A comparative analysis of bubble point pressure prediction using advanced machine learning algorithms and classical correlations

摘要

The need for fluid properties or PVT (Pressure-Volume-Temperature) properties, is part of the entire Exploration and Production (E&P) lifecycle from exploration to mature asset management to the typical later life events such as, Improved Oil Recovery (IOR). As the projects mature, the need for such data and its integration for various discipline-specific workflows and its interpretation in the light of reservoir performance varies. Among all the key PVT properties, bubble point pressure is probably the most important parameter. Bubble point pressure is important because it is the point at which constant composition and variable composition portions of the depletion paths merge. Geometrically, bubble point pressure appears to be a discontinuity. In addition, it dictates the existence (or not) of the incipient phase (i.e., gas phase) leading to the changes in the flow characteristics both in porous media and as well as within the wellbore and the facilities. Furthermore, it is also a good indicative of a possible gas cap when the reservoir is at saturation (reservoir pressure is equal to the bubble point pressure) or near-saturated. Among the highlighted uses, there are many more used such as the determination of the elements of miscibility, gas lift design, etc. Therefore, it is very important to estimate the bubble point pressure accurately.