The Implementation Phase: Improve the Intelligent Field Real-Time Data Reliability Using Key Performance Indices

Al-Amer, Abdulrahman A.; Al-Naser, Naser; Al-Kadem, Mohammad S.; Khamseen, Mohannad A. Abo

doi:10.2118/176150-ms

Cited by 5 publications

References 4 publications

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Enriching Field Real-Time Data Reliability via the Implementation of Lean Six Sigma Concept

Kadem

Yateem

Al-Khamis

2017

Day 4 Thu, April 27, 2017

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Nowadays, with the advanced technology, there is a large quantity of real time data that flows from the equipment in the field to the engineers’ desktop. The quality of data is, in most cases, questionable. A high quality data is required to be utilized in production workflows and technical studies. Failure to acquire reliable data will affect the calculations of production parameters, hence impacting the overall understanding of well performance. Therefore, monitoring data reliability and quality is essential. A project was initiated to tackle all various reliability issues for eight fields where a focus team was formulated. The team assessed the current data reliability and facilitated developing the action plans with the use of Lean Six Sigma concept which follows five phases; define, measure, analyze, improve and control. Multiple tools such as fishbone diagram and 5-WHY were used to identify the root causes for having reliability issues along with a correspondent solution (s) which aided developing a detailed implementation plan. The project goal is targeting an increase in overall data reliability in a six-month period. An anticipated increase of 5% to overall data reliability is to be achieved post to the tags deletion and re-mapping campaign. It is worth mentioning that the utilization of production workflows through effective monitoring of wells rate compliance and ESP is associated with remarkable cost saving. Securing high data reliability from various equipment will enable engineers to track the wells’ rates and status at their desktops. Moreover, effective monitoring of ESP performance will help preventing the occurrence of trips and optimize ESP operations in the field. Last but not least, effective data monitoring will ensure the upkeep of the Intelligent Field equipment.

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Enriching Field Real-Time Data Reliability via the Implementation of Lean Six Sigma Concept

Kadem

Yateem

Al-Khamis

2017

Day 4 Thu, April 27, 2017

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Smart Oilfield Technologies and Management: Maximizing Real-Time Surveillance and Utilization

Al-Kadem

Yateem

Amri

2018

SPE Annual Technical Conference and Exhibition

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Smart oilfield technologies and management real-time data surveillance, in terms of reliability and availability, has proven essential in the process of prolonging asset lifespan, improving asset integrity, and proactively preventing problems. This illustrates a leadership role in the integration of cutting-edge technologies by utilizing an Intelligent Field concept. Surveillance capitalizes on real-time data transmitted from Intelligent Field equipment, where mathematical algorithms and logic are automated and imposed. The application captures specific sets of data to help identify and analyze challenges associated with Intelligent Field equipment. Major prevailing benefits include, identifying systematic techniques to utilize automated diagnostics for reduction in human intervention, develop field level surveillance, cross-validating measurements through online modeling, and further enhance collaboration. This paper details the methodology, the outcome, the requirements, and considerations associated with effective real-time data utilization in energy industry applications. The platform allows business team members and their partners to communicate, collaborate, and coordinate activities in real time.

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Innovative Machine Learning Method to Locate the Root Cause of the Unreliable Data Coming from Intelligent Field Equipment

Wasfi

Alqasimi

Kadem

et al. 2019

Day 2 Tue, November 12, 2019

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Analyzing large amounts of continuous real-time data along with sustaining its reliability is a challenging task for engineers. Low reliability of data can prompt off base data examination results and make it difficult to make critical decisions in the oil and gas business, especially for the cases where the processing of data is continuous real-time data. One example is the continuous real-time data coming from Intelligent Field equipment. Intelligent field equipment for upstream can be a combination of Wellhead sensors, ESP, PDHMS, MPFM, SWC, SPFM, MOVs and H2S sensors. Intelligent Field equipment data follows specific transmission nodes as it flows from each sensor or instrument to a Remote Terminal Unit (RTU), to the Supervisory Control and Data Acquisition (SCADA) system, to a Plant Information (PI) data historian, and after appropriate filtration into the Exploration and Production (E&P) Corporate Database (Oracle), and finally into Petroleum Engineering developed applications. The unreliability of data can occur at any transmission nodes along this path. To discover the root cause of the unreliability, the Engineer must go through an exhaustive and lengthy process. Therefore, this paper introduces a promising methodology that tackles this challenge, by utilizing machine learning methods, to develop pattern recognition algorithms that recognize the transmission nodes where the unreliability of data appeared. Therefore, this paper aims to tackle two main challenges:Estimating data reliabilityDetecting the error location of unreliable data By utilizing Machine Learning methods to develop pattern recognition algorithms that recognizes the transmission nodes where the unreliability of data appeared. Two out of three machine learning algorithms were selected; Decision Tree and Gradient Boosting (GB). Decision Tree showed an accuracy of 94.6%, while Gradient Boosting showed higher accuracy of 96.4% when estimating data reliability and error location determination. The robustness of GB over decision tree is that GB construct and train trees all at once while the latter train each tree independently. Training all trees at once helps to learn faster and reduce the error dramatically.

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The Implementation Phase: Improve the Intelligent Field Real-Time Data Reliability Using Key Performance Indices

Cited by 5 publications

References 4 publications

Enriching Field Real-Time Data Reliability via the Implementation of Lean Six Sigma Concept

Enriching Field Real-Time Data Reliability via the Implementation of Lean Six Sigma Concept

Smart Oilfield Technologies and Management: Maximizing Real-Time Surveillance and Utilization

Innovative Machine Learning Method to Locate the Root Cause of the Unreliable Data Coming from Intelligent Field Equipment

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