How Reliable Is Fluid Gradient in Gas/Condensate Reservoirs?

Kabir, C.S.; Pop, Julian

doi:10.2118/99386-pa

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…In previous work, Kabir and Pop (2007) used the Student t-test to calculate the accuracy of compositional gradients of gascondensate reservoirs. Jackson et al (2007) and Zhou et al (2008) used a comparable method to determine if subtle pressure gradient differences are statistical or real.…”

Section: Measurements On Measurands Produced By Two Stable Processesmentioning

confidence: 99%

Using Large Digital Data Sets to Improve the Accuracy of Reservoir Pressure Measurements

Hassig

2013

All Days

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The abundance of digital calibration information can be leveraged through statistical mining to improve calibration methodologies in tool manufacturing. Historical tool metrology, unable to store massive data, relied on easily processed, summary performance metrics. However, with modern computing power, more computationally expensive methods of large data sets can be used to refine tool and process metrology, accuracy, and reliability. Empirical data from large sets of quartz memory gauges suggest that tools are consistently outperforming the accuracy specification derived from historical static calibration. This paper presents an alternative metrological methodology that provides additional insight into the accuracy of calibrated gauges. An analytic framework based on the Satterthwaite confidence interval (CI) is developed using an unpaired sample t-test and simultaneous field measurements from five stable pressure segments are used to validate the predicted variances between gauges. The methodology significantly improves measurement accuracy and reliability and is applicable to any instrumentation that undergoes a consistent, repeatable, and linear calibration.In addition to reevaluating existing tool specifications, the methodology can be integrated into metrological practices in the oil industry where large digital data sets and painstakingly accurate tools and calibration devices are commonly found. Presented are results from memory gauges, but by extension the method is applicable in other formation evaluation endeavors. The analytic framework predicts a factor of proportionality between the accuracy predicted by the CI and the traditional metrics of individually calibrated gauges. Specifically, a relationship is found between the 2-norm of the mean quadratic deviation (MQD) error of two individually calibrated pressure gauges. A case study of high-pressure, hightemperature (HPHT) gauges in five well tests in the North Sea and India confirm analytic predictions. The paper focuses on quartz memory gauges and improves absolute and relative accuracies of HPHT gauges by over 33% and 56%, accordingly. Comparable analysis can be applied to other tools to better evaluate metrology. The converse method is useful to determine a process' variance and bias -vital information to evaluate and compensate a facility, process, or device.

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Section: Measurements On Measurands Produced By Two Stable Processesmentioning

confidence: 99%

Using Large Digital Data Sets to Improve the Accuracy of Reservoir Pressure Measurements

Hassig

2013

All Days

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“…These new applications could include improved integration of pressure measurements and pressure gradient analysis with reservoir fluid and thermodynamic models. However such an approach requires good data quality control and data analysis, since pressure measurement problems, supercharging, wettability effects, and depth measurement errors can make it difficult to acquire representative pressure data and hence limit fluid gradient accuracy and reliability [6][7][8][9] . The presence of compositional gradients and depthdependent fluid property variations in hydrocarbon reservoirs has long been recognized.…”

Section: Introductionmentioning

confidence: 99%

Pressure Measurement and Pressure Gradient Analysis: How Reliable For Determining Fluid Density and Compositional Gradients?

Jackson

Carnegie

Dubost

2007

Proceedings of Nigeria Annual International Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractPressure-depth plots have been used for over thirty years to evaluate fluid density, fluid contacts, and pressure compartmentalization in formation tester pressure surveys. However in the Niger Delta region and other offshore deepwater environments, many reservoirs are multilayered and highly variable in terms of connectivity, permeability and fluid properties. Such complexity and reservoir heterogeneity means conventional pressure-depth plot and pressure gradient analysis of wireline pressure data is not easy, and identification of in-situ fluid type can be difficult. There is also mounting evidence for the presence of compositional gradients in the hydrocarbon columns of some reservoirs -this raises questions about the conventional approach to pressure gradient analysis and uncertainties in inferring fluid properties and contacts from pressure gradients.In this contribution using several field examples, we discuss and review formation pressure measurement techniques and data quality, and compare conventional and advanced methods of pressure gradient analysis and fluid contact determination. The interpretation techniques compared include traditional pressure-depth graphical methods, the excess-pressure method and statistical tests. Depth dependent fluid property variation from fluid gradients, PVT properties and EOS-models are compared and discussed. Guidelines are presented on how to interpret wireline pressure measurements in multilayered siliclastic reservoirs, perform connectivity and compositional gradient assessment. We describe how to improve on these interpretations by performing more advanced formation testing procedures; some of which are based upon new and emerging technology.

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Optimization of Jiyang depression block X shale condensate reservoir well spacing based on geology–engineering integration

Chen,

Xiao,

Jiang

et al. 2024

Natural Gas Industry B

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How Reliable Is Fluid Gradient in Gas/Condensate Reservoirs?

Cited by 12 publications

References 28 publications

Using Large Digital Data Sets to Improve the Accuracy of Reservoir Pressure Measurements

Using Large Digital Data Sets to Improve the Accuracy of Reservoir Pressure Measurements

Pressure Measurement and Pressure Gradient Analysis: How Reliable For Determining Fluid Density and Compositional Gradients?

Optimization of Jiyang depression block X shale condensate reservoir well spacing based on geology–engineering integration

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