Access to real time production and injection data available on a desktop greatly enhances a production optimization engineer's efficiency. This paper describes a smart management system utilized for retrieving and evaluating all flow assurance related data for more than 700 wells. These wells are located in both onshore & offshore fields operated by Cairn Oil & Gas, a vertical of Vedanta Limited. The paper discusses system architecture, interface details, system design, functionality, visualization, application and benefits of the system. To avoid unnecessary replication of data entry or retrieval, the system interfaces with other software systems. The software is also capable of direct hardware interfacing for data retrieval and system control. Currently the system connects to a range of software that includes Digital Oilfield Management System, Well Intervention Information System, and Laboratory Information Management System. The smart system is utilized as a single point for managing/ monitoring data from all such interfaces. Data may also be entered manually using standard templates, csv files, attachments (videos, photographs, documents, log files etc.) and spreadsheets. The system is fully web-enabled, real time and is used for performance monitoring of all flow assurance and well stimulation related data. The data includes stimulation treatment design, scale inhibitor squeeze performance, scale monitoring and prediction, water chemistry, chemical performance monitoring, corrosion prediction and monitoring etc. The input data is converted into information within the system by a powerful calculation/logic engine; enabling users to analyze issues/problems more efficiently and provide faster resolution. The system includes automatic alarms, emails, reporting, future projections, etc. Automated cumulative cost performance plots (Scorpion plots) are also inbuilt in the system and are used to evaluate cost-benefit of individual chemical treatments. These analyses enable well productivity to be improved and ineffectual operations to be abandoned. Examples of well stimulation assessment are included, which illustrate the value of analytics on performance improvement. The smart system covers raw data to dashboard information and setting realistic KPIs for future well interventions with respect to remediation and inhibition. Other examples include both in-built prediction tools (hydrogen sulphide phase equilibria) and linked software such as predicting scale deposition, squeeze lifetime modelling and corrosion rate prediction. Although running with live data retrieval, the system start up included upload of all historical data, thus ensuring utility from the outset. This software also has new modules added which enables the analysis of sporadic operation data (e.g. stimulation pumping analysis); cumulative cost production analysis and fracturing operations. This smart system will allow an engineer to make better informed decisions based on a consistent dataset with less time spent in gathering and analyzing data. As the system retains a full flow assurance history, it can be utilized for training new employees, bringing them rapidly upto speed with well operation characteristics. This paper aims at describing an overall approach that has been implemented to demonstrate automated performance monitoring and analysis of operations with an intention of improving oil realization while reducing cost for these activities.
In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the Base and Divergence Values features. An Artificial Neural Network Model (ANN) is developed and the result is validated manually. Another optimized Genetic Algorithm-Artificial Neural Network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.