Well Performance Monitoring Using Management by Exception Rules and Alerts

2021 ◽  
Author(s):  
Maruan Nadirov ◽  
Renat Sadyrbakiyev ◽  
Anton Skopich
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Continuous Production ◽  
Operating Conditions ◽  
Critical Conditions ◽  
Process Conditions ◽  
Full Potential ◽  
Time Process ◽  
Well Performance ◽  
Process Data

Abstract Wells in Tengiz and Korolev oil fields are equipped with data transmitting devices, which provide real-time process data used by Production engineers for continuous production monitoring and identification of unusual process conditions. Monitoring and analysis of each well performance becomes a tedious process with growing well inventory. Up until recently, real-time data from wellsite transmitters was not used to its full potential to simplify and automate well performance analysis. To improve the quality of daily well performance monitoring and detection of abnormal process conditions, sets of data rules have been developed to create alerts and screens with real-time process data managed by exception. These alerts and screens help to identify malfunctioning equipment and changes in operating conditions. Timely evaluation of critical conditions helps to proactively prepare a mitigation plan and prevent unscheduled well shutdowns. Data management by exception allows automatic filtering of big data sets and draws attention only to wells with deviations from the stable operating regime. Detailed review of highlighted well conditions helps to differentiate between malfunctioning equipment and actual changes in operating conditions. Fast identification of the issues allows taking preventative actions to maintain process stability of each producing well. Implementation of these tools significantly reduced number of unscheduled well shutdowns due to leaks in Surface Controlled Subsurface Safety Valve (SCSSV) hydraulic system and pneumatic valves control system. The screens also help to identify malfunctioning equipment including pressure and temperature gauges, pressure downhole gauges (PDHGs) and multiphase flow meters (MPFMs), as well as flow assurance issues such as hydrate formation. Developed data rules can be useful for any field equipped with data transmitting devices. This paper aims to share the best practices of using real-time operational data analytics to identify malfunctioning equipment, changing operating conditions and other process related issues to maintain stable production process.

Maximising Asset Value through Implementation of Dynamic Well Operating Envelop

2021 ◽  
Author(s):  
Rafael Islamov ◽  
Eghbal Motaei ◽  
Bahrom Madon ◽  
Khairul Azhar Abu Bakar ◽  
Victor Hamdan ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Oil And Gas ◽  
Operating Conditions ◽  
Well Performance ◽  
Time Dynamic ◽  
Well Model ◽  
Maximum Range ◽  
Facilities Design ◽  
Well Models

Abstract Dynamic Well Operating Envelop (WOE) allows to ensure that well is maintained and operated within design limits and operated in the safe, stable and profitable way. WOE covers the Well Integrity, Reservoir constraints and Facility limitations and visualizes them on well performance chart (Hamzat et al., 2013). Design and operating limits (such as upper and lower completion/facilities design pressures, sand failure, erosion limitations, reservoir management related limitations etc) are identified and translated into two-dimensional WOE (pressure vs. flowrate) to ensure maximum range of operating conditions that represents safe and reliable operation are covered. VLP/IPR performance curves were incorporated based on latest Validated Well Model. Optimum well operating window represents the maximum range of operating conditions within the Reservoir constraints assessed. By introducing actual Well Performance data the optimisation opportunities such as production/injection enhancement identified. During generating the Well Operating Envelops tremendous work being done to rectify challenges such as: most static data (i.e. design and reservoir limitations) are not digitized, unreliable real-time/dynamic data flow (i.e. FTHP, Oil/Gas rates etc), disintegrated and unreliable well Models and no solid workflows for Flow assurance. As a pre-requisite the workflows being developed to make data tidy i.e.ready and right, and Well Model inputs being integrated to build updated Well Models. Successful WOE prototype is generated for natural and artificially lifted Oil and Gas wells. Optimisation opportunities being identified (i.e. flowline pressure reduction, reservoir stimulation and bean-up) Proactive maintenance is made possible through dynamic WOE as a real time exceptional based surveillance (EBS) tool which is allowing Asset engineers to conduct the well performance monitoring, and maintain it within safe, stable and profitable window. Additionally, it allows to track all Production Enhancement jobs and seamless forecasting for new opportunities.

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Rainer Kurz ◽  
J. Michael Thorp ◽  
Erik G. Zentmyer ◽  
Klaus Brun
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Process Conditions ◽  
Plant Design ◽  
Well Performance ◽  
Worst Case ◽  
Gas Industry ◽  
Machine Performance ◽  
Equipment Sizing

Equipment sizing decisions in the oil and gas industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions, and others. Since the ultimate goal is to meet production commitments, the traditional method of addressing this is to use worst case conditions and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances, by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, however, they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will, therefore, usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbomachinery.

Correlating Time-Resolved Pressure Measurements with Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

2021 ◽  
pp. 1-40
Author(s):  
Eric DeShong ◽  
Benjamin Peters ◽  
Reid A. Berdanier ◽  
Karen A. Thole ◽  
Kamran Paynabar ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Feedback ◽  
Domain Knowledge ◽  
Performance Monitoring ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Pressure Measurements ◽  
Time Resolved ◽  
Learning Techniques ◽  
Purge Flow

Abstract Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.

A Novel Method to Compress Real-Time Process Data

2012 ◽  
Vol 430-432 ◽  
pp. 1298-1301
Author(s):  
Xiao Jian Zheng
Keyword(s):  
Real Time ◽  
Sampling Interval ◽  
Compression Algorithm ◽  
Time Process ◽  
Time Data ◽  
Process Data ◽  
Real Time Data ◽  
Novel Method

Most existing real-time data compressing algorithms are focused on dynamic and inconstancy of the process data, but a basic observation is ignored with some unexpectedness: on condition that sampling interval is not large, difference between amplitudes of real-time process data from two neighboring samples is relatively small, and most of data amplitudes are in the same range. In this paper we propose a compression algorithm based on the observation and experimentally evaluate the proposed approach and demonstrate that our algorithm is promising and efficient.

scholarly journals BUSINESS MODEL FRAMEWORK FOR USING REAL-TIME PROCESS DATA FOR DESIGN DECISION MAKING

2011 ◽  
Author(s):  
Matty Janssen ◽  
Paul Stuart
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Business Model ◽  
Environmental Data ◽  
Design Decision ◽  
Time Process ◽  
Time Data ◽  
Process Data ◽  
Model Framework ◽  
Business Decision

In recent years real-time data management systems have become commonplace at pulp and paper mills, and mills seek to use this important resource for improved operation of production facilities as well as for business decision-making. This paper presents a comprehensive and holistic approach to business modeling in which real-time process data, cost data, and environmental data are used in a “bottom-up” manner to exploit their potential for process decision-making. The paper describes a hypothetical case study in which the business model concept is illustrated by application to a process design problem at an integrated newsprint mill.

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

2021 ◽  
Author(s):  
Eric T. DeShong ◽  
Benjamin Peters ◽  
Reid A. Berdanier ◽  
Karen A. Thole ◽  
Kamran Paynabar ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Feedback ◽  
Domain Knowledge ◽  
Performance Monitoring ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Pressure Measurements ◽  
Time Resolved ◽  
Learning Techniques ◽  
Purge Flow

Abstract Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.

Automating Well Performance Monitoring of Real Time Data

2011 ◽  
Author(s):  
Ahmed Saleh Al-nuaim ◽  
Gary M. Williamson ◽  
Marwan M. Labban ◽  
Keith Richard Holdaway ◽  
Steffen Krug
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Well Performance ◽  
Time Data ◽  
Real Time Data
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