Experimental Design as a Framework for Multiple Realisation History Matching: 
F6 Further Development Studies

Alessio, Laurent; Bourdon, Laurent; Coca, Spencer

doi:10.2523/93164-ms

Cited by 7 publications

(4 citation statements)

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“…Response surface models have been frequently used in petroleum engineering. There are number of articles available in uncertainty analysis of reservoir behavior (Damsleth, Hage and Volden 1992, Friedmann, Chawathe and Larue 2003, Cheong and Gupta 2005, well optimization (Zabalza, et al 2000, Landa and Güyagüler 2003, Valladao, et al 2013, and history matching (Eide, et al 1994, White and Royer 2003, Alessio, Bourdon and Coca 2005, Gupta, et al 2008, Cheng, Dehghani and Billiter 2008, Arwini and Stephen 2011).…”

Section: Alternative Approaches To Simulation Modelsmentioning

confidence: 99%

“…For example, response surface proxy models which use regression have a fixed structure with different components and coefficients. These coefficients then are adjusted during the training process; however, the structure of function (for example polynomial function) is fixed (Eide, et al 1994, White and Royer 2003, Alessio, Bourdon and Coca 2005, Gupta, et al 2008, Cheng, Dehghani and Billiter 2008, Arwini and Stephen 2011. In order to match these functional forms, hundreds of runs are needed.…”

Section: The Keys In Developing An Srmmentioning

confidence: 99%

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Assisted history matching using pattern recognition technology

Shahkarami¹,

Mohaghegh²,

Hajizadeh³

2018

IJOGCT

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Assisted History Matching Using Pattern Recognition Technology Alireza Shahkarami Reservoir simulation and modeling is utilized throughout field development in different capacities. Sensitivity analysis, history matching, operations optimization and uncertainty assessment are the conventional analyses in full field model studies. Realistic modeling of the complexities of a reservoir requires a large number of grid blocks. As the complexity of a reservoir increases and consequently the number of grid blocks, so does the time required to accomplish the abovementioned tasks.

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Section: Alternative Approaches To Simulation Modelsmentioning

confidence: 99%

Section: The Keys In Developing An Srmmentioning

confidence: 99%

Assisted history matching using pattern recognition technology

Shahkarami¹,

Mohaghegh²,

Hajizadeh³

2018

IJOGCT

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“…History matching is a challenging, non-unique solution exercise 1,2,3 . It becomes increasingly complex with increasing numbers of wells and reservoir heterogeneity.…”

Section: The Problem Statementmentioning

confidence: 99%

Multi-Realisation Full-Field History-Matching For CBM Resources a New Approach with Global Optimisation Coupled with Geo-Statistical Filtering

Alessio

Nasri

Pan

et al. 2013

SPE Asia Pacific Oil and Gas Conference and Exhibition

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CBM reservoirs are known to exhibit a large degree of variability in production characteristics and performance at the interwell space. This is a consequence of the variable nature of the fractured permeability network, but also other important measurable parameters such as gas content, isotherms, and much more difficult to measure ones such as porosity, relative permeability and matrix shrinkage.Once the field is on production for a period of time, operators often wish to perform a history-match of the asset, to determine the remaining production potential, and the most adequate future development activities. Traditionally, this exercise is conducted with numerical models.An unfortunate reality of history matching is the existence of multiple solutions, which will behave differently when forecasted in the future. This is a consequence of the fact that multiple parameters influence the match and their interdependency produces effects that are may compensate each other; many of these parameters are generally either poorly understood and/or highly variable at the inter-well scale. In addition, most of the critical parameters for dynamic performance are not measured in well logs, nor seismic (which often is not available) -only through more expensive activities such as coring and production testing.Given this reality, using the classical conventional gas workflows for the implementation of 3D history matched models for CBM fields is shown to be inefficient since the transition from the reservoir characterization to the static modelling is insufficiently constrained, and doesn't include the production performance of the existing wells.In order to adapt the classical workflows to the complex case of CBM fields, we introduce in this paper a filtering process based on geo-statistical constraints which uses the single well stochastic history matches to generate field matches and invest the matched drainage areas on the generation of "average properties" maps. These maps can thereafter be used as input into the infill drilling analysis and the resource assessment studies, but also introduced into the static model to condition the property model, and achieve a field history-match which much more closely reproduces the production behavior of individual wells. .

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“…Entretanto, esses métodos são limitados a problemas com um número bastante reduzido de parâmetros de ajuste do modelo. Há também muitos trabalhos aplicado ao problema de ajuste de histórico, como Kabir e Young [42] no campo "Meren", Alessio et al [43] em um campo de carbonato de "Luconia", Peake et al [44] no reservatório de "Minolish Oolite", e King et al [45] A despeito da grande variedade de métodos descritos na literatura, os métodos mais modernos buscam formular o problema de ajuste de histórico como um problema de inferência Bayesiana, onde o objetivo é estimar a distribuição de parâmetros incertos do modelo condicionado a dados ruidosos medidos em campo. Os métodos baseados no filtro de Kalman foram desenvolvidos dentro dessa filosofia Bayesiana.…”

Section: Métodos Baseados Em Planejamento De Experimentos E Modelosunclassified

Método Híbrido Baseado Em Filtro De Kalman E Modelos Generativos De Aprendizagem Profunda No Ajuste De Histórico Sob Incertezas Para Modelos De Fácies Geológicas

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Experimental Design as a Framework for Multiple Realisation History Matching: F6 Further Development Studies

Cited by 7 publications

References 0 publications

Assisted history matching using pattern recognition technology

Assisted history matching using pattern recognition technology

Multi-Realisation Full-Field History-Matching For CBM Resources a New Approach with Global Optimisation Coupled with Geo-Statistical Filtering

Método Híbrido Baseado Em Filtro De Kalman E Modelos Generativos De Aprendizagem Profunda No Ajuste De Histórico Sob Incertezas Para Modelos De Fácies Geológicas

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