Optimizing oil and gas field management through a fractal reservoir study model

Habib, Mukhtar; Yao, Guangqing; Xie, Chaoqun; Charles, Samba Prisca; Jakada, Hamza; Danlami, Musa Salihu; Ahmed, Hafizullah Abba; Omeiza, Ibrahim Abdullateef

doi:10.1007/s13202-016-0252-8

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Therefore, history matching does not merely deal with the uncertainty in the system, but also with the uncertainty in its own inputs which can potentially affect the results. Although different types of reservoir uncertainty have been comprehensively studied previously (Abdollahian et al 2018;Babak and Deutsch 2008;Habib et al 2017;Mozaffari et al 2017;Stephen and Macbeth 2008;Tavassoli et al 2004;Xu et al 2018), the uncertainty in the observed data has not drawn the attention of researchers so far.…”

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

View full text Add to dashboard Cite

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Separation of productivity index zones using fractal models to identify promising areas of fractured reservoir rocks

Afzal

Abdideh

Saein

2023

J Petrol Explor Prod Technol

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Identifying geological characteristics such as rock types and fractures is an important step in fractured reservoirs’ modeling and developing oil and gas fields. The productivity index (PI) is an essential parameter for this purpose. There are different methods for separating and identifying rock types and fractures, including simple statistical methods and complex fractal methods based on the spatial structure of the data. In this study, rock areas were isolated after modeling the PI parameter in a rock reservoir in southern Iran by ordinary kriging estimation. Then, the fractal concentration–area (C–A) and concentration–number (C–N) methods were used to classify the PI zones. The C–A fractal analysis revealed six different rock types and zones, and the C–N fractal method indicated four anomalies based on PI data in the studied reservoir rock. Based on the C–N and C–A models, the parts with PI ≤ 44 and PI ≤ 63, respectively, correspond to the production of wells from the reservoir rock matrix in this oil field and PI ≥ 223 include the production of wells at the fracture network of the reservoir rock. Fractal modeling indicates that the highest PI values occurred in the southeast and northwest parts of the studied oil field, suggesting better reservoir rock quality in this area. This problem is attributed to the presence of faults and the accumulation of fractures in these areas, which increases reservoir rock’s PI and permeability. The present study showed that multifractal methods are a very accurate method for separating all types of rock types in the reservoir and it separates things that are not visible in other methods such as petrophysical methods. The anomalies and communities identified for the PI parameter with these methods are well confirmed by geological evidence, especially the impact of fractures, faults and other diagenesis factors in the reservoir rock.

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Structural and stratigraphic controls on reservoir architecture: A case study from the lower Oligocene Vicksburg Formation, Brooks County, Texas

Turner,

Ahmed,

Bissell

et al. 2024

Marine and Petroleum Geology

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Optimizing oil and gas field management through a fractal reservoir study model

Cited by 4 publications

References 32 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

Separation of productivity index zones using fractal models to identify promising areas of fractured reservoir rocks

Structural and stratigraphic controls on reservoir architecture: A case study from the lower Oligocene Vicksburg Formation, Brooks County, Texas

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