Abstract
The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.
Highlights
The drilling rate of penetration (ROP) is a measure of the speed or the progress of the drill bit when it drills subsurface formation
A certain load has to be applied on the bit, and this is known as the weight on bit (WOB)
The ROP model was built using an artificial neural network (ANN) feed-forward network with the six input parameters discussed in the previous section
Summary
The drilling rate of penetration (ROP) is a measure of the speed or the progress of the drill bit when it drills subsurface formation. One of the most common models of predicting ROP was developed by Bourgoyne and Young [11] with nine inputs (depth, equivalent mud density, equivalent circulation density, WOB, drill bit size, drillstring rotation, Q, mud density, plastic viscosity) to account for many parameters, such as bit hydraulics and overbalance pressure. AL-AbdulJabbar et al [12] developed a new ROP model with full consideration of different drilling parameters and mud rheological properties They used nine inputs, and two exponents were calculated to reflect the bit exponent and the formation compressive strength. The objective of this study is to use ANN to develop a new real-time ROP model using field data including drilling parameters and changing formation strength from three onshore wells.
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