Real Time ◽  
Fiber Optics ◽  
Data Transmission ◽  
Distribution Networks ◽  
Gas Storage ◽  
Transmission System ◽  
Lessons Learned ◽  
Time Data ◽  
Real Time Data

Abstract Natural gas has become one of the major sources of energy for homes, public buildings and businesses, therefore gas storage is particularly important to ensure continuous provision compensating the differences between supply and demand. Stogit, part of Snam group, has been carrying out gas storage activities since early 1960's. Natural gas is usually stored underground, in large storage reservoirs. The gas is injected into the porous rock of depleted reservoirs bringing the reservoir nearby to its original condition. Injected gas can be withdrawn depending on the need. Gas market demands for industries and homes in Italy are mostly guaranteed from those Stogit reservoirs even in periods when imports are in crisis. Typically, from April to October, the gas is injected in these natural reservoirs that are "geologically tested"; while from November to March, gas is extracted from the same reservoirs and pumped into the distribution networks to meet the higher consumer demand.  Thirty-eight (38) wells, across nine (9) depleted fields, are completed with downhole quartz gauges and some of them with fiber-optics gauges. Downhole gauges are installed to continuously measure and record temperature and pressure from multiple reservoirs. The Real Time data system installed for 29 wells is used to collect, transmit and make available downhole data to Stogit (Snam) headquarter office. Data is automatically collected from remote terminal units (RTUs) and transferred over Stogit (Snam) network. The entire system works autonomously and has the capability of being remotely managed from anywhere over the corporate Stogit (Snam) IT network. Historical trends, including fiber optics gauges ones, are visualized and data sets could be retrieved using a fast and user-friendly software that enables data import into interpretation and reservoir modeling software. The use of this data collection and transmission system, versus the traditional manual download, brought timely data delivery to multiple users, coupled with improved personnel safety since land travels were eliminated. The following pages describe the case study, lessons learned, and integrated new practices used to improve the current and future data transmission deployments.

Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents

2021 ◽  
Author(s):  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Hamdan Mohamed Alsaadi ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Predictive Analytics ◽  
Drilling Process ◽  
Time Data ◽  
Surface Parameters ◽  
Normal Behavior ◽  
Real Time Data ◽  
Productive Time

Abstract This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

Automatic Drilling Fluids Monitoring

2021 ◽  
Author(s):  
Knut Taugbøl ◽  
Bengt Sola ◽  
Matthew Forshaw ◽  
Arild Fjogstad
Keyword(s):  
Real Time ◽  
Coming Out ◽  
Data Transfer ◽  
Drilling Fluid ◽  
Drilling Fluids ◽  
Time Data ◽  
Control Center ◽  
Well Control ◽  
Fluid Properties ◽  
Operation Center

Abstract The drilling fluid is the primary barrier against well control incidents when drilling a well in conventional mode and the drilling fluid properties must be correct at all times to prevent well control incidents. Automatic drilling fluid monitoring through automated measuring techniques combined with real time data transfer into control center with 24/7 surveillance substantially improves this control compared to conventional methods relying on manual measurements with long sampling intervals. New measurement devices have been introduced to the industry which measure the drilling fluid properties of all fluid going into the well as well as fluid coming out from the well. Properties measured are among others density and a full rheology profile. The data are transferred to users on the rig as well as directly to onshore operation centers. This highly improves the fluid engineering, enabling a more precise diagnostician and treatment in real time. This also improves efficiency when performing displacements from one fluid system to another. This paper will present new units for automatic drilling fluids measurements and its use in offshore drilling. The surveillance of fluid properties and the use of data at an onshore operation center will be presented. The drilling fluid properties are also detrimental for drilling parameters such as ECD (equivalent circulating density), surge and swab pressures and hole cleaning properties and the added data will improve any estimation of such parameters. The paper will present experiences from use of these data into advanced real time hydraulic measurements and models for automatic drilling control and explain how this can improve safety in the drilling operations as well as improve the drilling efficiency.

Online Quality Monitoring of Plastic Parts Using Real-Time Data From an Injection Molding Machine

2020 ◽  
Author(s):  
Jonathan Loftis ◽  
Saeed Farahani ◽  
Srikanth Pilla
Keyword(s):  
Injection Molding ◽  
Real Time ◽  
Quality Monitoring ◽  
Valuable Insight ◽  
Least Squares Regression ◽  
Molding Machine ◽  
Time Data ◽  
Injection Molding Machine ◽  
Feature Extraction Method ◽  
Real Time Data

Abstract Every day-increasing connectivity and access to data can provide valuable insight to the plastics industry. While the amount of accessible data has been increasing, the means to process and store it efficiently while squeezing valuable process information out of it has not been prioritized. The increase in connectivity has led to much of this data being stored and used in cloud computing systems which can be both monetarily and computationally expensive. Motivated by this fact, the feasibility of using real-time data directly captured from injection molding machine is investigated in terms of their capabilities for online quality monitoring. Using the built-in sensors that are usually existed in the standard injection molding machines (barrel pressure, screw position, and clamp force) and a dimensional reduction method, models are derived to predict quality of injection molded parts (Weight, Thickness, and Diameter). The developed models show high predictive capability with R2 values ranging from 0.89–0.97. Moreover, the combination of the proposed feature extraction method and implementation of Partial Least Squares Regression (PLS) demonstrates that most of the computing for automatic quality control can be done on local edge computing hardware with a significantly summarized data, and only control commands need to be sent through the cloud.

scholarly journals Developing a smartphone app with UK migrants for UK migrants: lessons learned from focus group work

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R Burns ◽  
G C G Hugenholtz ◽  
V Kirkby ◽  
N Elsay ◽  
R W Aldridge
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Well Being ◽  
Lessons Learned ◽  
Migrant Population ◽  
Time Data ◽  
Longitudinal Data Collection ◽  
Real Time Data ◽  
The Uk ◽  
App Design

Abstract Background In 2018, 14% of people living in the UK were born abroad, yet we have a limited understanding of the broader determinants of their health. To address this knowledge gap, the Health on the MovE (HOME) smartphone application (app) study was conceived. Through app-based surveys, the study will examine how risk factors for health and well-being are distributed among migrants and how these vary over time since migration to the UK. There is a lack of research addressing the development of apps for longitudinal data collection in the general population - and we did not find any in migrant groups. Methods To better inform the design of the HOME app study, three workshops were held in 2018 and 2019, involving both migrants and App development experts. We used a semi-structured interview schedule focused on five themes: smartphones, apps and research, HOME app wireframe (screen-by-screen review of the app), types of surveys and survey schedules, resource section content, and participant engagement strategies. The participants were purposively sampled to reflect the migrant population arriving in the UK from non-EU countries. Results Migrants reported high smartphone use and were positive about the app design and app-based research. Concerns around privacy and data protection were highlighted and limits were suggested for the frequency of surveys and the number of questions used. Mental health was identified as a key topic for research. Participants requested the inclusion of resources concerning asylum claim procedures and immigrant and labour laws. Migrants advised that study recruitment material should clearly state the purpose and scope of the research and requested regular feedback on study outcomes. Conclusions The workshops provided important feedback and facilitated the co-production of the HOME app. Overall findings suggest that the study would be both acceptable to the migrant population and feasible for real-time data collection. Key messages The process identified potential barriers to the acceptability and feasibility of an app-based study for real-time data collection in the UK migrant population. Organising workshops with migrants allowed for an iterative process of co-production of the HOME app. Their critical comments resulted in subsequent changes to the app design and study methodology.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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