In recent years, considerable resources have been invested to exploit vast amounts of data that get collected during exploration, drilling and production of oil and gas. Data-related digital technologies potentially become a game changer for the industry in terms of reduced costs through increasing operational efficiency and avoiding accidents, improved health, safety and environment through strengthening situational awareness and so on. Machine learning, an application of artificial intelligence to offer systems/processes self-learning and self-driving ability, has been around for recent decades. In the last five to ten years, the increased computational powers along with heavily digitized control and monitoring systems have made machine learning algorithms more available, powerful and accurate. Considering the state-of-art technologies that exist today and the significant resources that are being invested into the technologies of tomorrow, the idea of intelligent and automated drilling systems to select best decisions or provide good recommendations based on the information available becomes closer to a reality. This study shows the results of our research activity carried out on the topic of drilling automation and digitalization. The main objective is to test the developed machine learning algorithms of formation classification and drilling operations identification on a laboratory drilling system. In this paper, an algorithm to develop data-driven models based on the laboratory data collocated in many scenarios (for instance, drilling different formation samples with varying drilling operational parameters and running different operations) is presented. Moreover, a testing algorithm based on datadriven models for new formation detection and confirmation is proposed. In the case study, results on multiple experiments conducted to test and validate the developed machine learning methods have been illustrated and discussed.
Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, an idea of intelligent and fully automated machineries working on a drilling floor that is capable of consistently selecting best decisions or predictions based on realtime information available and providing drillers and operators with such recommendations, becomes closer to a reality every day. This work shows results of the research carried out on the topic of drilling automation. Its objectives are to design and test proof of concept technologies conducted on a laboratory-scale autonomous drilling rig developed at University of Stavanger, Norway. Main contribution of the study is on drilling speed (ROP) optimization with considering operational safety to personnel and environment (HSE) and drilling efficiency along with a digitized drilling program for directional drilling. The case studies are presented to show the different scenarios for drilling vertical wells and inclined wells.
In recent years, drilling digitalization and automation have advanced from being automation of rig floor equipment to an idea that is starting to be applied to entire drilling processes. However it is very costly in terms of field testing and validating developed novel technologies. To address this limitation, we take advantage of a laboratory drilling rig to run a large number of drilling tests. By introducing various drilling scenarios while drilling different formations using various combinations of the operational parameters, we could be able to collect a large amount of data for data driven methods development and testing. The main study in this paper is to develop machine learning algorithms for identifying abnormal drilling and test these algorithms on the rig based on the responses of the rig sensors in real time operations. The idea also helps us determine what the most important parameters or their combinations for drilling incidents detection are, that we could pay greatest attention to make right decisions with the help of drilling data during real-time operations.
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