Permeability Determination From Well Log Data

Mohaghegh, Shahab D.; Balan, B.; Ameri, S.

doi:10.2118/30978-pa

Cited by 98 publications

(49 citation statements)

References 14 publications

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“…These algorithms include multiple linear regression (Dahraj and Bhutto 2014;Mohaghegh et al 1997;Xue et al 1997), generalized additive modeling (Al-Mudhafar and Mohamed 2015; AlMudhafar and Bondarenko 2015; Lee et al 2002;Rafik and Kamel 2016), multivariate adaptive regression splines (Al-Mudhafar and Al-Khazraji 2016;Xie 2008), neural networks (Lee and Datta-Gupta 1999;Lee et al 2002;Mohaghegh et al 1997), fuzzy logic (Nashawi and Malallah 2009), and support vector regression (Al-Anazi and Gates 2011).…”

Section: Introductionmentioning

confidence: 99%

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Al‐Mudhafar

2017

J Petrol Explor Prod Technol

135

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In this paper, an integrated procedure was adopted to obtain accurate lithofacies classification to be incorporated with well log interpretations for a precise core permeability modeling. Probabilistic neural networks (PNNs) were employed to model lithofacies sequences as a function of well logging data in order to predict discrete lithofacies distribution at missing intervals. Then, the generalized boosted regression model (GBM) was used as to build a nonlinear relationship between core permeability, well logging data, and lithofacies. The well log interpretations that were considered for lithofacies classification and permeability modeling are neutron porosity, shale volume, and water saturation as a function of depth; however, the measured discrete lithofacies types are sand, shaly sand, and shale. Accurate lithofacies classification was achieved by the PNN as the total percent correct of the predicted discrete lithofacies was 95.81%. In GBM results, root-mean-square prediction error and adjusted R-square have incredible positive values, as there was an excellent matching between the measured and predicted core permeability. Additionally, the GBM model led to overcome the multicollinearity that was available between one pair of the predictors. The efficiency of boosted regression was demonstrated by the prediction matching of core permeability in comparison with the conventional multiple linear regression (MLR). GBM led to much more accurate permeability prediction than the MLR.

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Section: Introductionmentioning

confidence: 99%

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Al‐Mudhafar

2017

J Petrol Explor Prod Technol

135

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“…As shown in Table 3, the multiple (variable) regression coefficients (R) of the three mathematical models are high and they all indicate good relationship between the dependent and independent variables (Mohaghegh et al, 1997;Wendt et al, 1986). The significance F values of the three models are less than 0.0005 which indicates that they are statistically significance.…”

Section: Prediction Of Petrophysical Properties Of D3 Reservoir Intermentioning

confidence: 93%

“…The dependent variables are permeability, R35 and flow zone indicators, while bulk density and gamma ray log data were used as predictor or independent variables because the two parameters reflect variations in permeability and porosity (Dewan, 1983). The theory of multiple regression analysis can be found in (Draper and Smith, 1998;Mohaghegh et al, 1997;Wendt et al, 1986;Orlov, 1996).…”

Section: Dataset and Methodsmentioning

confidence: 99%

“…In the application of the mathematical models, the shortcomings of using multiple regression analysis in the model development and properties' predictions, expressed by Mohaghegh et al (1997), such as prediction of negative values were taken into considerations. For example, negative values were predicted with gamma ray log values greater than 50Api, for sandy shale-shale.…”

Section: Prediction Of Petrophysical Properties Of D3 Reservoir Intermentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneities and Intra Sand-Body Compartmentalization in Late Oligocene Delta-Front Deposit, Niger Delta, Nigeria

Oyanyan¹,

Oti²

2016

American Journal of Geoscience

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This paper presents the study of the heterogeneity in lithofacies, porosity, permeability, mineral grain density, pore-throats sizes and hydraulic rock types and the intra sand-body compartmentalization in cored delta-front deposit of Greater Ughelli depobelt of Niger Delta. Sedimentological study of core samples results in the identification of nine lithofacies and the interpretation of environments of deposition as mainly proximal and distal delta-front mouth bar. The sand unit indicates moderate to excellent reservoir quality with core porosity between 16.2 and 29.5% and permeability between 16.8 and 7,560 md. Dykstra-Parsons core permeability distribution coefficient of 0.97 indicates that the studied reservoir sand-body is vertically highly heterogeneous, with high potential for vertical intra sand-body compartmentalization. Graphical cluster analysis of Flow Zone Indicators (FZI) led to the identification of nine Hydraulic Flow Units (HFU) with distribution controlled by depositional facies. The prediction of permeability, porethroats (r35) and flow zone indicator values of the reservoir in an uncored-well using predictive mathematical models developed with multiple regression analysis enabled the inter-well correlation of hydraulic flow units and indicates lateral continuity of reservoir compartments. An intra sand-body compartmentalization evaluated with core permeability values, Winland r35 coefficients and flow zone indicators was corroborated with formation pressure data analysis. Results show that the studied delta front-mouth bar reservoir is vertically compartmentalized by intercalated shales. Fractures in over-pressured zones were found to reduce vertical reservoir fluid compartmentalization by intercalated shales. However, reduction in reservoir pressure due to hydrocarbon production can result in dynamic compartmentalization and consequently, reduction in hydrocarbon recovery.

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“…Mohaghegh et al (1997) presented a comparison of empirically determined models, multiple variable regressions, and ANN, and showed that ANN resulted in better performance than the other methods. Lim and Kim (2004) demonstrated that fuzzy logic and ANN can be utilized as a powerful tool to determine reservoir properties from well logs.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic-Unit-Based Fuzzy Model to Predict Permeability from Well Logs and Core Data of a Multi-Layer Sandstone Reservoir in Ulleung Basin, South Korea

Jun

Kang

Jang

et al. 2015

Energy Exploration & Exploitation

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This paper presents a method to predict permeability of an offset well using well logs and core data from a multi-layer sandstone reservoir with various depositional environments. Many studies have been conducted to predict permeability but limited to wells drilled in a formation with a single depositional environment. This study implements fuzzy models to predict the FZI (flow zone indicator) and permeability, and applied HU (hydraulic unit) grouping to classify the rock properties of various depositional environments in terms of the correlation between permeability and porosity. Then, an individual fuzzy model is developed for each HU group. A field application confirms that the method can be applied to permeability prediction using well data from various depositional environments. A comparative study shows that HU grouping plays a key role in grouping cores of similar flow characteristics in the case of core data from multiple layers with various depositional environments.

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Permeability Determination From Well Log Data

Cited by 98 publications

References 14 publications

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Heterogeneities and Intra Sand-Body Compartmentalization in Late Oligocene Delta-Front Deposit, Niger Delta, Nigeria

Hydraulic-Unit-Based Fuzzy Model to Predict Permeability from Well Logs and Core Data of a Multi-Layer Sandstone Reservoir in Ulleung Basin, South Korea

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