A supervised machine learning model for rate of penetration (ROP) prediction was developed that is efficient for use with real-time data. Once ROP can be predicted with a sufficiently high and consistent degree of accuracy, drilling parameters such as differential pressure, flow-rate, and rotary speed can be swept to determine an optimum ROP several times during the drilling of a stand of pipe.

Eight different types of machine learning models were trained using a population of 50 horizontal wells in the Permian Basin. Fifteen drilling parameters, or input features in terms of machine learning were proposed for this development. They included surface torque, flow rate, hydraulic parameters and the three mentioned above. A new technique using lagged features allows data from the well not directly linked to the current operation to act as a proxy for formation properties. This lagging is believed to account for formation changes and bit wear. A fully automated pipeline was developed that fetches WITSML data, qualifies it and then stores it in a structured format in the AWS cloud. The predictive/optimization model is also cloud based and can be used anywhere the user has internet access.

The various machine learning models were compared using leave-one-out-cross-validation by training on 45 of the wells and blind testing on the remaining five. Mean Absolute Percentage Error (MAPE) was used to compare the models because it allows a percentage estimate of ROP prediction accuracy. The winning model was multivariate adaptive spline regression because it is fast enough to keep up with real time drilling, provides sufficiently accurate predictions, and offers clear interpretability. Average MAPE was 13% and was consistent across wells with widely varying ROP's. This bolstered our confidence in the machine learning model to predict ROP in real time and optimize the key-controllable parameters.


ROP optimization has long been a quest in reducing the cost of drilling wells. This has become more acute in the long lateral sections of unconventional reservoir wells. The drilling industry is moving towards the automated optimization of key controllable parameters such as differential pressure, weight-on-bit, rotary speed, and flow rate. However, simply drilling faster is only part of the solution. There is the often discussed “sweet spot” where ROP is maximized and lateral, axial, and torsional vibrations are minimized. Directional control must be maintained to minimize correcting slides or tool command sets. Drilling efficiency parameters such as Mechanical Specific Energy (MSE), and depth of cut must be kept in check to protect PDC bit life. Other undesirables such as lost circulation, wellbore instability, and inefficient hole cleaning should be accounted for to avoid stuck and lost-in-hole situations.

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