Abstract

Drilling is probably the most critical, complex, and costly operation in the oil and gas industry and unfortunately, errors made during the activities related are very expensive. Therefore, inefficient drilling activities such as connection duration outside of optimal times can have a considerable financial impact, so there is always a need to improve drilling efficiency.

It is for this fact, that the measure of different behaviors and the duration of the drilling activities represent a significant opportunity in order to maximize the cost saving per well or campaign. Reducing the cost impact and maximizing the drilling efficiency are defined by the way used to calculate the perfect well time by the technical limit, non-productive time (NPT), and invisible lost time (ILT), in an operating company drilling plan.

Different approaches to measure the invisible lost time that could be present in the in slips activity on the drilling operation are compared. Results show the differences between multiple techniques applied in real environments coming from a cloud platform.

The methodologies implemented are based on the following scenarios, the first one use a combination of a custom technical limit based on technical experience, the historical data limit using standard measures (mean, average, quartiles, standard deviation, etc.), and a depth range variable (phases) differentiation, initial, intermediate, and final hole sizes is used.

A complexity comparison uses the rig stand and phase footage variables for base line (count and duration) definition per phase, the non-productive time activities exclusion and data replace techniques mixing with an out of standard time detection in slips behavior (motor assemblies, bit replacing, bottom hole assembly (BHA), etc.) using standard and machine learning mechanisms. A final methodology implements an in slip ILT by technical limit definition using machine learning.

The results using the same data set (set of wells) and coming from the different methods has been evaluated according to the total invisible lost time calculated per phase, percentage of activities evaluated with invisible lost time per phase and the variation of ILT considering the activities defining the technical limit. Finally, the potential implementation by any operator can be evaluated for these methodologies according to their specific requirements.

This analysis creates a guideline to operating companies about multiple techniques to calculate ILT, some using innovative procedures applied on machine learning models.

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