History matching of experimental and CMG-STARS results

Tunnish, Ahmed; Shirif, Ezeddin; Henni, Amr

doi:10.1007/s13202-018-0455-2

Cited by 18 publications

(18 citation statements)

References 2 publications

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“…However, since this initial model is highly uncertain, the production data are used to mitigate the model uncertainties. History matching is widely used in the oil and gas industry, and many different methods of performing it have been published in the literature (Chakra and Saraf 2016;Hamdi et al 2015;Makhlouf et al 1993;Obidegwu et al 2017;Oliver and Chen 2011;Oliver et al 2008;Tunnish et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

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History matching is the process of modifying a numerical model (representing a reservoir) in the light of observed production data. In the oil and gas industry, production data are employed during a history matching exercise in order to reduce the uncertainty in associated reservoir models. However, production data, normally measured using commercial flowmeters that may or may not be accurate depending on factors such as maintenance schedules, or estimated using mathematical equations, inevitably has inherent errors. In other words, the data which are used to reduce the uncertainty of the model may have considerable uncertainty in itself. This problem is exacerbated for gas condensate and wet gas reservoirs as there are even greater errors associated with measuring small fractions of liquid. The influence of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this paper, the effect of systematic and random flow measurement errors on history matching is investigated. Initially, 14 production data sets with different ranges of systematic and random errors, from 0 to 10%, have been employed in a history matching exercise for an oil reservoir and the results have later been evaluated based on a reference model. Subsequently, 23 data sets with errors ranging from 0 to 20% have been employed in the same process for a wet gas reservoir. The results show that for both cases systematic errors considerably affect history matching, while the effect of random errors on the considered scenarios is seen to be insignificant. Although reservoir model parameters in the wet gas reservoir were not as sensitive to the flow measurement errors as in the oil reservoir, for both cases, the future production forecast was significantly affected by the errors. Permeability was seen to be the most sensitive history matching parameter to the flow measurement errors in the oil reservoir, while for the wet gas reservoir, the most sensitive parameter was the forecast of future oil and gas production. Finally, considering the noticeable effect of systematic errors on both cases, it is suggested that flowmeter calibration and regular maintenance is prioritised, although the subsequent economic cost needs to be considered.

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Section: Introductionmentioning

confidence: 99%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

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“…Hence, the contribution of polymer/nanoparticle toward recovery factor should not be ignored. The secondary and tertiary flooding data results were employed as input data for (CMOST) assisted history‐matching functions, discussed in subsequent sections 18,19,27,40 …”

Section: Resultsmentioning

confidence: 99%

“…The STARS simulator package in CMG is widely employed compositional tool in the petroleum industry, with the capacity to develop reservoir models 24‐27 . The number of grids in I‐direction was varied as 10, 50, and 100 blocks to corroborate results of the numerical simulation study.…”

Section: Experimental and Simulationmentioning

confidence: 99%

“…Kazempour and others investigated the validity of multi‐phase component EOR systems in detail, and identified the dynamic behavior of fluid components existing within core‐flood model 18 . Tunnish et al successfully matched experimental flooding results using CMG to effectively tune relative permeability curves and predict the chemical fluid's ability to produce in‐situ crude oil 27 . Dahbag et al reported the performance of ionic liquid/surfactant flooding during chemical oil recovery processes, and found the results can be used to predict future scenarios 28 .…”

Section: Introductionmentioning

confidence: 99%

“…Dahbag et al reported the performance of ionic liquid/surfactant flooding during chemical oil recovery processes, and found the results can be used to predict future scenarios 28 . CMG tool is reliable and instrumental in evaluating the potential of conventional and modern EOR methods 17‐19,24‐28 …”

Section: Introductionmentioning

confidence: 99%

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Numerical simulation of enhanced oil recovery studies for aqueous gemini surfactant‐polymer‐nanoparticle systems

Pal

2020

AIChE Journal

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The article investigates the efficacy of gemini surfactant/polymer/nanoparticle flooding on chemical EOR. Initially, physicochemical behavior of aqueous chemical fluids were investigated via interfacial tension reduction, wettability alteration, adsorption, viscosity moderation and oil displacement experiments. During compositional analysis, Cartesian model with specified grid properties, injection flow-rate, well pattern, and rock-fluid characteristics was developed using CMG-STARS tool. Contour map analyses showed that oil saturation decreased from~80% (initial) to 31.96%, 30.68%, and 29.30% after {14-6-14 GS + chase water}, {14-6-14 GS + PHPA + chase water}, and {14-6-14 GS + PHPA + SiO 2 chase water} flooding respectively. Tertiary recoveries of 15-19% were achieved, depending on injected fluid composition. Experimental data were history matched via CMOST tool to achieve good matching of simulated results. The CMG flooding simulator provides a holistic approach to investigate oil displacement profiles, assess flooding recovery capabilities with near-accuracy and predict the feasibility of proposed chemical EOR projects.

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Flow and tracer transport in a single fracture at non-isothermal conditions

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2022

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History matching of experimental and CMG-STARS results

Cited by 18 publications

References 2 publications

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Numerical simulation of enhanced oil recovery studies for aqueous gemini surfactant‐polymer‐nanoparticle systems

Flow and tracer transport in a single fracture at non-isothermal conditions

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