ROP Modeling Using Neural Network and Drill String Vibration Data

Abstract

Vibrations are caused by bit and drill string interaction with formations under certain drilling conditions. They are affected by different parameters such as weight on bit, rotary speed, mud properties, BHA and bit design as well as by the mechanical properties of the formations. During the actual drilling process the bit interacts with different formation layers whereby each of those layers usually have different mechanical properties. Vibrations are also indirectly affected by the formations since weight on bit and rotary speed are usually optimized against changing formations (drilling optimization process). Therefore it can be concluded that for optimized drilling reduction of vibrations is one of the challenges.A fully automated laboratory scale drilling rig, the CDC miniRig, has been used to conduct experimental tests. A three component vibration sensor sub attached to drill string records drill string vibrations and an additional sensor system records the drilling parameters. Uniform concrete cubes with different mechanical properties were built. Those cubes as well as a homogeneous sandstone cube were drilled with different ranges of weight on bit and bit rotary speed. The mechanical properties of all cubes were measured prior to the experiments. During all experiments, drilling parameters and the vibration data were recorded. Based on analyses of the data in the time and the frequency domain, linear and non-linear models were built. For this purpose the interrelations of sandstone and concrete mechanical properties, drilling parameters and vibration data were modeled by neural networks. Application of sophisticated attribute selection methods showed that vibration data in both, time-and frequency domain, have a major impact in modeling the rate of penetration.

Rotary steerable systems: mathematical modeling and their case study

“…Similarly, the ROP normal model ( f h (⋅) ) represents the rate of penetration with 90 degrees to the axial direction, or where F bit is the total bit force calculated by (1) applied by the RSS and the formation, and is the model parameter representing the factors like well path geometry, rock properties and so on. In the literature, there are several ROP models to calculate ROP v Eren (2011); Bataee et al (2010), for instance like Bingham model Bingham (1964), Teale model (Teale 2015) and Bourgoyne model (Bourgoyne et al 1986). In this study, Teale's model is used, given by ( 9) where is the friction coefficient, D is the bit diameter, A b is the wellbore area and E s is the specific energy of the rock.…”

“…The biasing mechanism is the "actuator" for the RSS Li et al (2020). This steering is made with the use of an actuator which eccentrically displaces the center line of the drilling system away from the center line of the hole by a controllable offset Elshafei et al (2015). Typically, the RSS actuator is a 3-pad tool which pushes one pad against the formation to direct the bit in the opposite direction.…”

Drilling Vibration Modes and Penetration Rate Modeling using Artificial Neural Network and Multiple Linear Regression Analysis in Khoman Formation at the Egyptian Western Desert

“…Recently, and with the help of progressing neural network modeling method, numerous models were built for ROP prediction using drilling data. Abdolali Esmaeili et al [7] develops an ROP model using neural network and drill string vibration data.…”

“…These results indicate that each input plays a great role according to each input weight, therefore optimizing the drilling process through controlling vibration level severity with little harmful impact on the rate of penetration. Tables (7,8) demonstrate a more comprehensive set of error calculations for the modeled regression-neural ROP, and vibration level results in axial, lateral, and stick-slip drilling modes. As can be seen that all error statistical values are small which confirms the reliability of both neural and regression model results.…”

An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation – Ahdeb Oil Field

“…Multi-Layer perceptron (MLP) is one of the best ANN structures which is widely used because of its ability of modeling a complex relationship between variables, which gives results with a high accuracy [18]. Multi-layer perceptron has an input layer, one or more hidden layers and an output layer [19]. The input layer only distributes the input elements.…”