Integrated Reservoir and Production Management: Solutions and Field Examples

Boe, Arild; Nordtvedt, Jan-Erik; Schmidt, Harald S.

doi:10.2118/65151-ms

Cited by 4 publications

(1 citation statement)

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“…Several papers have presented improved well monitoring and reservoir description using continuous downhole data. [8][9][10][11][12] . However, a challenge at this level is preparing the data for continuous analysis by including data and processing validation algorithms.…”

Section: T Comparison Of Cera and Chevron Resultsmentioning

confidence: 99%

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Unneland

Hauser

2005

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Digital Oil Field is rapidly gaining attention within the oil and gas industry. Several oil and gas operators are working to develop their vision for an oil field of the future, testing new technologies, setting up programs and participating in industry events. The vision is an integrated approach allowing more real-time control of asset management. Many different names are used in the upstream industry to describe this trend; Smart Fields, Digital Oil Field, Next Generation Oilfield, Field of the Future, e-field, Instrumented Field and Intelligent Energy. However, there is still uncertainty as to what needs to be done and what value it will actually bring to the industry. Several operators and service companies are transitioning from the initial envisioning and abstract phase to projects creating measurable value for the company. In this paper, Chevron's efforts within the Digital Oil Field domain, including concept, business case, corporate governance, technology development, partnerships, deployment and field experience are presented.

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Section: T Comparison Of Cera and Chevron Resultsmentioning

confidence: 99%

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Unneland

Hauser

2005

SPE Annual Technical Conference and Exhibition

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Development of a Marginal Gas-Condensate Field Using a Novel Integrated Reservoir and Production Management Approach

Nygard

Kramer²,

Kulkarni³

et al. 2001

SPE Asia Pacific Oil and Gas Conference and Exhibition

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This paper outlines how data acquisition, data analysis, and control actions, can be implemented remotely and in real time to enhance the reservoir and production management process. System components, their integration, and the opportunity they offer to implement reservoir and production management work processes based on real-time data will be discussed and exemplified. We will show how such a system can be implemented on a field. For this, we will utilize the planned installation of such a system in the Huldra gas/condensate field in the Norwegian sector of the North Sea. We will describe how "closed-loop control" for reservoir and production management will be implemented on this field, and give examples from work processes on real-time data leading to faster and better decision support, and, finally, decision making and implementation. Introduction Over the past fifteen years, reservoir management has improved significantly in several ways. Most notably, two technology "waves" have made important contributions to increased value creation - namely reservoir modeling and simulation, and advanced drilling. Reservoir modeling and simulation allowed for detailed analyses of the reservoir, facilitating the understanding of the reservoir behavior in a much better way than before, and allowing for far better predictions. In fact, in the North Sea, this technology is regarded as being the single most important contributor to improved oil recovery from about the mid eighties and a decade onwards. The next wave was advanced drilling. It allowed for locating wells in undrained areas in a very accurate manner. Referring again to the North Sea, advanced drilling combined with reservoir modeling and simulation are considered as the most important factors for value adding from the mid nineties and onwards.

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Knowledge Discovery in Databases and Multiphase Flow Metering: The Integration of Statistics, Data Mining, Neural Networks, Fuzzy Logic, and Ad Hoc Flow Measurements Towards Well Monitoring and Diagnosis

Alimonti

Falcone²

2002

SPE Annual Technical Conference and Exhibition

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The usual approach to the interpretation of producing wells is based on mechanistic models for the simulation of steady state and transient flow regimes. However, there are significant reservations about convergence problems, computational limits, the need for extensive tuning on field data, the instability of boundary conditions, the limited applicability of existing multiphase flow models, and the uncertainties associated with choke valve models. The current industry standards are critically reviewed within this framework. The real-time monitoring of producing wells is recognised as the best way of optimising field performance. Monitoring a producing well implies the ability to track, in real-time, any changes in fluid composition, flow rates, or pressure and temperature profiles. Multiphase Flow Metering (MFM) plays a key role in this scenario. Such information, combined with the critical analysis of historical data from the well itself or from analogue wells, allows diagnosis of the system and prediction of future trends. However, field data per se' do not necessarily generate knowledge. This is particularly true for large databases, which are difficult to manipulate to provide suitable inputs for wellbore simulators. This paper suggests how MFM, Knowledge Discovery in Databases (KDD) and Fuzzy Logic (FL) can offer an alternative approach to the analysis of producing wells. KDD is the automated extraction of patterns representing knowledge implicitly stored in large information repositories. Distributed, ad-hoc field measurements (including MFM and downhole measurements) can be processed via data cleaning, data integration, data mining, artificial intelligence, and pattern evaluation. FL can then manage the resulting information in terms of flow assurance and production optimisation. The same techniques can also be extended to the reservoir and the production network, for an integrated approach to production system analysis

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Integrated Reservoir and Production Management: Solutions and Field Examples

Cited by 4 publications

References 0 publications

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Development of a Marginal Gas-Condensate Field Using a Novel Integrated Reservoir and Production Management Approach

Knowledge Discovery in Databases and Multiphase Flow Metering: The Integration of Statistics, Data Mining, Neural Networks, Fuzzy Logic, and Ad Hoc Flow Measurements Towards Well Monitoring and Diagnosis

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