Simple Tools for Forecasting Waterflood Performance

Can, Bunyamin; Kabir, C.S.

doi:10.2118/156956-ms

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…Yang (2009) used the same assumption of a semi-log relationship between the oil-water relative permeability ratio and water saturation and derived an analytic solution that relates oil fractional flow and volumetric sweep efficiency. Can and Kabir (2012) improved on Yang's model by correcting its performance at high WOR. Kabir and Izgec (2009) tried to identify high conductivity layers or channels between injector and producer pairs using a modified-Hall analysis.…”

Section: Two-phase Modelsmentioning

confidence: 99%

A large scale network model to obtain interwell formation characteristics

Gherabati

Hughes

White

et al. 2017

IJOGCT

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Section: Two-phase Modelsmentioning

confidence: 99%

A large scale network model to obtain interwell formation characteristics

Gherabati

Hughes

White

et al. 2017

IJOGCT

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“…It is assumed to be constant with respect to time. The time constant s j , another important model parameter, is defined as By definition, it is a characteristic time for the pressure wave to travel from injectors to a producer (Cao 2014). In Eq.…”

Section: The Capacitance/resistance Model (Crm)mentioning

confidence: 99%

“…One can find applications of the CRM in literature by Lee et al (2011), Parekh and Kabir (2011), Can and Kabir (2012), Nguyen (2012), Soroush et al (2013), and Tafti et al (2013), with respect to waterflooding, gasflooding, and other processes. Nevertheless, the CRM is a singlephase-flow model (Cao 2014), which history matches and predicts the total-production rates. Hence, a fractional-flow model must be implemented into the CRM to separate the oil production from the total production.…”

Section: Introductionmentioning

confidence: 99%

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

Cao

Luo

Lake

2015

SPE Reservoir Evaluation &Amp; Engineering

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Many empirical and analytical models were developed to forecast oil production. Empirical models (including data-driven models) can, for example, find correlations between oil cut and production, but they lack explicit knowledge of the physical behavior. Classic analytical models are loyal to reservoir physics. Nevertheless, they often require estimation of water saturation as a function of time, which is difficult to obtain for multiwell reservoirs. It is desirable to combine advantages of both empirical and analytical models and develop a physical-model-based method that uses field data to infer oil rate.In this paper, we propose to infer fractional-flow models from field data by use of the Koval (1963) theory. We inversely solved the Koval fractional-flow equation to obtain a relationship between water cut and dimensionless time. By history matching field water-cut data, two model parameters, the Koval factor and the producer-drainage volume, are estimated. Nevertheless, it is challenging to use the Koval approach as a predictive model directly because the injection contribution into each producer in a future-time horizon must be evaluated first.To address this issue, we combine the Koval approach with the capacitance/resistance model (CRM), which characterizes the injector/producer connectivities and response time. The material balance of fluids is established in a producer-based drainage volume to consider the contributions from nearby injectors and the time lag in production caused by reservoir/fluids compressibility. A regression approach is simultaneously advanced to minimize the model error. Because of robustly integrating the reservoir physical behavior and the data-driven approach, the combination of the Koval theory and the CRM can result in a synergy that leads to accurate oil-rate predictions.We validated this integrated method in synthetic homogeneous and heterogeneous reservoirs to test its reliability, and further applied it to a field case in western Venezuela. Case studies demonstrate that one can use this integrated model as a real-time tool to characterize interwell connection and to predict future oil production accurately.

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“…However, its application shows that the prediction error is very large and the application value is small. Since then, many scholars have done a lot of extended research on general waterflooding characteristic curve, mainly including the following aspects: the application scope and adaptability of existing waterflooding characteristic curve [38][39][40], theoretical derivation of existing waterflooding characteristic curve expression [41,42], correction of existing waterflooding characteristic curve [43], and analysis of influencing factors of waterflooding characteristic curve [44].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Flow-Stop Time in Deep-Water Volatile Oilfields: A Case Study of Akpo Oilfield in Niger Delta Basin

Kang

Liu

Li³

et al. 2021

Geofluids

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Due to the difference in oil and water density, the wellhead pressure continues to decrease with water-cut rising in deep-water volatile oilfields. Once it is close to the lower limit, the production well will stop flowing. This phenomenon seriously affects the production and recoverable reserves. By taking the dynamic relative permeability which can reflect the macroscopic movement of oil and water in the reservoir as an intermediate bridge, production performance has been combined with dominant reservoir factors, including reservoir structure, reservoir connectivity, and heterogeneity. By the statistical analysis of actual data, this paper clarified the quantitative relationships between dominant reservoir factors and production performance and established the refined prediction methods for production dynamics including water-cut and liquid production rate. A prediction method for the wellhead pressure was further established, and the flow-stop time of single well can be accurately predicted. The results can be used in annual production forecast and recoverable reserve evaluation. This method had been successfully applied in Akpo oilfields in the Niger Basin. The results show that the production dynamics are significantly affected by reservoir factors in deep-water turbidite sandstone reservoir and the prediction method considering reservoir factors will be much more applicable. In deep-water volatile oilfields, the flow-stop risk of the production well in middle and high water-cut stages is very great and is mainly affected by the water-cut and liquid production rate. Judging from the application effect of Akpo oilfields, this method has high prediction accuracy and can be used to guide optimization and adjustment in deep-water oilfields.

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Simple Tools for Forecasting Waterflood Performance

Cited by 17 publications

References 15 publications

A large scale network model to obtain interwell formation characteristics

A large scale network model to obtain interwell formation characteristics

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

A Prediction Method for Flow-Stop Time in Deep-Water Volatile Oilfields: A Case Study of Akpo Oilfield in Niger Delta Basin

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