Abstract
Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.
Highlights
In recent years, the energy industry has showed significant interest in the digitization of well construction processes for improved safety and cost reduction
The results from this study showed that artificial neural network (ANN) performed better when predicting the density and did not perform as well when predicting the viscosity or the gel strength, which is attributed to their nonlinear behavior
Sixty out of the 77 experimental data points were used in training, and the results showed that evolutionary fuzzy system (EFS) outperformed the ANN, adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression methods in the remaining 17 data points that the algorithms were tested on
Summary
The energy industry has showed significant interest in the digitization of well construction processes for improved safety and cost reduction. A mechanistic model can be inaccurate because it is unlikely to perfectly model the complex physical interactions associated with cuttings transport These include the effects of pipe rotation, eccentricity, inclination, chemical interactions, fluid and cuttings properties, etc. The model is applied to the experimental datasets collected at The University of Tulsa—Drilling Research Projects (TUDRP) These datasets were collected through the experimental research projects conducted in the last 40 years, which include a wide spectrum of wellbore and pipe sizes, inclinations, rate-of-penetration (ROP) values, pipe rotation speeds, flow rates, and fluid and cuttings properties. The results show that, especially for this particular research area, the data-driven model performs significantly better. Using this model, a genetic algorithm is applied to determine the optimal flow rate and pipe rotation speed. In the Appendix, basic working structures of neural networks and genetic algorithms are introduced along with the particular network parameters used in the study
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