Use of quality maps in reservoir management

Martini, Roger; Schiozer, Denis José; Nakajima, L.

doi:10.1590/s1678-58782005000400016

Cited by 11 publications

(5 citation statements)

References 13 publications

(11 reference statements)

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“…The various static and dynamic parameters could be combined together to form the quality maps. Due to dynamic parameters such as oil saturation, the quality maps change during the production period [18,19]. The quality map is applied to solve decision making problems as an auxiliary analysis with parameters like cumulative oil production [20].…”

Section: Quality Mapsmentioning

confidence: 99%

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An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

Motahhari

Rafizadeh

Ahmadi

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.

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Section: Quality Mapsmentioning

confidence: 99%

“…Equation ( 17) is applied to normalize the decision matrix S. Then, equation ( 18) is utilized to compute the Shannon entropy. Afterward, equation (19)…”

Section: Shannon Entropymentioning

confidence: 99%

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

Motahhari

Rafizadeh

Ahmadi

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…As a result, these parameters are considered central to any waterflood optimization process. Traditionally, optimization of well placement and rates has been done using quality maps that indicate which regions of the reservoir have not been properly swept by the injected water (Cruz et al 1999;Badru 2003;Martini et al 2005). While this method has been useful to some extent, it cannot properly place wells in locations that take advantage of long-term high oil saturation or respond to the dynamics of reservoir fluid flow over a long period of time.…”

Section: Part Ii: a Strategy To Include Well Type And Number Of Wellsmentioning

confidence: 99%

On The Joint Optimization of Well Placement and Control

Awotunde

2014

All Days

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Of concern, in the development of oil fields, is the problem of determining the optimal locations of wells and the optimal controls to place on the wells. Extraction of hydrocarbon resources from petroleum reservoirs in a cost effective manner requires that the producers and injectors be placed at optimal locations and optimal controls be imposed on the wells. While the optimization of well locations and well controls plays an important role in ensuring that the net present value of the project is maximized, optimization of other factors such as well type and number of wells also plays important roles in increasing the profitability of investments. Until very recently, improving the net worth of hydrocarbon assets has been focused primarily on optimizing the well locations or well controls, mostly manually. In recent times, automatic optimization using either gradientbased algorithms or stochastic (global) optimization algorithms has become increasingly popular.We present a two-part series illustrating how to effectively optimize well placement and rates without dramatically increasing the size of the optimization problem. In this first part of the work, we present two approaches to reduce the number of design variables in well rate optimization. Polynomial and trigonometry models are proposed to model the change of well rates with time and these models are parameterized with coefficients which can be determined from any optimization method. Thus, instead of using well rates as the control variables in the optimization process, we use the parameters of equations that are able to model the variation of well rates with time. Each model has a specific number of coefficients to model the change in rate over time and each well has a distinct value for each coefficient in the model. This means that the number of variables is fixed regardless of the number of years of operating the wells. The total number of optimization parameters is thus the product of the number wells and the number of coefficients in the model. In this way the method allows us to find the average daily or monthly or annual well rate for each well. This significantly reduces the number of variables required and the time required in running the optimization algorithm. The method also makes it possible to use optimization algorithms that would otherwise break down when the number of design variables is too large.In the second part, a joint optimization approach to estimating optimal well locations, well rates, well type and well number is proposed. Our approach uses a set of well coordinates and a set of well controls as the optimization parameters. The set of well controls, however, covers both the negative and positive parts of the real line. The search interval of the well controls is divided into three parts, one part denoting the region where the well is an injector, a second part denoting the region where there is no well, and a third part denoting the region where the well is a producer. By this, the optimization algorithm is able to match every...

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“…Um dos pontos cruciais neste momento é determinar a estratégia de drenagem da jazida. Este é um processo complexo que envolve uma série de variáveis e na qual se busca a maximização do retorno econômico do reservatório (Martini et al, 2005).…”

Section: Estratégia De Drenagem De Um Campounclassified

Valor Da Completação Inteligente Na Conversão De Poços Através Do Uso De Opções Reais

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Use of quality maps in reservoir management

Abstract: The definition and management of a production strategy for petroleum fields is one of the

Cited by 11 publications

References 13 publications

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

On The Joint Optimization of Well Placement and Control

Valor Da Completação Inteligente Na Conversão De Poços Através Do Uso De Opções Reais

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