Abstract: A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.

Abstract: Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.

Abstract: Multiple projected rate of penetration (ROP) values can be determined for purposes of adjusting well tools and well characteristics. For example, surface data can be determined based on a surface sensor signal. Downhole data can be determined based on a downhole sensor signal. A first value indicating a first projected ROP of a drill bit can be determined by providing the surface data as input to a first machine-learning model. A second value indicating a second projected ROP of the drill bit can be determined by providing the downhole data as input to a second machine-learning model. A third value indicating a third projected ROP of the drill bit can be determined by providing the first value and the second value input to a third machine-learning model. An operating characteristic of a well tool can be adjusted based on the third value.

Abstract: A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.

Abstract: Multiple projected rate of penetration (ROP) values can be determined for purposes of adjusting well tools and well characteristics. For example, surface data can be determined based on a surface sensor signal. Downhole data can be determined based on a downhole sensor signal. A first value indicating a first projected ROP of a drill bit can be determined by providing the surface data as input to a first machine-learning model. A second value indicating a second projected ROP of the drill bit can be determined by providing the downhole data as input to a second machine-learning model. A third value indicating a third projected ROP of the drill bit can be determined by providing the first value and the second value input to a third machine-learning model. An operating characteristic of a well tool can be adjusted based on the third value.