Principal components analysis of spectral components

Guo, Hao; Marfurt, Kurt J.; Liu, Jianlei; Dou, Qifeng

doi:10.1190/1.2370422

Cited by 18 publications

(2 citation statements)

References 5 publications

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“…In addition to providing numerical results, PCA also can generate a number of graphical representations that provide valuable insight into interesting patterns in the data analyzed. PCA has been used extensively in geosciences for facies estimation (Doveton 1994;Guo et al 2006;Singh and Carigalli 2007). Also, it has been applied to other oilfield-related tasks, such as the estimation of fault orientation (Tingdahl 1999), selection of strategies for the improved remediation of formation damage (Tague 2000), the characterization and prediction of reservoir properties through reparameterization (Zhu et al 1999;Lee et al 2002;Rwechungura et al 2011), and the design of hydraulic-fracture treatments (Aoudia et al 2010;Kazakov and Miskimins 2011).…”

Section: Objective and Methodologymentioning

confidence: 98%

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Bhattacharya

Nikolaou

2013

SPE Journal

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Unconventional gas resources have dramatically changed the future energy landscape. Developing these resources involves substantial risk. Such risk can be mitigated if information gathered at initial stages of the development of a field is used efficiently and effectively to guide future development. A variety of tools-such as decline-curve analysis (DCA), type-curve analysis, simulator history matching, and artificial intelligence (AI)-is used to that effect. These tools accomplish partially overlapping but different tasks. Additional tools that could not only facilitate the analysis of historical data but also guide future development would be of value. In this work, we propose an efficient methodology that can use historical production data from existing wells to answer questions such as the following: Which wells will behave similarly? Which wells will behave differently from each other or from standard expectations? Which factors will contribute to these differences? How can data from existing wells be used to anticipate the performance of new wells? The proposed methodology relies on standard principal-component analysis (PCA) and principal-component regression (PCR). The application of PCA to historical production data from twelve wells in the Holly Branch field quickly identified wells with distinct behavior. The subsequent investigation of pressure and the completion data for these wells revealed reasons for such distinct behavior. Finally, a simple linear model was built with PCR, with good ability to predict production from new wells, as assessed through cross-validation. The value of the efficiency offered by the proposed methodology would be much higher for larger data sets, for which manual analysis of production data is more cumbersome.

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Section: Objective and Methodologymentioning

confidence: 98%

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Bhattacharya

Nikolaou

2013

SPE Journal

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“…We then calculated spectral decomposition information including both magnitude and phase using software developed at the University of Oklahoma. A common method for dealing with high-dimensional data is to reduce the dimension using a linear projection (Guo et al, 2006). This horizon was chosen because it is well centered within the Red Fork Formation.…”

Section: Iptc 16842mentioning

confidence: 99%

Using Diffusion Maps for Latent Space Analysis on Seismic Waveforms of Incised Valleys in the Anadarko Basin, Oklahoma, USA

2013

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In this work we present a new method for latent space mapping based upon inter-point similarities. This method, diffusion maps, has a number of nice qualities compared with previous methods including the fact that it is based upon inter-point similarities rather than a Euclidean space. We then demonstrate application of this approach to mapping an incised valley system from the Anadarko Basin, Oklahoma, USA. TX 75083-3836, U.S.A., fax +1-972-952-9435

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A Home for Principal Component Analysis (PCA) as part of a Multi-Agent Safety System (MASS) for Human-Robot Collaboration (HRC) within the Industry 5.0 Enterprise Architecture (EA)

Rajendran

Kebria

Mohajer

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Principal components analysis of spectral components

Cited by 18 publications

References 5 publications

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Using Diffusion Maps for Latent Space Analysis on Seismic Waveforms of Incised Valleys in the Anadarko Basin, Oklahoma, USA

A Home for Principal Component Analysis (PCA) as part of a Multi-Agent Safety System (MASS) for Human-Robot Collaboration (HRC) within the Industry 5.0 Enterprise Architecture (EA)

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