Mega I-Field Application in the World's Largest Oil Increment Development

Al-Mulhim, Waleed; Al-Faddagh, Hussain Ali; Al-Shehab, Mahdi Abdulla; Shamrani, Sultan S.

doi:10.2118/128837-ms

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…The majority are production monitoring optimization oriented, and others include drilling and completion activities (Abdulaziz et al, 2010;Waleed et al, 2010). The majority are production monitoring optimization oriented, and others include drilling and completion activities (Abdulaziz et al, 2010;Waleed et al, 2010).…”

Section: Dof Definitionmentioning

confidence: 99%

Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

Saputelli¹,

Bravo

Nikolaou

et al. 2013

All Days

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In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations. These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.Since the appearance of computers and the internet, many enabling technologies entered the oil-patch. Over the years, various areas improved as a result of significant commercial, corporate and academic efforts. However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management. This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned. Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions. These are presented in four interrelated areas: people, automated workflows, processes and technologies.

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Section: Dof Definitionmentioning

confidence: 99%

Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

Saputelli¹,

Bravo

Nikolaou

et al. 2013

All Days

View full text Add to dashboard Cite

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Maximizing the Value of the Intelligent Field: Experience and Prospective

et al. 2012

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Our industry has embarked largely on implementing the intelligent field initiative in recent years, which is a clear indication of the surge to adapt to new methods, processes and workflows to manage existing and new fields. The capacity at which this technology was implemented worldwide is a sign of a strong buy-in from the industry that this technology will deliver on its promises in terms of maximizing hydrocarbon production, recovery, profits, or health, safety and environmental compliance. Saudi Aramco is considered to be a leader in deploying and utilizing the intelligent field technology to maximize the value of hydrocarbon reservoirs. This paper discusses the implementation of intelligent field initiative in Saudi Arabian fields and particularly in the world's largest intelligent field, the Khurais complex. It highlights the huge infrastructure to measure and transmit data and to manage and control production in this field. As a result of the lessons learned from this experience, the classical reservoir management concepts are going through a major transformation to adapt to a fundamental change in data acquisition and production controls. Moreover, the engineers were faced with a new challenge in the reservoir monitoring process and overall field optimization, which requires a paradigm shift in analysis, interpretation, and decision making to maximize the value of the intelligent field. So far, the industry's efforts to utilize intelligent field technology are mainly focused at well level to optimize production rate and ensure target rate compliance. Saudi Aramco's efforts are going beyond the well level to optimize reservoir performance and enhance oil recovery. This paper illustrates some of those efforts, which resulted in a better understanding of well deliverability and reservoir connectivity. In addition, this paper highlights the major challenges facing operating companies, to manage those fields. It provides methods and workflows to mitigate these challenges and maximize the value of intelligent fields and fully utilize the existing infrastructure and capabilities.

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Application of Best in Class Reservoir Management Practices in the Production Startup of a New Giant Field Development through Integration of Real Time Data: A Case Study

2014

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The development team successfully met the production startup milestone for a giant carbonate oil field. Central to the story of this significant accomplishment was the application of best-in-class reservoir management practices through integration of real-time data. In this field development, Saudi Aramco invested the necessary resources to bring Intelligent Field capabilities to its field operations. The challenge to fully utilize real-time data for prudent reservoir management practices prior to field startup and during the pre-water injection phase was met by the development team as described in this case study. This paper describes the nature of the reservoir development and puts emphasis on the advantageous role that real-time data acquisition has played in allowing key reservoir management objectives to be met. The field is equipped with state-of-the-art data sensing and transmission capabilities. Examples of key reservoir management goals that will be discussed in this paper include maintaining reservoir pressure, water injection efficiency assessments, dynamic reservoir characterization process, real-time reservoir simulation model calibration process, and the concurrent release of field production/injection guidelines.

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Mega I-Field Application in the World's Largest Oil Increment Development

Cited by 7 publications

References 3 publications

Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations

Maximizing the Value of the Intelligent Field: Experience and Prospective

Application of Best in Class Reservoir Management Practices in the Production Startup of a New Giant Field Development through Integration of Real Time Data: A Case Study

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