Hybrid regularization methods for seismic reflectivity inversion

Proppant is one of the major elements of a hydraulic fracture process that can influence the productivity of an oil or gas well. The permeability of a propped fracture may be determined through laboratory measurements, utilizing experimental relationships, or using analytical methods. All methods have their advantages and disadvantages, and this study attempts to offers a solution that combines the various methods to achieve a practical solution. The standard measurement method for fracture conductivity and permeability is a time-consuming and delicate procedure and can be performed only at the cost of a significant degree of uncertainty. Investigating a method that can predict fracture permeability in significantly less time and with an acceptable level of accuracy would be useful for the industry. Proppant can be viewed as an unconsolidated, heterogeneous (in size distribution) although well-sorted granular packing, and the different relationships that can predict the permeability of such systems may be utilized. Such interdependencies account for the frictional pressure drop caused by the proppant particles and often neglect the pressure drop caused by the surface of the apparatus. To extend the applicability of the various models, the combination of a theoretical model that accounts for the pressure drop caused by the fracture wall with those models would have clear practical advantages. For this reason, a new equation had to be developed that establishes the relationship between the fracture permeability and the so-called modified particle friction factor. Since the frictional pressure drop correlations usually contain porosity as a parameter, determining its value under reservoir conditions is essential and a new procedure had to be developed. Since the different relationships were developed under different conditions to those under which the proppant-packs are usually used, providing a solution to the issues of their wide range of application is the primary objective of this study.

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Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data

Geng

Wang

2020

Engineering Geology

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Hybrid regularization methods for seismic reflectivity inversion

Cited by 9 publications

References 50 publications

Blind Inversion of Multichannel Nonstationary Seismic Data for Acoustic Impedance and Wavelet

Blind Inversion of Multichannel Nonstationary Seismic Data for Acoustic Impedance and Wavelet

Semi-analytical approach to the determination of fracture permeability

Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data

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