Fully coupled end-to-end drilling optimization model using machine learning

Abstract

Drilling optimization has been studied extensively given its impact on an oil and gas project – especially in a low-price environment. This is generally approached by optimizing the rate of penetration (ROP) of the well, which may not always be the best strategy. Additionally, optimization strategies rarely include the effect of torsional drilling vibrations (stick-slip) – the biggest ROP inhibitor. As such, most optimization studies have attempted to optimize only one drilling metric – ROP, mechanical specific energy (MSE) or vibrations – independent of the other, despite their interaction downhole. This does not represent bottom hole conditions accurately. This paper introduces a method of coupling several downhole parameters using machine learning algorithms. Individual models for ROP, torque on bit (TOB), MSE, and stick-slip are built using a data-driven modeling approach using the random forests algorithm. The random forest algorithm is used given its lower error rate in modeling ROP (11%), TOB (13%), and classifying drilling vibrations (F-1 score of 0.94) as compared to other machine learning algorithms evaluated in this paper (linear regression, k nearest neighbors, bagging, and neural networks). The models are coupled by building them conjointly during training. The model uses weight-on-bit (WOB), rotary speed, flowrate, and rock strength as inputs to provide an optimal set of these input parameters to use ahead of the bit for optimal drilling. The coupled model was evaluated to optimize ROP and MSE on validation data. ROP increased by 31% and MSE decreased by 49% on average with the use of this optimization model. A case study from the Williston Basin is discussed to illustrate the practical application of this model.