Drilling with mud motors (positive displacement motors) dominates oilfield drilling operations due to its operational and economic advantages over conventional rotary drilling. In US land operations, whether it is a bent-motor or rotary steerable BHA configuration, a mud motor is required by almost every run to provide additional power to boost the rate of penetration (ROP). However, there is limited information to show the efficiency and status of the mud motor while it drills downhole. Based on many downhole sensor measurements, it’s not uncommon to see the average bit rotational speed decrease as the run progresses. This indicates the loss of motor efficiency due to the degradation of the mud motor. Without any mitigation action, worse drilling dynamics and surface and downhole equipment failures are likely to follow. This paper presents a new analysis method to estimate the energy and efficiency of the mud motor based on both downhole and surface data. By leveraging big data and modeling capability, it aims to provide a way of monitoring mud motor efficiency and to improve drilling energy management in planning and operations for drilling systems with a mud motor.

The analysis method is based on analytical equations and utilizes synchronized downhole and surface data to calculate mud motor efficiency. Differential pressure, flow rate, and top drive rotational speed are from surface data, while downhole rotational speed is from downhole measurement. By incorporating the mud motor model, the torque, rotational speed, and power input and output of the mud motor are calculated, thus the efficiency can be computed as the ratio of power input to power output. Moreover, the process is scaled up to a large database where data from thousands of field runs are collected, generating a more insightful analysis of the mud motor efficiency in the field. It takes advantage of the big data collected from thousands of wells and cloud computing platforms for answer product development.

An analysis workflow, data processing procedure, and dashboard have been developed to evaluate and visualize the decay of mud motor efficiency on a large scale. By incorporating the field run metadata, the analysis results show statistically how efficiently the mud motor energy is used in drilling, what the optimized operation parameters should be to achieve high efficiency, and which mud motor and bit combination is more efficient in a certain field. A lot more of these questions could now be answered with the establishment of the analysis workflow and its implementation on the large data sets. In well planning, the analysis gives a methodology to select a better bit and mud motor combination for longer and faster runs in different field applications. Meanwhile, it can be applied to generate parameter road maps to achieve better energy management while drilling through various formations. Furthermore, the analysis creates real-time data analytics answer product for mud motor efficiency monitoring that could effectively reduce failure rate and nonproductive time

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