Seismic‐guided estimation of log properties (Part 3: A controlled study)

Schultz, Philip S.; Ronen, Shuki; Hattori, Masami; Mantran, Pascal; Corbett, Chip

doi:10.1190/1.1437036

Cited by 40 publications

(23 citation statements)

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“…Schultz et al . (, ) and Ronen et al . () offered the multi‐attribute method in which a statistical relationship between some seismic attributes and reservoir properties is assigned at well locations, and then this relationship is used to predict reservoir parameters over 3D volume.…”

Section: Introductionmentioning

confidence: 93%

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

Ashtari

2015

Geophysical Prospecting

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In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir.

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Section: Introductionmentioning

confidence: 93%

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

Ashtari

2015

Geophysical Prospecting

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“…Multi-variate regression and neural networks can combine seismic attributes and well-log measurements by calibrating multiple seismic attributes with well-log values and predict well-log properties away from well control (Schultz et al, 1994a(Schultz et al, , 1994bRussell et al, 1997;Hampson et al, 2001). Multi-variate regression finds a linear relationship between a combination of seismic attributes and the target log values, and neural networks find a non-linear, implicit relationship between seismic attributes and log values from neural-network training.…”

Section: Introductionmentioning

confidence: 99%

Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

Moon

Lee

Kim

et al. 2016

Journal of Applied Geophysics

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“…However, these two papers did not address the problem of inverting data for the joint PDF of a continuous multidimensional model vector, as in Devilee et al (1999), Meier et al (2007aMeier et al ( , 2007b, and Curtis (2009, 2011). Hampson et al (2001) and Schultz et al (1994) also apply neural networks to predict log properties from seismic data. They discuss several practical aspects of application of neural networks for prediction of log properties.…”

Section: Introductionmentioning

confidence: 99%

Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

2012

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A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional-and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF's of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

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Seismic‐guided estimation of log properties (Part 3: A controlled study)

Cited by 40 publications

References 0 publications

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

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