Using support vector regression to estimate sonic log distributions: A case study from the Anadarko Basin, Oklahoma

Crânganu, Constantin; Breaban, Mihaela

doi:10.1016/j.petrol.2013.02.011

Cited by 31 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…; Rajabi et al . ; Asoodeh and Bagheripour ; Cranganu and Breaban ; Zoveidavianpoor et al . ; Maleki et al .…”

Section: Introductionunclassified

“…A number of studies have demonstrated the use of artificial intelligence systems to predict the sonic wave velocities from other borehole data. Cranganu and Breaban (2013) predict sonic log from gamma ray and deep resistivity logs using support vector regression. Rezaee, Kadkhodaie Ilkhchi and Barabadi (2007) utilized fuzzy logic, neuro-fuzzy and artificial neural network approaches to predict Vs from conventional log data in a sandstone reservoir.…”

Section: Introductionmentioning

confidence: 99%

“…The prediction of sonic velocities in carbonate reservoirs has been demonstrated in several studies (Rezaee et al 2007;Rajabi et al 2010;Asoodeh and Bagheripour 2012;Cranganu and Breaban 2013;Zoveidavianpoor et al 2013;Maleki et al 2014;Bagheripour et al 2015), but studies applying this methodology to hard rock environments are sparse. One reason is the paucity of petrophysical data for many hard rock environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of sonic velocities from other borehole data: An example from the Kevitsa mine site, northern Finland

Kieu

Kepic

Kitzig

2018

Geophysical Prospecting

View full text Add to dashboard Cite

P‐wave and S‐wave velocities are vital parameters for the processing of seismic data and may be useful for geotechnical studies used in mine planning if such data were collected more often. Seismic velocity data from boreholes increase the robustness and accuracy of the images obtained by relatively costly seismic surface reflection surveys. However, sonic logs are rarely acquired in boreholes in‐and‐near base metal and precious metal mineral deposits until a seismic survey is planned, and only a few new holes are typically logged because the many hundreds of holes previously drilled are no longer accessible. If there are any pre‐existing petrophysical log data, then the data are likely to consist of density, magnetic susceptibility, resistivity and natural gamma logs. Thus, it would be of great benefit to be able to predict the velocities from other data that is more readily available. In this work, we utilize fuzzy c‐means clustering to build a “fuzzy” relationship between sonic velocities and other petrophysical borehole data to predict P‐wave and S‐wave velocity. If boreholes with sonic data intersect most of the important geological units in the area of interest, then the cluster model developed may be applied to other boreholes that do not have sonic data, but do have other petrophysical data to be used for predicting the sonic logs. These predicted sonic logs may then be used to create a three‐dimensional volume of velocity with greater detail than would otherwise be created by the interpolation of measured sonic data from sparsely located holes. Our methodology was tested on a dataset from the Kevitsa Ni‐Cu‐PGE deposit in northern Finland. The dataset includes five boreholes with wireline logs of P‐wave velocity, S‐wave velocity, density, natural gamma, magnetic susceptibility and resistivity that were used for cluster analysis. The best combination of input data for the training section was chosen by trial and error, but differences in the misfit between the various training datasets were not particularly significant. Our results show that the fuzzy c‐means method can predict sonic velocities from other borehole data very well, and the fuzzy c‐means method works better than using multiple linear‐regression fitting. The predicted P‐wave velocity data are of sufficient quality to robustly add low‐frequency information for seismic impedance inversion and should provide better velocity models for accurate depth conversion of seismic reflection data.

show abstract

“…; Rajabi et al . ; Asoodeh and Bagheripour ; Cranganu and Breaban ; Zoveidavianpoor et al . ; Maleki et al .…”

Section: Introductionunclassified

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of sonic velocities from other borehole data: An example from the Kevitsa mine site, northern Finland

Kieu

Kepic

Kitzig

2018

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…More recently, algorithms such as artificial neural network (ANN) and support vector machine (SVM) have been employed in petroleum engineering for modeling the complex and nonlinear phenomena which occur in well-test analysis [45], well-log interpretation [46][47][48], reservoir characterization [49], PVT [50][51][52] and permeability studies for crude oils [53]. In 2003, a threelayer back propagation artificial neural network (BPNN) model was adopted by Huang et al [54] to accurately predict MMP for both pure and impure CO 2 injection cases.…”

Section: Introductionmentioning

confidence: 99%

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Bian

Han

et al. 2016

Fuel

View full text Add to dashboard Cite

“…Artificial neural networks have been proved to be capable of approximating any nonlinear function to any degree of accuracy provided that there are sufficient number of samples for network training and learning, and in recent years, the network approaches have some successful applications in petroleum engineering [2,3,4,5,17], such as sedimentary microfacies prediction, lithology classification, and reservoir prediction.…”

Section: Introductionmentioning

confidence: 99%

Velocity Inversion for Sandstone Reservoir Based on Extreme Learning Machine Neural Network

Cao¹,

Yang²

2015

2015 First International Conference on Computational Intelligence Theory, Systems and Applications (CCITSA)

View full text Add to dashboard Cite

This study focuses on reservoir velocity inversion from seismic data using extreme learning machine in Yanqi gas field. The analysis data consist of target logs from wells which tie the 3-D seismic volume. The specific aim of this work is to build reliable non-linear network models for velocity inversion and then predict velocity logs from seismic data for the whole survey. We first calculate five types of seismic attributes and extract them at well locations. Then pair them with the velocity logs as the training data for the ELM network. Appropriate network parameters are determined and non-linear ELM model predictor is set up for the research. We then carry out the velocity inversion for the whole survey. The results show that the ELM network has good performance, and two favorable areas are suggested for further research.

show abstract

Using support vector regression to estimate sonic log distributions: A case study from the Anadarko Basin, Oklahoma

Cited by 31 publications

References 8 publications

Prediction of sonic velocities from other borehole data: An example from the Kevitsa mine site, northern Finland

Prediction of sonic velocities from other borehole data: An example from the Kevitsa mine site, northern Finland

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Velocity Inversion for Sandstone Reservoir Based on Extreme Learning Machine Neural Network

Contact Info

Product

Resources

About