A computer-implemented method comprising: using a non-linear Kalman filter to describe the dynamics of a system comprising well construction equipment and the geological environment where interaction with the equipment occurs; using a deep neural network to represent functions in the Kalman filter that represent the system via an internal state vector; training the deep neural network with data acquired from well construction operations to generate a trained deep neural network generating a base internal state vector, that corresponds to a pre-defined well construction operational procedure, using the trained deep neural network, the pre-defined operational procedure being defined as a sequence of desired internal states and desired controls for the operational procedure and wherein the predefined operational procedure is divided into segments by activity labels; receiving operation data from the well construction equipment responsive to performing the actual well construction operational procedure in the geological environment; generating an internal state vector for the actual well construction operational procedure using the operation data and the trained deep neural network and classifying internal states of the actual well construction operational procedure with activity labels from the pre-defined operational procedure; comparing at least the generated internal state vector to at least the base internal state vector, wherein the comparison comprises an operation-level comparison to evaluate the predefined operational procedure versus the actual operational procedure and an activity-level comparison to evaluate an activity on the predefined operational procedure versus the corresponding activity in the actual well construction operational procedure; outputting one or more signals to control one or more pieces of the well construction equipment based on the comparison. using a non-linear Kalman filter to describe the dynamics of a system comprising well construction equipment and the geological environment where interaction with the equipment occurs; using a deep neural network to represent functions in the Kalman filter that represent the system via an internal state vector; training the deep neural network with data acquired from well construction operations to generate a trained deep neural network using a deep neural network to represent functions in the Kalman filter that represent the system via an internal state vector; training the deep neural network with data acquired from well construction operations to generate a trained deep neural network generating a base internal state vector, that corresponds to a pre-defined well construction operational procedure, using the trained deep neural network, the pre-defined operational procedure being defined as a sequence of desired internal states and desired controls for the operational procedure and wherein the predefined operational procedure is divided into segments by activity labels; receiving operation data from the well construction equipment responsive to performing the actual well construction operational procedure in the geological environment; generating an internal state vector for the actual well construction operational procedure using the operation data and the trained deep neural network and classifying internal states of the actual well construction operational procedure with activity labels from the pre-defined operational procedure; comparing at least the generated internal state vector to at least the base internal state vector, wherein the comparison comprises an operation-level comparison to evaluate the predefined operational procedure versus the actual operational procedure and an activity-level comparison to evaluate an activity on the predefined operational procedure versus the corresponding activity in the actual well construction operational procedure; outputting one or more signals to control one or more pieces of the well construction equipment based on the comparison.