Estimation of reservoir porosity based on seismic inversion results using deep learning methods

Abstract

Location limitation of logged wells restricts the porosity estimation across the whole reservoir target, whereas seismic data are always collected to cover larger areas. In this paper, inversion results of seismic data are proposed as inputs for the prediction of reservoir porosity, even though the resolution is decreased, compared with well-log readings. The non-linear inversion scheme used is able to explore the complex relationship between rock properties and seismic data, which could potentially provide a higher quality of inversion results. As a regression process, Convolutional Neural Networks is then applied to estimate the reservoir porosity, based on the outputs of seismic inversion scheme. Incorporating 2D kernel filters which are convolved with input rock properties, the local information inside filters window is considered, and a better prediction performance is to be guaranteed. This is due to the fact that reservoir porosity is formed under depositional and digenetic rules, and it is intrinsically correlated with rock properties along the vertical direction in a short range. The designed workflow is applied to a real dataset from the Vienna Basin where compressibility and shear compliance are inverted and then used as inputs for the porosity estimation by Convolutional Neural Networks. For a comparison, the traditional Artificial Neural Networks is also trained and applied to the same dataset. It is concluded that the Convolutional Neural Networks can achieve a higher accuracy, and a 3D cube of reservoir porosity is obtained without location restriction of well logs.