A novel artificial intelligence automatic detection framework to increase reliability of PLT gas bubble sensing

Abstract

Abstract Production logging tools (PLTs) and formation testing, even in logging while drilling (LWD) conditions during underbalanced drilling, are key technologies for assessing the productivity potential of a gas well and therefore to maximize recovery. Gas bubble detection sensors are key components in determining the fluid phases in the reservoir and accurately quantify recoverable reserves, optimize well placement, geosteering and to qualify the production ability of the well. We present here a new nonlinear autoregressive - breakdown artificial intelligence (AI) detection framework for PLT gas bubble detection sensors that categorize in real-time whether and which sensors become unreliable or have broken down during the logging measurements. AI tools allow the automatization of this method that is critical during data quality control of post-drilling PLT, but it is essential when the measurements are performed in LWD as data assessment and processing need to occur in real-time. This AI framework was validated on both a training and testing dataset, and exhibited strong classification performance. This method enables accurate real-time breakdown detection for gas bubble detection sensors.