Over the past decade, oil and gas service companies have implemented real-time operations and remote surveillance centers to reduce the non-productive time (NPT) associated with wellbore integrity and downhole failures. Despite these efforts, NPT remains at around 15 to 35% of the total well cost. Companies seek to reduce NPT with knowledge management systems collecting best practices, knowledge cubes along the wellbore, and lessons learned. On the other hand, drilling activities are inherently data intensive, and as the amount of available data increases, it makes it correspondingly difficult for engineers to interpret the situation in a short period of time. Case-based reasoning (CBR) is a method that can use the real-time drilling data to index and automatically recall the various experiences that are now only available through dedicated knowledge management systems. Using this method, human experience can be collected in a company case base, linked to observed data, and automatically brought forward in real time when it is again relevant. In this paper, we introduce the CBR method and discuss some of the challenges in applying this method to drilling data. The DrillEdge computer system, based on this methodology is introduced, which uses the WITSML standard for drilling data acquisition, integration, and mass storage, making it possible to use in an integrated environment with current real-time operations and remote centers. It will also highlight the importance of a unique user interface to interpret the real-time results during drilling operations. An evaluation of the system was performed in which the system learned to recognize and, subsequently, predict problems when historical data from several land-drilling operations in Latin America were played back as simulations. A rigorous testing routine was applied to evaluate the capability of the CBR system to correctly identify potential risks and provide the user with remedial actions within a specified time-critical window.
With deeper and more complex wells being drilled every year to gain access to reserves, experience and knowledge are the keys for minimizing associated risks and reducing costly non-productive time.Over the last decade, drilling optimization processes and practices have delivered substantial drilling improvements in many different environments worldwide. This has been based on a continuous improvement cycle, consisting of four distinct phases: planning, execution, post-well analysis and lessons learned, which essentially yields personnel experience and knowledge.Given the current demographics in the oil industry, which is heavily biased towards the 50+ years of age, the "big crew change" is just around the corner. As more and more graduate engineers enter the industry the resulting experience gap needs to be bridged.Within the controlled environment of the office, graduate engineers are offered guidance and support during the planning phase, however real-time execution requires timely specialist advice and guidance to mitigate drilling hazards. Drilling hazards provide tell-tale signs, and if these signs are captured early, corrective actions can be taken to reduce or completely avoid their impact, reducing operational risk and well delivery costs. This paper describes an expert system that has been developed to offer immediate guidance to support the delivery of specific real-time specialist advice -based on the system's self-learning of previous drilling events. The expert system monitors real-time drilling parameters and uses case-based reasoning (CBR) to recognize and capture current incidents that are similar to those that have occurred in the past.The system can be used to prevent drilling problems before they occur with real-time case-based reasoning for enhanced risk assessment and hazard mitigation. The paper will review examples of packoffs, stuck pipe and lost circulation that have been mitigated.
Leveraging dry wave monitoring technology to detect oil spills can contribute to quicker and more cost-effective oil spill rescue operations than leveraging in-water solutions or laser technology. Using X-band radar-based technology in combination with infrared-cameras is a proven method which supports both oil-spill surveillance and response. This technical paper highlights the physical material properties that make oil spill detection possible with a combined system of x-band radar technology and infrared cameras.
The ability to detect oil spills reliably, as well as to carry out fast and effective recovery operations, is the onus of any extraction company working in the field. Unfortunately, detecting and tracking oil spills is no easy task. By employing a combination of radar, optical and infrared (IR) sensors, operators are empowered to not only detect, characterise and track spills, but also to locate "combatable oil", i.e. where oil is at its thickest, and therefore, where recovery efforts should be mobilised. Radar-based oil spill detection (OSD) systems can be supported to great effect by the addition of optical and IR sensors. Real-time evaluation of oil spill thickness using such a combination of sensors is achieved thanks to the exploitation of some of oil's natural properties. The first of these properties is that oil will interact with the surface of the sea, affecting radar backscatter imagery. When oil is present on the sea surface it impacts the surface tension of the water and, as a result, wind does not create the same short-wave patterns on the surface as in areas free of oil. This means that areas covered with oil will not exhibit backscatter in the same way as areas free of oil. The second of these properties is that when oil and water are at the same temperature, oil emits less infrared energy than water, meaning that oil will appear cooler than water when observed using an infrared sensor. Additionally, unlike water, oil absorbs almost all light in the visible part of the electromagnetic spectrum, meaning that thick oil (where the upper layers of oil are insulated from the water below) will heat up in the daytime and become hotter than water. At night, the inverse will be observed, with the upper layer of thick oil cooling to below the temperature of the surrounding water. As a result of these properties, areas of the sea that are polluted with oil will appear differently on the various sensor displays based on the thickness of the spill, the wind and current conditions, and whether it is day or night. By harnessing the combination of radar, optical and infrared sensors, operators can determine the location of spills in daylight or darkness. They can also determine the leading edge of the spill, allowing for an understanding of its trajectory, and where recovery efforts should focus their attention. Furthermore, automatic tracking and historical insights can add much-needed information to user interface displays, giving operators comprehensive situational awareness.
High quality environmental data are critical for any offshore activity relying on data insights to form appropriate planning and risk mitigation routines under challenging weather conditions. Such data are the most significant driver of future footprint reduction in offshore industries, in terms of costs savings, as well as operational safety and efficiency, enabled through ease of data access for all relevant stakeholders. This paper describes recent advancements in methods used by a dual-footprint Pulse-Doppler radar to provide accurate and reliable ocean wave height measurements. Achieved improvements during low wind weather conditions are presented and compared to data collected from other sources such as buoys and acoustic doppler wave and current profiler (ADCP) or legacy. The study is based on comparisons of recently developed algorithms applied to different data sets recorded at various sites, mostly covering calm weather conditions.
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