Exact Lithologic Boundary Detection Based on Wavelet Transform Analysis and Real-Time Investigation of Facies Discontinuities Using Drilling Data

Exact detection of lithologic boundaries is one of the main challenges in exploration, drilling operations, and geology. Investigation of facies discontinuities has been performed using petrophysical data regarding sharp changes along the wellbore. Due to the fact that recorded well logging signals contain lots of high-frequency waves (noise), detection of the layer boundaries comes with some uncertainties that should be eliminated by denoising those signals. Wavelet transform analysis is a good approach to denoise the signals and its ability has been proven in several studies. In this study, implementation of wavelet transform analysis resulted in an innovative approach for exact differentiation of neighborhood lithologic units. Detection of boundaries between different layers, especially the ones in the vicinity of the reservoir during drilling operations, is one of the crucial issues in petroleum well engineering. This purpose is usually achieved by cutting analysis and geological maps, which are not accurate enough and may cause substantial problems. Unconfined rock compressive strength can be considered as an accurate criterion to detect geological boundaries. In this study, an artificial neural network (ANN) model is developed that can predict the unconfined rock compressive strength of formations being drilled by importing 10 drilling parameters as inputs. Because rock strength will experience sudden changes while entering the next layer, it can be used as a key parameter to determine boundaries.