Abstract: Disclosed herein are methods and systems to predict and alter the characteristics of cuttings bed in a wellbore in real time or ahead of time during planning to avoid the consequences of poor hole cleaning, i.e., high torque and drag, stuck drills, broken drill tools, lost circulation, low rates of penetration, and any related delays in the drilling schedule. The methods may include collecting drilling cuttings from drilling a wellbore penetrating a subterranean formation, analyzing the drilling cuttings, predicting characteristics of cuttings bed based at least in part on the drilling cuttings, predicting the effects of the characteristics of the cuttings bed on hole cleaning, changing at least one parameter controlling drilling operation when the hole cleaning is impaired by the effects of the characteristics of the cuttings bed, and predicting an impact of the change of the at least one parameter on the characteristics of the cuttings bed.

Abstract: Determining the likelihood of an event occurring during a drilling operation can utilize various input parameters collected from one or more sensors located downhole or at a surface location of the borehole undergoing the drilling operation. The collected input parameters can be weighted according to user parameters and then calculated over time to determine a trend in the input parameters. The trend can be analyzed against one or more probability models, such as using a machine learning process, to determine a potential for the event to occur and an estimated time interval over which the event may occur. The results of this analysis can be used by users or drilling systems to implement corrective actions to reduce or avoid the potential for the event occurring.

Abstract: A drillstring, in a drilling environment, operatively connected to an information handling system, where the information handling system is configured to perform a method for modifying drilling operations that includes obtaining measured drillstring data associated with the drillstring, and calculating a heat generation using the measured drillstring data, where, based on the heat generation, a drilling modification procedure is performed.

Abstract: Determining the likelihood of an event occurring during a drilling operation can utilize various input parameters collected from one or more sensors located downhole or at a surface location of the borehole undergoing the drilling operation. The collected input parameters can be weighted according to user parameters and then calculated over time to determine a trend in the input parameters. The trend can be analyzed against one or more probability models, such as using a machine learning process, to determine a potential for the event to occur and an estimated time interval over which the event may occur. The results of this analysis can be used by users or drilling systems to implement corrective actions to reduce or avoid the potential for the event occurring.

Abstract: A method for creating generated formatted data, that includes receiving, by a sequential data generator, raw data, where the raw data includes formation data at a drilling environment, processing the raw data to obtain generated recommendation data, where the generated recommendation data includes a proposed drilling location, and creating the generated formatted data, where the generated formatted data includes the generated recommendation data.