Prediction of Penetration Rate Ahead of the Bit through Real-Time Updated Machine Learning Models

Rate of penetration (ROP) in petroleum engineering refers to the speed of forward motion of the drilling tools during the drilling process. This is an important parameter that has long been optimized for maximization, keeping in mind human, safety, and environmental factors along with consideration to downhole tools. Its importance is validated by estimating the drilling. Longer estimates of drilling time translate to increased costs. Drilling costs are affected mainly due to the following contributing factors: non-productive time, idle time, and invisible time. Attempts have been made to reduce these times to reduce costs. Simultaneously the time taken for drilling can also be reduced by effectively increasing the ROP. Drilling depths, on average are between 5,000 to 10,000 feet, coupled with a formation that has complex properties are major factors contributing to non-productive time covering a high proportion of drilling time. Thus, a large non-productive time leads to longer drilling cycles, and eventually, a low ROP. In an attempt to reduce the non-productive time, there is a need to optimize the ROP. Higher ROP facilitates a decrease in time and thus costs. In this paper, ROP is effectively predicted using artificial neural networks not at the surface, but at the bit. The artificial neural network has several advantages that overcome the limitations of the conventional models. By effectively predicting ROP, estimation of the whole drilling process time and cost, identification of specific reasons that slow down the drilling process are possible, and proper measures to avoid these issues can be implemented. The target of any ROP optimization strategy should be to have the highest ROP mechanically possible, considering human health, safety, and environment, and factoring in conditions of the well and drilling state.