Physics-guided neural networks for predicting unconventional well performance — using Unconventional Rate Transient Analysis for smarter production data analytics

Geoscience modelling poses many challenges due to the limited sampling and the complexity of the phenomena that create the resulting rock and its properties. When adding to the geologic and geomechanical complexity the various possible fluid flow mechanisms that are often not fully understood, one realizes quickly how daunting geomodelling could be. As a result, oil and gas fields are often bought and sold using reserves computed with simple decline curve analysis tools. Unfortunately, these simplified production analysis tools contain no physics and do not help develop oil and gas assets which require the knowledge of 1) rock properties distribution and 2) the impact of the rock properties on the selected production mechanism. For example, if one has a naturally fractured reservoir, the presence or absence of the natural fractures and the way the wells are drilled to encounter or avoid these rock properties will determine the reserves and the future of the company developing such an asset. Very often the future of many companies is not very bright due to their lack of knowledge of the distribution of the rock properties such as natural fractures and the subsequent fluid flow resulting from drilling and fracking into these heterogeneous rock properties.