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Drilling operations consist of breaking the rock to deepen a wellbore for oil or gas extraction. A drilling fluid, circulating from the surface through the drill pipe and from the annulus to the surface, is used to remove rock cuttings and maintain hydrostatic pressure. Drilling fluid lost circulation incidents (LCIs) are major sources of non-productive time (NPT) in drilling operations. These incidents occur due to preexisting natural fractures (vugs, caverns, etc.) and/or drilling-induced hydraulic fractures. The initiation of an LCI could lead to other hazardous drilling phenomena, such as formation influx or kick/blowout, stuck pipe incidents, among others. LCIs are typically monitored at the rig site by observing drilling fluid levels in the fluid tanks. This manual process incurs missing the occurrence or late detection of LCIs. Machine learning (ML) and deep learning (DL) classification algorithms are powerful in processing time-series data and achieving early detection of such temporal phenomena. In this study, we performed a large-scale analysis of the surface drilling and rheology data obtained from historical wells with LCIs. This analysis includes primary and secondary preprocessing steps including, aggressive sampling, feature engineering, and window normalization to derive generalizable DL models for real-time operations. Focal loss was utilized to account for data class imbalance and train robust and generalizable models. The results obtained from different ML/DL algorithms showed that one-dimensional convolutional neural network models resulted in the best performance with state-of-the-art precision, recall, and F1 scores of 87.34%, 73.40%, and 79.77%, respectively, on unseen test drilling data.

Primary emulsifiers are an integral part of an invert emulsion (water in oil) oil based mud system. These are a class of long chain fatty acids and their derivatives. Primary function of an emulsifier in invert emulsion invert emulsion oil based mud system is to minimize the interfacial tension (IFT) between water and oil to have a stable emulsion which is an important quality of oil based mud. Water droplets are surrounded by these long chain fatty acids like an encapsulation. Fatty acids have hydrophilic head group and hydrophobic head group. The hydrophilic end groups are in contact with the surface of water droplets while the hydrophobic tail groups extend into oil phase thereby forming an osmatic cell. Only water can pass through the osmatic cell wall but not salts. Tall oil fatty acid (TOFA) are the most widely used commercially available emulsifiers which shows excellent emulsion stability even at harsh conditions such as high temperature and high pressure. TOFA is a by product of paper industry. Vegetable oil is also a good source of fatty acids present in the form of triglycerides of saturated and unsaturated fatty acids of varying carbon chain lengths. Our focus on this work is to utilize the abundant availability of used cooking/vegetable oil available for the application of emulsifier by chemical modification. A comparative study has been carried out by formulating invert-emulsion OBM using commercially available emulsifier and the emulsifier derived from used cooking/vegetable oil. The performance of emulsifier developed from waste vegetable oil has been discussed in detail.