Heterogeneous nature and complex rock properties of carbonate reservoirs makes the drilling process challenging. One of these challenges is uncontrolled mud loss. Caves or a system of cavities could be a high-risk zone for drilling as the mud losses cannot always be controlled by conventional methods, such as mud weight (MW) / equivalent mud weight (ECD) optimization, or by increasing concentration of lost circulation material (LCM) in the drilling mud. Seismic-based detection of such karstification objects is inefficient due to relatively small size, various shapes and low contrast environment. In this paper we, based on drilling data from the Barents sea, analyzed possible patterns in real-time drilling data corresponding to drilling through karstification objects. These patterns can serve as real-time indicators of zones with higher risk of karsts and can be used as an online tool for decision support while drilling in karstified carbonates.

The nature of carbonate deposition can cause the development of unique geological features such as cavities and vugs called karsts. Encountering karsts while drilling can lead to serious consequences. To improve drilling safety in intervals of karstification, it is important to detect karsts as early as possible. The use of state-of-the-art geophysical methods cannot guarantee early or even real-time detection of karsts or karstification zones. In this paper we demonstrate, based on an analysis of 20 wells drilled in karstified carbonates in the Barents Sea, that a karst that is dangerous for drilling is often surrounded by one or more other karstification objects, thus forming a karstification zone. These zones can be detected in real time through certain patterns in drillstring mechanics and mud flow measurements. They can serve as indicators of intervals with a high likelihood of encountering karsts. The identified patterns corresponding to various karstification objects are summarized in a table and can be used by drilling engineers. Apart from that, these patterns can also be utilized for training machine learning algorithms for the automatic detection of karstification zones.

Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. These risks are closely related to drilling through karsts — vugs, cavities and fractures. Therefore it is important to detect karsts early enough to avoid drilling into them or, once drilling in a karstification region is detected, to prepare risk mitigating actions. Some geophysical methods can be used for karsts detection, however, they have limitations and cannot guarantee early detection of karsts. One of the recent studies has shown that certain patterns in real-time drilling data can serve as indicators of zones with a higher likelihood of encountering karsts. In this paper, we demonstrate how these patterns can be detected in an automated manner with an adaptive differential filter algorithm. The method has been validated on real drilling data.

The nature of carbonate deposition as well as diagenetic processes can cause the development of unique geological features such as cavities, vugs and fractures. These are called karsts. Encountering karsts while drilling can lead to serious consequences such as severe mud losses, drops of bottom hole assembly and gas kicks. To improve drilling safety in intervals of karstification, it is important to detect karsts as early as possible, preferably in advance. In this paper, we review methods and technologies that can be used for the prediction and early detection of karsts. In particular, we consider acoustic, resistivity, seismic and drilling-data methods. In addition to the inventions and technologies developed and published over the past 40 years, this paper identifies the advantages, limitations and gaps of these existing technologies and discusses the most promising methods for karst detection and prediction.

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