AIMR (Azimuth and Inclination Modeling in Realtime): A Method for Prediction of Dog-Leg Severity based on Mechanical Specific Energy

Abstract

Since the 1980?s horizontal drilling has been a game-changing technology as it allowed the oil and gas industry to produce from reservoirs previously considered marginal or uneconomic. However, while it is considered a mature technology, directional drilling is still done in a reactive fashion. Although many directional drillers are quite adept at predicting the directional response of the bottomhole assembly (BHA) in a given well, the ability to manage all of the drilling parameters on a foot by foot basis while accurately predicting the effects of each parameter is impossible for the human brain alone. Given current rig rates, any amount of increased slide time and its reduced ROP which occurred due to poorly predicted directional response can result in a significant economic impact.

There exist many measured parameters or system inputs which have been proven to affect the directional response of a drilling system. One parameter whose effect has not been investigated is mechanical specific energy or MSE. MSE is measure of how efficient the drilling process is in relation to rate of penetration. To date, MSE has primarily been used with for vibration analysis and rate of penetration optimization.

The following dissertation covers research into the effect of MSE on the overall wellbore direction change or dog-leg severity. Using published experimental data, a correlation was developed which shows a clear relationship between the dog-leg severity, rate of penetration (ROP) and MSE. The correlation requires only a few hundred feet of drilling before it is able to be tuned to match an individual well?s results. With minimal tuning throughout the drilling of a well, very good results can be obtained with regards to forecasting dog-leg severity as the wellbores were drilled ahead. The correlation was tested using data from multiple, geo-steered wells drilled in a shale reservoir. The analysis of the correlation using real-world data proved it to be a robust and accurate method of predicting the magnitude of dog-leg severity. The use of this correlation results in a smoother wellbore, drilled with a faster overall ROP with a better chance of staying within the geologic targets.