In this paper, we present a methodology for determining lithological difference at the bottom of the well during drilling operations. Our approach is based on the analysis of mechanical parameters of drilling. These parameters are receiving as real-time time-series data. The central part of the methodology is a model based on the machine learning approach. Our model and the whole methodology can be applied to real drilling cases. The set of parameters that are required for the methodology can be collected from the typical mud logging station.
The main use case for the methodology is an optimization of the geosteering process. The most modern geosteering approaches are based on the LWD data. It is the main restriction of common approaches for the adjustment of the direction of drilling. The problem is that the LWD sensors are placed for a few decimals meters before the bit in a typical Bottom Hole Assembly (BHA) design. As a result, these a few tens of meters are drilling in a "blind window".
The methodology is illustrated on the historical data of drilling of the Novoportovskoe oilfield. At the current stage, the results of the testing show that suggested methodology can correctly classify two out of three cases of changes of lithotypes while drilling.