Increasing use of single-walled carbon nanotubes (SWCNTs) will lead to their increased release into the environment. Previous work has shown negative effects of SWCNT on growth and survival of model organisms. The aim of the current study was to determine the effect of SWCNT well-dispersed by either DNA or sodium cholate (SC) on the unicellular green algae Chlamydomonas reinhardtii in stagnant water conditions. Growth measurements were taken up to ten days for algae treated with varied levels of DNA:SWCNT or SC:SWCNT or controls, and chlorophyll content after 10 days was determined. Results show no effect on either growth or chlorophyll content of algae at any concentration or duration. This is in contradiction to prior work showing toxicity of SWCNT to environmental model organisms.
The current state of the drilling industry tends to focus on technology and techniques that reduce the cost of well construction. One approach is to use drilling automation to help improve ROP, and perhaps more importantly, to mitigate drilling dysfunctions and human errors than can lead to large, unplanned expenses. SPE's Drilling Systems Automation Technical Section (DSATS) created an international university competition to encourage the development of new drilling algorithms and to get more young people involved in drilling. The students must design, build and operate a small (6 ft / 2 m) drilling rig with fully automated sensors and controls. They drill a specially created, multi-layered rock sample as fast and as straight as possible, with only two buttons: start and stop. This paper summarizes the work done by the West Virginia University team that won the 2016 competition. It shows scalable drilling algorithms can be developed on a miniature rig that can later be transferred to ongoing drilling programs. DSATS implemented a competition to encourage students to investigate the use of automation techniques and tools for drilling systems. The competition fosters a greater understanding of complicated drilling systems, challenging students across disciplines to consider the possibilities of a career in upstream drilling operations. This project requires each university team to first submit a proposal, including structural design, control architecture, and sensor selection with no knowledge of the material to be drilled. Teams were advised to consider mitigating the effect of nonplanar junctions as well as the possibility of lost circulation, and to be especially cognizant of vibration and torque issues. The winning team received a travel grant to attend the ATCE to present their test results at the DSATS symposium in Dubai. This paper addresses the details of that presentation. It includes: Drilling limitations and critical parametersConstruction issues and initial operations that required a re-designFinal design criteria, constraints, tradeoffsSummary of recorded data and key eventsDrilling parameters and how they impacted the testEconomic considerationsSignificant lessons learnedConclusions and recommendations As the competition finishes its second year, it remains the only competition of its kind that requires a multidisciplinary approach at a university level, and prepares those involved for the type of interwoven, team approach often at the heart of oilfield operations today. While the rig designs are practical, they are not limited by historical features or commonplace rig designs. Hands-free drilling is possible and proven on a small scale as more and more companies begin to implement full scale operations to mitigate drilling dysfunctions and improve ROP to lower costs.
Drilling automation has focused on developing predictive controls based on existing formation and well sites for which abundant data is available. These methods are not suitable in new locations where there is little information and where drilling data has not been recorded. This study focuses on a proof-of-concept to allow drilling in locations with little or no data available by determining drilling parameters via an artificial intelligence algorithm. The methods used were tested for use in the second annual Drillbotics competition sponsored by the Drilling Systems Automation Technical Section (DSATS) of the Society of Petroleum Engineers (SPE). This study addresses the difficulties and challenges faced in adapting artificial intelligence optimization algorithms for use with real-world applications. Furthermore, the limitations of such a system are examined and the breakdown between the algorithm and operational limitations are explored. A review of past drilling automation attempts and research was conducted and existing problems identified. This research was completed on a pilot-scale drilling rig used by West Virginia University in the Drillbotics competition. The rig was used with multiple samples made in-house in order to provide a variety of materials, inclinations, and drilling conditions. The review of the test was subject to professional judges to provide an unbiased decision and served to advance this study.
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