Permeability Predictions in Carbonate Reservoirs Using Optimal Non-parametric 
Transformations: An Application at the Salt Creek Field Unit, Kent County, TX

Barman, Indranil; Sharma, Abhinav; Walker, Richard F.; Datta‐Gupta, Akhil

doi:10.2523/39667-ms

Cited by 5 publications

(5 citation statements)

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“…4 The superior predictive ability of our proposed approach is verified using blind tests. Conventional multiple regression did not provide satisfactory permeability predictions in this field.…”

Section: Applications and Resultsmentioning

confidence: 83%

“…[3][4][5][6][22][23][24][25][26][27] Generalized Additive Models (GAM). We utilize non-parametric regression techniques that do not require a priori assumptions regarding functional forms to model the data.…”

Section: Permeability Correlationmentioning

confidence: 99%

“…4 Second approach is based on classification using lithofacies information. In the first approach, the well log dataset is divided into 6 different stratigraphical zones.…”

Section: Comparison With Other Techniques For Permeability Predictionmentioning

confidence: 99%

“…4 Other approaches have used lithofacies information identified from cores and also the concept of hydraulic flow units (HFU's). The simplest approach utilizes flow zones or reservoir layering.…”

Section: Introductionmentioning

confidence: 99%

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Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

Lee¹,

Datta‐Gupta²

1999

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWe propose a two-step approach to permeability prediction that utilizes non-parametric regression in conjunction with multivariate statistical analysis. First we classify the well log data into electrofacies types. This classification does not require any artificial subdivision of the data population but follows naturally based on the unique characteristics of well log measurements reflecting minerals and lithofacies within the logged interval. A combination of principal component analysis, model-based cluster analysis and discriminant analysis is used to characterize and identify electrofacies types. Second, we apply non-parametric regression techniques to predict permeability using well logs within each electrofacies. Three non-parametric approaches are examined viz. alternating conditional expectations (ACE), generalized additive model (GAM) and neural networks (NNET) and the relative advantages and disadvantages are explored.We have applied the proposed technique to a highly heterogeneous carbonate reservoir in the Permian Basin, west Texas: Salt Creek Field Unit (SCFU). The results are compared with three other approaches to permeability predictions that utilize data partitioning based on reservoir layering, lithofacies information and hydraulic flow units. An examination of the error rates associated with discriminant analysis for uncored wells indicates that data classification based on electrofacies characterization is more robust compared to other approaches. For permeability predictions, the ACE model appears to be the best among the three nonparametric approaches.

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Section: Applications and Resultsmentioning

confidence: 83%

Section: Permeability Correlationmentioning

confidence: 99%

“…4 Second approach is based on classification using lithofacies information. In the first approach, the well log dataset is divided into 6 different stratigraphical zones.…”

Section: Comparison With Other Techniques For Permeability Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

Lee¹,

Datta‐Gupta²

1999

Proceedings of SPE Annual Technical Conference and Exhibition

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“…The applications, in complex carbonate reservoirs, have shown outstanding promise in the management of many patterns of heterogeneity in rock properties (Barman et al, 1998;Lee et al, 2002). Nevertheless, it remains significantly difficult to identify sharp local variations in a reservoir property caused by abrupt changes in the depositional environment (Huang et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Comparison between permeability estimates in a carbonate reservoir of Campos Basin using empirical and mathematical approaches

Carrasquilla

Mureb

2021

Braz J Geophys

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Abstract. The permeability estimate, using geophysical well logs, is an important and difficult task in a reservoir characterization, which is usually done with empirical models or statistical regression. Thus, logs were used to achieve this goal in two wells of a carbonate reservoir in Campos Basin, together with geological information and laboratory measurements on rock samples. The test Well A10 was zoned in electrofacies and models were built for each one. Next, the hydraulic flow units were defined and correlated to the logs and to the electrofacies. The same was done in a blind test in Well A3. The Principal Component Analysis, Cluster and Discriminant techniques, Multiple Linear Regression, Alternate Conditional Expectation and Hydraulic Flow Unit were used. The quality of the estimates was calculated using the Mean Absolute Error and the Coefficient of Determination. At Well A10, the Hydraulic Flow Unit was the most promising approach. The Alternate Conditional Expectation, without zoning, was the closest to experimental laboratory data in Well A3. These results indicate that all methods are feasible in inferring permeability, however, an inadequate classification of zones can lead to erroneous estimates.Keywords: carbonate reservoir, permeability estimates, multivariate statistics, well logs, laboratory measurements. Comparação entre estimativas da permeabilidade em um reservatório carbonático da Bacia de Campos usando abordagens empíricas e matemáticasResumo. A estimativa da permeabilidade, usando perfis de poços, é uma tarefa importante e difícil na caracterização de reservatórios, a qual é normalmente feita com modelos empíricos ou regressão estatística. Com esse fim, os perfis de poços juntamente com informações geológicas e medições de laboratório em amostras de rocha foram usados em dois poços de um reservatório carbonático da Bacia de Campos. O poço de teste A10 foi zoneado em eletrofácies e modelos foram construídos para cada uma. As unidades de fluxo hidráulico foram definidas e correlacionadas aos perfis a às eletrofácies. Isso foi feito num teste cego no poço A3. As técnicas Análise de Componentes Principais, de Cluster e Discriminante, Regressão Linear Múltipla, Expectativa Condicional Alternada e Unidade de Fluxo Hidráulico foram utilizadas. A qualidade das estimativas foi calculada com o Erro Médio Absoluto e o Coeficiente de Determinação. No Poço A10, a Unidade de Fluxo Hidráulico foi a abordagem mais promissora. A Expectativa Condicional Alternada, sem zoneamento, foi a técnica mais próxima dos dados experimentais de laboratório no poço A3. Esses resultados indicam que todos os métodos são viáveis na inferência da permeabilidade, porém, uma classificação inadequada das zonas pode levar a estimativas errôneas.Palavras-chave: reservatório carbonático, estimativas de permeabilidade, estatísticas multivariadas, perfis de poços, medições laboratoriais.

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Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

Lee

Datta‐Gupta

1999

SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

We propose a two-step approach to permeability prediction that utilizes non-parametric regression in conjunction with multivariate statistical analysis. First we classify the well log data into electrofacies types. This classification does not require any artificial subdivision of the data population but follows naturally based on the unique characteristics of well log measurements reflecting minerals and lithofacies within the logged interval. A combination of principal component analysis, model-based cluster analysis and discriminant analysis is used to characterize and identify electrofacies types. Second, we apply non-parametric regression techniques to predict permeability using well logs within each electrofacies. Three non-parametric approaches are examined viz. alternating conditional expectations (ACE), generalized additive model (GAM) and neural networks (NNET) and the relative advantages and disadvantages are explored. We have applied the proposed technique to a highly heterogeneous carbonate reservoir in the Permian Basin, west Texas: Salt Creek Field Unit (SCFU). The results are compared with three other approaches to permeability predictions that utilize data partitioning based on reservoir layering, lithofacies information and hydraulic flow units. An examination of the error rates associated with discriminant analysis for uncored wells indicates that data classification based on electrofacies characterization is more robust compared to other approaches. For permeability predictions, the ACE model appears to be the best among the three non-parametric approaches.

show abstract

Permeability Predictions in Carbonate Reservoirs Using Optimal Non-parametric Transformations: An Application at the Salt Creek Field Unit, Kent County, TX

Cited by 5 publications

References 0 publications

Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

Comparison between permeability estimates in a carbonate reservoir of Campos Basin using empirical and mathematical approaches

Electrofacies Characterization and Permeability Predictions in Carbonate Reservoirs: Role of Multivariate Analysis and Nonparametric Regression

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