Abstract
The prevention of well control incidents requires stringent well-design and casing standards, blowout prevention equipment, safe drilling practices, and multiple layers of controls and workflows. However, industry statistics suggest that most well control incidents occur due to human factors such as misinterpretation of data, delayed response to abnormal well conditions, and drilling crew physical fatigue. Real-time data has long been used by major operators for drilling optimization and well placement activities, but not so much for well control.
Exception-based monitoring of real-time data by remote operators can increase operational awareness of drilling crews, who can significantly minimize well control risks by taking proactive and precise actions. However, there are several challenges in the way that current technology is used. Real-time streaming data from a well can consist of 50 to 500 tags that make it challenging to simultaneously and continuously monitor such a large amount of data. The current technology requires determining the well condition from single-curve plots and generic alerts—a technique that is time consuming and allows for errors. Poor-quality data can also adversely affect interpretation, analysis, and decision making.
We have developed a new real-time alarm solution to overcome some of the above challenges by combining signal processing and complex logic to manipulate raw and fragmented data and to emulate the thought process of a drilling operator. This real-time system enables drilling operators to set up different alarm thresholds for various activities of well construction such as drilling, tripping, and circulation. A user-friendly alarm console provides valuable situational awareness, enabling the operator to proactively respond to abnormal conditions using real-time alerts based on well condition.
The Drilling and Completions Decision Support Center (D&C DSC) team at Chevron Energy Technology Company uses this technology for well control in its global operations. The following paper explains the technology platform of this system, data processing techniques, and some of the drilling events previously identified.
