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

Kadem, Mohammad; Yateem, Karam; Al-Khamis, Mohammed N.

doi:10.2118/188133-ms

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…Fahruri et al (2007) used LSS methodology to improve the well-acidizing process, whereas Wijaya et al (2010) used LSS to enhance the well-fracturing process. Al Kadem et al (2017) used LSS to increase the quality and reliability of real-time data flowing from the field to engineers. Finally, Manlapaz Vargas and Scott (2017) discussed the utilization of LSS to reduce environmental impact in the operation of an oilfield service company.…”

Section: The Use Of Lean Tools and Techniquesmentioning

confidence: 99%

Adoption and implementation potential of the lean concept in the petroleum industry: state-of-the-art

Rachman

Ratnayake

2019

IJLSS

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Purpose A systematic literature review is performed to reveal the state-of-the-art in the implementation of lean principles in the petroleum industry. This paper aims to generate a conceptual framework and reveal research gaps with respect to lean concept application in the petroleum industry. Design/methodology/approach After formulating research questions, the search strategy is generated, followed by data extraction, literature review and synthesis of the results. The search covers any studies in peer-reviewed scientific journals and conference proceedings in the period 1990-2017 that discuss the implementation of the lean concept in the petroleum industry. Findings The lean concept has been used to improve operational and technical aspects, contractor/supplier relationships, team organization and project management practice in the petroleum industry. Based on the literature review, a conceptual framework is generated comprising four main elements: leadership and commitment from management, employee involvement, cooperation and trust with contractors/suppliers and lean project management. These elements are the pillars that are founded on lean philosophy and principles to support technical/operational improvement in the organization. The types of literature identified indicate that the subject of the study is still immature. Research limitations/implications This study focuses only on the upstream sector of the petroleum industry, which restricts the generalizability of the results to midstream and downstream businesses. Practical implications This paper provides knowledge and information regarding the current state of lean implementation in the petroleum industry. The developed conceptual framework provides general guidance for practitioners regarding lean implementation in the petroleum industry, and is also expected to support research on theory building. Originality/value Few studies have discussed the application of the lean concept in the petroleum industry. This paper contributes a platform for researchers and practitioners to comprehend how the lean concept has been applied in the petroleum industry, and provides a foundation for further studies on lean implementation in the petroleum industry.

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Section: The Use Of Lean Tools and Techniquesmentioning

confidence: 99%

Adoption and implementation potential of the lean concept in the petroleum industry: state-of-the-art

Rachman

Ratnayake

2019

IJLSS

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

Cited by 7 publications

References 6 publications

Adoption and implementation potential of the lean concept in the petroleum industry: state-of-the-art

Adoption and implementation potential of the lean concept in the petroleum industry: state-of-the-art

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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