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.

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.

Intelligent Operating Envelope Integrated with Automated Well Models Improves Asset Wide PCP Surveillance and Optimization

2021 ◽  
Author(s):  
Mohammed Al Sawafi ◽  
Antonio Andrade ◽  
Nitish Kumar ◽  
Rahul Gala ◽  
Eduardo Marin ◽  
...  
Keyword(s):  
Real Time ◽  
Optimum Operating ◽  
Well Performance ◽  
Optimum Operating Condition ◽  
Visualization System ◽  
Well Design ◽  
Well Model ◽  
Operational Activities ◽  
Petroleum Development ◽  
Well Models

Abstract Petroleum Development Oman (PDO) has been a pioneer in improving Well management processes utilizing its valuable human resources, continuous improvement and digitalization. Managing several PCP wells through Exception Based Surveillance (EBS) methodology had already improved PCP surveillance and optimization across assets. The key to trigger EBS was to keep Operating Envelope (OE), Design Limits updated in Well Management Visualization System (WMVS) after every change in operating speed (RPM), workover and new completion. The sustainable solution was required for automatic update of OEs, having well inflow potential and oil gain opportunities available for quicker optimization decisions for further improvements. PDO has completed a project automating PCP well modeling process where models are built and sustained automatically in Well Management System (WMS) for all active PCP wells, with huge impact on day-to-day operational activities. The paper discusses utilization of physics based well models from WMS to automatically update OE, identify oil gain potential daily and enable real time PCP performance visualization in WMVS. The integration of WMS and WMVS was completed to share data between two systems and automatically update well's OE daily. A tuned well model from WMS was utilized to provide well performance data and sensitivity analysis results for various RPMs. Among the various data obtained from WMS, live OE of torque and fluid above pump (FAP) for various speeds, operating limits, design limits, locked in potential (LIP) for optimization and pump upsize were utilized to process PCP well EBS and create live OE visualization. The visualization is created on a torque-speed chart where a live OE and FAP can be observed in provided picture with current RPM and torque with optimum operating condition. The project is completed after conducting successful change management across PDO assets and after thorough analysis of implementation following benefits were observed: 5% net gain of total PCP production is being executed with zero CAPEX using LIP reports. 50% of engineer's time was saved by updating OEs in WMVS automatically, reduction of false EBS and EBS rationalization. 200% improvement in PCP well performance diagnostics capabilities of Engineers. 15% CAPEX free optimization and pump upsize cases were identified based on well inflow potential. 100% visibility to PCP well's performance was achieved using well model. The visualization has supported engineers monitoring well performance in real time and easily identifying ongoing changes in well and pump performance. PCP well models have supported engineers in new PCP well design and pump upsize. The current efforts in utilizing real time well models, inferred production, automating processes to update OE is one more step toward Digitalization of PCP Surveillance and optimization and to achieve self well optimization for further improving operational efficiency.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

scholarly journals IMPACT OF GAS LIFT INJECTION RATE ON OIL PRODUCTION RATE

2021 ◽  
pp. 76-94
Author(s):  
Dr. Mohamed A. GH. Abdalsadig
Keyword(s):  
Real Time ◽  
Energy Demand ◽  
Oil And Gas ◽  
Injection Pressure ◽  
Production Operation ◽  
Well Performance ◽  
Time Data ◽  
Daily Work ◽  
Wellhead Pressure ◽  
Gas Lift

As worldwide energy demand continues to grow, oil and gas fields have spent hundreds of billions of dollars to build the substructures of smart fields. Management of smart fields requires integrating knowledge and methods in order to automatically and autonomously handle a great frequency of real-time information streams gathered from those wells. Furthermore, oil businesses movement towards enhancing everyday production skills to meet global energy demands signifies the importance of adapting to the latest smart tools that assist them in running their daily work. A laboratory experiment was carried out to evaluate gas lift wells performance under realistic operations in determining reservoir pressure, production operation point, injection gas pressure, port size, and the influence of injection pressure on well performance. Lab VIEW software was used to determine gas passage through the Smart Gas Lift valve (SGL) for the real-time data gathering. The results showed that the wellhead pressure has a large influence on the gas lift performance and showed that the utilized smart gas lift valve can be used to enhanced gas Lift performance by regulating gas injection from down hole.

scholarly journals Structure-property Relationship of Flavonoids as Potential Green Inhibitors for Oilfield Scales: A Mini-review

2021 ◽  
pp. 41-51
Author(s):  
Dominica Una ◽  
Dulu Appah ◽  
Joseph Amieibibama ◽  
William Iheanyi Eke ◽  
Onyewuchi Akaranta
Keyword(s):  
Oil And Gas ◽  
Operating Conditions ◽  
Inhibition Mechanism ◽  
Well Performance ◽  
Structure Property ◽  
Chemical Additives ◽  
Scale Inhibition ◽  
Continuous Application ◽  
Green Scale Inhibitors ◽  
Good Scale

Scale deposits are a significant flow assurance issue in oil and gas operation with huge financial consequences. Not only does scaling drastically impair well performance, but it also has the potential to permanently destroy formation and equipment. Scale inhibitors are commonly used to prevent the accumulation of scales. A good scale inhibitor should be stable at the minimum effective inhibitor concentration under imposed operating conditions without interfering with or being affected by other chemical additives. However, most conventional scale inhibitors that possess these attributes, do not meet environmental restrictions which make them unfavorable for continuous application, prompting the industry to focus more on developing eco-friendly substitutes. This paper reviews the various types of scale inhibitors and general scale inhibition mechanism, summarizes scale concepts and ultimately, assesses the potential of flavonoids from natural plants as potential green scale inhibitors.

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.

Technical Solution Improves Safety, Efficiency of Well Construction Offshore Australia

2021 ◽  
Vol 73 (10) ◽  
pp. 46-48
Author(s):  
Chris Carpenter
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Oil And Gas ◽  
New Technologies ◽  
Primary Objective ◽  
Asia Pacific ◽  
Design Stage ◽  
Technical Solution ◽  
Drilling Performance ◽  
Management Optimization

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202353, “Drilling-Performance and Risk-Management Optimization Offshore Australia: Improving Overall Safety and Efficiency of the Well-Construction Process,” by Chandrasekhar Kirthi Singam, Farshid Hafezi, and Clyde Rebello, Schlumberger, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20–22 October. The paper has not been peer reviewed. The emergence of real-time well construction performance-monitoring centers has improved the service delivery for operators across numerous offshore oil fields in Australia significantly. The complete paper details new technologies and work flows implemented for three Australian offshore wells, with the primary objective of improving drilling efficiency while managing associated risks. Additional objectives included optimizing daily operational performance, thus delivering time savings for the operator and highlighting areas of possible improvements. Introduction The paper describes a successful drilling campaign in a challenging field in the Timor Sea. It describes how data analysis, risk evaluation, and real-time performance monitoring can be influential in saving rig time and project success. As part of this project, a major operator in Australia decided to perform an infill drilling campaign involving three high-angle directional wells (J type) in a saturated, complex field. The campaign design stage was initiated in 2017 with a main objective of delivering the project within authority-for-expenditure (AFE) budget and with planning for all potential challenges. Technical Overview The technical solution (Fig. 1) was deployed using drilling-interpretation software and executed its work flows to achieve the required objectives.

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.

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