Summary: Rate of penetration (ROP) is one of the important indicators for evaluating drilling efficiency, which provides the basis for drilling parameter optimization. ROP prediction methods can be divided into two main categories—physical models and machine learning models. The physical models are derived from classical drilling theory and experimental results, with clear physical meanings, good interpretability, and generalization. However, physical models do not result in very accurate predictions due to assumptions and experiences introduced in the modeling process. Machine learning models can effectively learn the intrinsic relationships between data through the training of a lot of data. However, machine learning models are like black boxes and their performances are highly dependent on the quality of drilling data in oil fields, so their interpretability and generalization ability are relatively low. This study mainly focuses on establishing a more accurate model of ROP prediction with clear interpretability. To achieve this goal, two novel categories of hybrid modeling approaches were introduced for horizontal drilling in the China Jimusar oil field, one of which is the error compensation by machine learning and another is the weighted average outputs. In the first category of hybrid model, physical models are taken as the main submodel, and machine learning models are used to predict and counteract the errors caused by physical models. By this method, the physical model can effectively ensure its physical meaning and generalization, and the machine learning model, as a submodel, can effectively compensate for the low-accuracy defects of physical models to improve prediction accuracy. In the second category, combining physical models with machine learning models utilizing ensemble learning, the deficiencies of models are cancelled out by the other models in the ensemble—like a team effector. The paper presents hybrid models with four suggested steps, which include data collection and preprocessing, optimal selection of physical model, optimal selection of machine learning model, and establishment of hybrid model. The performances of physical models, machine learning models, and hybrid models are intercompared. From the view of prediction accuracy, model interpretability, modeling difficulty, and generalization, the hybrid model with error compensation by machine learning is the optimal method for ROP prediction. This study also demonstrates an optimal trade-off between high accuracy and good interpretability.

Summary: High drag and torque on drillstring and difficulty in hole cleaning are two major challenges in extended-reach drilling, and these two challenges are usually coupled with each other. However, previous studies considered the drillstring mechanics and hole cleaning as two relatively independent issues and studied them separately, which cannot fully reveal the complex mechanisms of pipe sticking and obtain proper drilling parameters to ensure hole cleaning and reduce drag and torque. To solve this problem, in this paper we present the concept of pipe-cuttings interaction and elaborate on the two interaction mechanisms, which are the effect of pipe rotation on cuttings transport and the effect of cuttings distribution on pipe load. Second, we introduce the stirring diffusion factor and pipe-cuttings contact stress to quantify the pipe-cuttings interaction, and we obtain their mathematical expressions through nonlinear regression of numerical simulation results. Third, we establish the mechanical pipe-cuttings interaction model by modifying and combining the tubular mechanical model and the cuttings transport model. Finally, we validate the interaction model and apply it to a case study of extended-reach drilling in the South China Sea. The results show that the interaction model is of high prediction accuracy and is superior to the conventional tubular mechanical and cuttings transport models because the interaction between pipe rotation and cuttings transport is sufficiently considered. The problem of poor hole cleaning for a 12¼-in. section is serious, and pipe sticking occurs frequently for the extended-reach well in the case study because the stationary bed height is more than 10% in actual drilling. It is usually difficult to ensure hole cleaning of a 12¼-in. section by only optimizing a single parameter of rate of penetration (ROP), flow rate, or rotational speed, but optimizing two of the parameters at the same time can achieve better results. Lastly, we establish a recommended chart of drilling parameters, which can aid engineers in making comprehensive recommendations on drilling parameters.

Mechanical specific energy (MSE) has been widely used to quantify drilling efficiency and maximize rate of penetration (ROP) in oil and gas wells drilling. In this chapter, MSE models respectively for directional or horizontal drilling and rotating drilling with positive displacement motor (PDM) are established based on the evaluation of virtues and defects of available MSE models. Meanwhile methods for drilling performance prediction and optimization based on MSE technologies are presented. Field data presented in this chapter indicates that the developed MSE models estimate MSE values with a reasonable approximation in the absence of reliable torque measurements, the method for optimizing drilling parameters can estimate optimum WOB values with different RPM to drill a specific formation interval with PDM. It also show that the optimum WOB is low for rotating drilling with PDM compared with the conventional drilling without PDM, increasing WOB does not always increase ROP but is more likely to decrease ROP. The drilling performance prediction and optimization methods based on MSE technologies could be effectively used to maximize ROP and allow operators to drill longer and avoid unnecessary trips, and is worthy to be applied and promoted with highly diagnostic accuracy, effective optimizing and simple operation.