Validation: A technique for selecting seismic attributes and verifying results

Schuelke, James S.; Quirein, John

doi:10.1190/1.1820645

Cited by 17 publications

(4 citation statements)

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“…Also, in order that the seismic data and the target well logs are scaled to the same resolution level, a convolutional approach is used [1]. The application of neural network analysis have been successfully done in the past, through the use of seismic attributes and, consequently, neural networks, with the prediction of the 3-D distribution of physical properties, such as porosity [2][3][4][5][6] and lithology [7,8]. Geophysical applications were used in the beginning of the 1990s in the development of early neural networks for the description and prediction of lithology of wells by using a propagation of Multi-Layer Feed Forward Network (MLFN) [9].…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Network Analysis for Seismic Data to Differentiate Reservoir Units of Yamama Formation in Nasiriya Oilfield A Case Study in Southern Iraq

Khorshid

Mohammed²,

–Taee³

2021

eijs

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The EMERGE application from Hampsson-Russell suite programs was used in the present study. It is an interesting domain for seismic attributes that predict some of reservoir three dimensional or two dimensional properties, as well as their combination. The objective of this study is to differentiate reservoir/non reservoir units with well data in the Yamama Formation by using seismic tools. P-impedance volume (density x velocity of P-wave) was used in this research to perform a three dimensional seismic model on the oilfield of Nasiriya by using post-stack data of 5 wells. The data (training and application) were utilized in the EMERGE analysis for estimating the reservoir properties of P-wave velocity, in addition to the neural network analysis and deriving relations between them at well locations. P- wave velocity slices of reservoir units (Yb1, Yb2, and Yc) of Yamama Formation were prepared to determine the enhancement trends within these units. From a general economic point of view, due to good prospecting in Cretaceous rocks, especially in Nasiriya oilfield, , Yamama Formation was found to contain hydrocarbon accumulation and can be considered as one of the most important reservoirs in southern Iraq.

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Section: Introductionmentioning

confidence: 99%

Application of Neural Network Analysis for Seismic Data to Differentiate Reservoir Units of Yamama Formation in Nasiriya Oilfield A Case Study in Southern Iraq

Khorshid

Mohammed²,

–Taee³

2021

eijs

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“…Hart and Balch (2000) present a case study on predicting reservoir properties from seismic attributes with limited well control, in which they propose a suite of visual-correlation schemes to define the attributes of choice. From a more quantitative aspect, Schuelke and Quirein (1998) propose to use cross validation as a measure of prediction performance, then select attributes that lead to higher cross validation. Since then, al-most all the proposed alternative strategies have shared one fundamental concept, which is to select attributes that lead to the lowest validation error.…”

Section: Introductionmentioning

confidence: 99%

Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

Zhao

Marfurt

2018

GEOPHYSICS

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With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter's knowledge. In other words, we expect the weights to represent "which attribute is 'favored' by an interpreter as input for unsupervised learning" from an interpretation perspective and "which attribute is 'favored' by the learning algorithm" from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter's knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised selforganizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.

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“…Hart and Balch (2000) present a case study on predicting reservoir properties from seismic attributes with limited well control, and propose a suite of visual correlation schemes to define the attributes of choice. From a more quantitative aspect, Schuelke and Quirein (1998) propose to use cross-validation as a measure of prediction performance, then select attributes that lead to higher cross-validation. Since then, almost all the proposed alternative strategies have shared one fundamental concept, which is to select attributes that lead to the lowest validation error.…”

Section: Introductionmentioning

confidence: 99%

Automated input attribute weighting for unsupervised seismic facies analysis

Zhao

Marfurt

2017

SEG Technical Program Expanded Abstracts 2017

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Typically, interpreters qualitatively choose input attributes for multiattribute facies analysis based on their experience and geologic target of interest. In this study, we augment this qualitative attribute selection process with quantitative measures of which candidate attributes best differentiate features of interest, by weighting input attributes based on their response from the unsupervised learning algorithm that used to generate the facies map, as well as interpreter's preference. We use self-organizing map (SOM) as an example of unsupervised seismic facies analysis algorithm. By comparing with results from equally weighted attributes, we demonstrate that the proposed attribute weighting workflow is able to represent the information from the input attributes more adequately.

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Validation: A technique for selecting seismic attributes and verifying results

Cited by 17 publications

References 1 publication

Application of Neural Network Analysis for Seismic Data to Differentiate Reservoir Units of Yamama Formation in Nasiriya Oilfield A Case Study in Southern Iraq

Application of Neural Network Analysis for Seismic Data to Differentiate Reservoir Units of Yamama Formation in Nasiriya Oilfield A Case Study in Southern Iraq

Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

Automated input attribute weighting for unsupervised seismic facies analysis

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