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.
Highlights
Logging while drilling (LWD) has become more and more widely utilized in the oil and gas industry, allowing improvement of well placement and support for geosteering and characterization of fluids and rock formations in real-time during the drilling process
Electrical and optical gas bubble sensors are widely applied in Production Logging in the oil and gas industry to determine the amount of gaseous components in the fluid phase
We developed a smart new nonlinear autoregressive breakdown detection forecasting algorithm for optical and electrical gas bubble sensors in real-time during LWD and production logging tools (PLTs) surveys
Summary
Logging while drilling (LWD) has become more and more widely utilized in the oil and gas industry, allowing improvement of well placement and support for geosteering and characterization of fluids and rock formations in real-time during the drilling process. One of the main conclusions is the recommendation by the authors to target the cross-analysis of infrared data with other available pressure-temperaturevolume correlations in order to better determine the mass flow rates for the phases and the bubble numbers While these measurements are quintessential for determining the various fluid phases in the wellbore, challenges may arise from unreliable or faulty readings of the gas bubble sensor devices. In real-time analysis systems (LWD), this becomes ever more crucial as a delay in the detection of these anomalies may result in considerably worse operational decisions, safety and monetary losses To overcome this challenge, we developed a smart new nonlinear autoregressive breakdown detection forecasting algorithm for optical and electrical gas bubble sensors in real-time during LWD and PLT surveys. This led to the decision of not utilizing other conventional modeling approaches such as stochastic models for this challenge
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