Artificial Neural Network as a Method for Pore Pressure Prediction throughout the Field

Abstract

Geomechanical model construction is an essential part of field development processes planning. Building a correct pore pressure model is one of the key tasks within the process of geomechanical model construction. The traditional approach to pore pressure modeling in oil and gas industry is based on the empirical analytical models usage. This approach has a number of disadvantages, which often lead to the constructed pore pressure model to be incorrect. The authors highlight two most significant disadvantages of the traditional approach: 1) a priori discrepancy between the empirical model and fundamental physical laws; 2) the impossibility of selecting such a combination of parameters of the standard analytical model, for which the resulting pressure corresponds to the entire set of actual field data (pore pressure measurements). This paper proposes a methodology for assessing the pore pressure distribution across the field, based on the usage of neural network technology. This approach potentially eliminates both of the above disadvantages from the pore pressure model building.

Reservoir Pressure Gradient Trend Prediction for the Potash Area of Delaware Basin Using Artificial Neural Network and Geophysical Log Cross Sections

“…by using seven parameters applied ANN to develop a pore pressure prediction model. Danila et al (2021) used ANN to predict pore pressure throughout a field. In their study, they used well logging and seismic data including total vertical depth (TVD) and Acoustic well logs.…”

“…In the study, they used parameters which are porosity (φ), bulk density, rate of penetration (ROP), rotation speed (RPM), weight on bit (WOB), mud weight (MW), and interval transient time (Δt) Aliouane et al (2015). also proposed FL and ANN based models for pore pressure prediction using logging data from shale gas reservoirs Danila et al (2021). used ANN to predict pore pressure throughout a field.…”