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with the explosive growth of unconventional oil and gas development including shale oil and shale gas, controlling the cost of logging services and further improving the logging quality while ensuring the professional competence and quantity of logging operators is an important issue facing the development of the entire industry.
ai intelligent remote cloud logging platform integrates the logging instrument terminal at the oil and gas exploration site with the base server cloud through internet technology to achieve cloud logging. by fully utilizing the advantages of human and technological resources in the base, and empowering the logging site through expert decision-making and artificial intelligence, the platform is designed to reduce duplicate investment in human resources at the logging site, and improve the logging quality throughout the entire block.
ai intelligent remote cloud logging platform
i. functional framework
ai intelligent remote cloud logging platform is based on a comprehensive logging instrument that collects and summarizes comprehensive logging data from oil and gas exploration sites, and a high-speed and stable data transmission system is constructed through a wide area network between the site and the base terminal; the base server collects and stores the logging data from the on-site terminal, as well as audio and video streams, and efficiently integrates the oil and gas drilling information, remote control, drilling condition analysis, geosteering, production dynamic management, etc. to achieve collaborative operation between the on-site terminal and the cloud. on this basis, with the assistance of expert collaboration system and artificial intelligence, it analyzes, guides, and remotely monitors the on-site operations for better on-site monitoring guidance, formation identification, and engineering accident warning through multi-well linkage and intelligent analysis of regional data to improve the quality of drilling and logging.
framework of ai intelligent remote cloud logging platform
remote monitoring and comparison with adjacent wells
ai intelligent remote cloud logging platform has a powerful information release system, which constructs a local information highway through wan. it can push and release information flows in a real-time, multi-dimensional and customized manner on various clients including computers, tablets, mobile phones, etc. to meet the needs of different users.
client information flow
ⅱ. ai-assisted prediction
ai supports the development of cloud logging technology from multiple dimensions. based on the construction of a large database of cutting samples in the early stage, combined with the analysis and application of deep neural network algorithm, it can quickly analyze and identify the lithology of drilling cuttings online, and establish geological profiles of strata. it can predict the mineral composition of rock cuttings and various physical performance indicators, and guide drilling and logging operations by cooperating with advanced spectral analysis technology.
rock cuttings identification
with the assistance of ai, it can improve the accuracy and timeliness of expert system prediction and early warning, increase overall decision-making efficiency, reduce the repetitive manpower investment of expert technicians, and improve the marginal benefits of cloud logging technology.
formation pressure index
ⅲ. field application
ai intelligent remote cloud logging platform has been applied on shale gas sites in sichuan, and has been successfully promoted to overseas markets such as azerbaijan and nigeria, with good application results. the cloud logging technology has realized the interaction of drilling and logging information, data collection, processing, and collaborative application, maximized the comprehensive resources of base experts and management decision-makers, and broken regional and time limitations, thereby providing scientific and timely decision-making basis, greatly shortening the time traditionally spent on waiting for measures, commands, results, and decisions, and improving the overall efficiency of production operation and decision-making.
oilfield site
