Under this climate of oil price and energy uncertainty, it is mandatory to limit the non-productive time (NPT) and achieve the highest levels of operational excellence. This is a key factor toward overcoming the evolving economic challenges, reducing budget and spending, and optimizing the return on investment. Worldwide, stuck pipe and borehole problems represent one major contributor into the NPT while drilling, reaming, tripping, casing and running completions. This NPT category becomes even more critical when dealing with shaly formations. Saudi Aramco constantly deal with offshore shaly formations in Saudi Arabia where stuck pipe and borehole problems contribute with over 24% to the overall drilling and workover NPT. Establishing best practices to minimize or prevent these problems will enhance the overall drilling performance and result in significant operating time reductions and cost savings. The major offshore re-entry operation challenges are first screened: formation instability, hole size, well trajectory, bottomhole assembly and experience and communication. The shaly formation rock nature is analyzed to understand the stressed shale instability root cause: mechanical and/or chemical. This diagnostic step establishes the formation shale properties and behavior. In relation to this information, three basic stuck pipe mechanisms (pack-off and bridging, differential sticking, and wellbore geometry) are discussed where the major contributing factors for each category are identified. This leads to the establishment of proactive recommendations and best practices. These practices will tackle the problem from different angles to construct an integrated solution. This includes drilling fluid design (rheology, filter cake, filtrate volume and properties, etc.), hole cleaning (rate of penetration, sweep pills, bottoms-up volume, etc.) and drilling parameters (trend analysis, flow rates, string motion, etc.). This paper will highlight the recommended practices and provide actual well examples where stuck pipe tendency was reduced in shaly formations.

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.

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This paper details using advancement in data analytics and the huge amount of data generated while drilling to develop an automated system to detect kicks while drilling. Detecting kicks in early stages gives the crew additional time to control it resulting in a safer and more efficient drilling operation. Five models were developed and evaluated to optimize kick detection they are: Decision Tree, K-Nearest Neighbor (KNN), Sequential Minimal Optimization (SMO) Algorithm, Artificial Neural Network (ANN), and Bayesian Network. The models were trained to detect kicks based on actual kick cases. The models are predicting kicks using only surface parameters such as: pressure gauges, flow meters, hook load, rate of penetration, torque, pump rate, and weight on bit. The performance of the five models is then evaluated and compared. Best two models were Decision Tree and K-Nearest Neighbor.

Polymer resin systems have many advantages over conventional cement. Since resins are solids free, they can be used in low injectivity zones such as narrow fractures where conventional cement cannot be pumped. In order to ensure successful field applications of resin systems, downhole temperature must be checked. The objective of this paper is to introduce a high temprarure resin system compared to conventional ones, evaluate the stability of epoxy resin formulation at high temperatures and identify the optimum temperature for successful application. Despite the many advantages of polymer resins, they have few limitations that can affect their performance and the success. One important limitation of resins is temperature sensitivity. Temperature affects the speed of the reaction of the resin with the curing agent. Therefore, it is important to design the polymer resin according to downhole conditions. As curing temperature approach glass transition temperature, the mechanical properties of the cured resin are compromised. Resin formulation #1 investigated in this study was used in the field for different applications. However, all the application were performed in tempratures lower than 225 °F due to temperature limitation. The objective of this paper is to investigate new resin formulation and its suitability to be applied in deeper section where temperature is way above 225 °F reaching in some application to more than 290 °F. The resin formulation was cured through polymerization process using an amine curing agent to improve properties even further. The final cured polymer was then analyzed through DSC, TGA, and SEM experiments.