Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion Yu, Hao and Gu, Hanming Institute of Geophysics and Geomatics, China University of Geosciences Wuhan, Wuhan, China Yu Hao China University of Geosciences email: yuhao8905@cug.edu.cn website: whimian.github.io Contact 1. Sayers, C. M., et al. “Use of reflection tomography to predict pore pressure in overpressured reservoir sands” SEG Technical Program Expanded Abstracts 22.1(2003):1362. 2. Gu, W., et al. “Application of seismic motion inversion technology in thin reservoir prediction: A case study of the thin sandstone gas reservoir in the B area of Junggar Basin” Natural Gas Geoscience 27.11(2016). References A new multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by- trace multivariate regression analysis on seismic-derived petrophysical properties to locally calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. Introduction Multivariate Model Results Developed from traditional geostatistical inversion methodologies, Seismic Motion Inversion (SMI) is a inversion method that utilize thin bed tuning effect for determining and optimizing the structure of reflection coefficient and simulate the distribution of sand bodies. Lateral variation of seismic motion instead of traditional variogram is used to describe the spatial variation of reservoir. Seismic Motion Inversion Application of the proposed methodology to an research area in East China Sea has shown that the method: • bridge the gap between seismic and well log pressure prediction; • give prediction values close to pressure measurements from well testing; • provide more detailed pressure variation both vertically and horizontally. Future works: An Uncertainty Analysis will be included in this pressure prediction workflow, not only as a quality control process but also as a measure of how reliable the predicted pressure data is when used in well planning and casing design. Conclusions The multivariate formation pressure prediction model proposed by Sayers (2003) is well balanced between conciseness and representativeness. Three petrophysical properties (velocity, porosity, and shale volume) are used to describe the variation of effective stress in this model: 𝑉= 𝑎0 −𝑎1𝜑−𝑎2𝐶+ 𝑎3𝜎𝐵 The porosity term 𝜑describes the degree of compaction while the shale volume term C describes the relative influence of different rock type. Though proposed as a model that can only deal with abnormal pressure generated by compaction disequilibrium, it can be used in predicting abnormal pressure caused by fluid expansion when combined with the unloading model proposed by Bowers. The corresponding equation for unloading can be formulated as: 𝑉= 𝑎0 −𝑎1𝜑−𝑎2𝐶+ 𝑎3 𝜎𝑚𝑎𝑥 𝜎 𝜎𝑚𝑎𝑥 1 𝑈 𝐵 Code for basic part of this project has been published as an open source python package -- pyGeoPressure. https://github.com/whimian/pyGeoPressure Seismic Initial Model likelihood prior well log Ir Ia seismic motion norm. Correlator posterior sampling expectation perturb accept reject Fig 1. SMI workflow Fig 2. SMI samples according to wave motion similarity and distance to well location. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Porosity and sh