Analytical tools aid understanding of history-matching effort in a fractured reservoir

Kabir, C.S.; Boundy, F.

doi:10.1016/j.petrol.2011.05.018

Cited by 10 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this analysis, consistency in matching pressure, rate, and cumulative production confirms data quality. When the solution divergence occurs after a certain time period, the notion of virtual flowmetering may be invoked (Kabir and Boundy 2011) to gain the desired consistency. Overall, there is no simple answer; one needs to use the fit-for-purpose tools to discern data quality and consistency.…”

Section: Parameter Estimation and Illustrative Field Examplesmentioning

confidence: 99%

Estimating Drainage-Area Pressure With Flow-After-Flow Testing

Kabir

Elgmati

Reza

2012

SPE Reservoir Evaluation &Amp; Engineering

View full text Add to dashboard Cite

Estimating the average drainage-area pressure ( p av ) of individual wells is a cornerstone to any reservoir-management practice. Yet conventional methods do not always offer reliable solutions to this vexing problem. This study shows that transient flow-afterflow (FAF) testing offers an excellent opportunity to establish p av in a time-lapse mode, when conducted following operational shutdowns. Instrumented wells are natural candidates for FAF testing.Real-time surveillance offers the opportunity to perform rate-transient analysis that results in drainage volume and, consequently, p av . However, gathering quality rate data commensurate with pressure over a long producing period is fraught with uncertainty, which raises questions about the validity of the p av so obtained. In addition, continuous changes in drainage-boundary conditions pose modeling challenges with a given reservoir model. Therefore, the independent estimation of p av cannot be overemphasized. This paper presents a theroretical framework for transient FAF testing and also shows a pragmatic approach to handling pressure/rate data incoherence.

show abstract

Section: Parameter Estimation and Illustrative Field Examplesmentioning

confidence: 99%